In an era of increasingly intricate financial markets, the advent of Generative AI (GenAI) in asset management represents a paradigm shift. Imagine a scenario where your investment strategies are dynamically honed by an AI that interprets the cacophony of market data into clear, strategic insights. This is not a vision of the distant future but a present-day reality, where AI is the anchor in decision-making, offering a competitive edge in asset management.

How GenAI is transforming asset management

Artificial intelligence (AI) revolutionizes asset management by improving investment decisions, portfolio management, and market trend forecasting. Key AI technologies driving this change include machine learning (ML), which analyzes data patterns for predictions (e.g., stock price movement prediction); natural language processing (NLP), which performs sentiment analysis of news and social media to gauge market sentiment; and predictive analytics, which uses statistical methods and ML to forecast market trends.

Building on these foundational AI techniques, GenAI processes unstructured data, including financial news and social media sentiment, to refine trading strategies and provide deeper market insights.

Here’s how GenAI specifically enhances asset management:

• Portfolio Optimization & Asset Allocation: Utilizes unstructured data to drive insights, deliver market intelligence, and support algorithmic trading.
• Proactive Risk Management: Continuously monitors market conditions, news, and sentiment for early warning signals, allowing for a more dynamic response to market changes.
• Streamlined Client Onboarding: This enhancement enhances onboarding with dynamic e-KYC and bot-assisted self-service options, making the process more efficient and user-friendly.
• Personalized Client Insights: Integrates GenAI with market attribution models and open-source data to provide tailored insights, ensuring investment strategies align closely with each client’s unique needs and risk profiles.

Why asset managers are turning to GenAI

Several key factors are propelling the adoption of GenAI in asset management:

• Data Abundance & Market Complexity: The financial sector generates vast amounts of data daily, and GenAI navigates this complexity by simulating scenarios and identifying investment opportunities.
• Real-time Decision-Making: Enables rapid response to market developments through real-time data processing and analysis.
• Alternative Data Sources: Analyzes diverse data sets, such as social media, satellite imagery, and geolocation, for unique investment insights.
• Risk Management: Enhances risk identification, assessment, and mitigation through scenario modeling and early warning systems.
• Alpha Generation: Identifies non-obvious alpha opportunities by uncovering hidden data patterns.
• Portfolio Optimization: Optimizes portfolios considering risk tolerance, investment objectives, and market conditions.
• Cost Efficiency: Automates routine tasks, reducing operational costs.
• Personalization: Provides personalized investment recommendations based on investor profiles.
• Competitive Advantage: Early adopters gain an edge through superior strategies and higher returns.
• Regulatory Compliance: Assists with compliance by providing auditable records.
• Scalability: Scales to accommodate growing data volumes and portfolios.

Asset management now integrates financial expertise, strategic planning, market understanding, and technology, especially GenAI, for informed, objective-aligned decisions. This integration is driven by market complexities and the need for data-driven decision-making, positioning asset management as a dynamic field.

Advantages of GenAI for asset management excellence

GenAI offers transformative potential for asset management, extending beyond process optimization to revolutionize investment strategies and enhance firm performance. Key advantages and capabilities include:

• Alpha Generation: Leverages advanced analytics to identify unique, risk-adjusted investment opportunities and generate excess returns.
• Personalized Financial Advice: Delivers tailored insights and recommendations aligned with individual client goals and risk profiles.
• Seamless Client Onboarding: Streamlines the onboarding process for a frictionless client experience.
• Optimized Marketing and Investment Operations: Enhances marketing strategies and investment operations through data-driven insights and automation to improve efficiency and ROI.
• Enhanced Decision-Making: Generates alternative scenarios, allowing asset managers to explore possibilities and outcomes for more informed investment choices. This data-driven approach reduces reliance on intuition.
• Improved Risk Management: Creates diverse market scenarios, enabling asset managers to assess portfolio vulnerabilities and develop robust risk mitigation strategies.
• Increased Efficiency: Streamlines operations by automating data processing, analysis, and reporting, freeing asset managers for higher-level tasks. (Efficiency gains are estimated at 50-80%.)
• Enhanced Portfolio Optimization: Analyzes vast datasets to identify optimal asset allocations, maximizing risk-adjusted returns through AI-powered algorithms.
• Addressing Cognitive Biases: GenAI’s objective data analysis counterbalances emotional biases, promoting more rational investment choices.
• Innovative Investment Strategies: Facilitates innovative strategies by uncovering hidden patterns and correlations through data analysis and scenario simulation.
• Adaptation to Market Dynamics: Enables rapid adaptation to changing market conditions through real-time insights and strategy adjustments.
• Strengthened Compliance and Reporting: Automates data validation and ensures regulatory adherence, enhancing transparency and reducing compliance risks.

Practical applications of GenAI in asset management

• Data and Predictive Analytics: GenAI advances this by predicting and generating new scenarios or data points. It can analyze unstructured data like news articles and social media sentiment or simulate market responses to hypothetical events. It offers real-time, nuanced insights into market dynamics, shock events, and asset valuation changes, allowing for more proactive and strategic decision-making.
• Portfolio Enhancement and Generation: GenAI goes beyond static analysis by dynamically adjusting portfolio allocations in response to real-time data, including unconventional data sources. It can simulate various market conditions to suggest innovative portfolio adjustments or new investment product designs tailored to specific market conditions or investor profiles.
• Investor Communication and Reporting: GenAI can revolutionize investor communication by generating personalized content. It can create detailed, context-aware reports or interactive dashboards that adapt to the individual investor’s interests, questions, or preferences, enhancing engagement and understanding.
• Operational Automation: GenAI significantly transforms operations by automating routine tasks and creating new processes or solutions. It can learn from operational data to suggest workflow optimizations, automate complex data analysis, or even generate documentation and compliance reports with much greater detail and accuracy, thereby improving efficiency, reducing errors, and enabling staff to focus on strategic roles.

Navigating the challenges of GenAI implementation

While GenAI offers transformative potential in asset management, it also introduces specific challenges:

• Ethical and Regulatory Challenges: GenAI’s ability to generate new data or content amplifies concerns about transparency, especially since the decision-making process can be less straightforward than traditional AI. This raises issues about decisions, the potential for bias in generated outputs, and who is accountable for AI-generated actions or recommendations. Ensuring compliance with emerging regulations like the EU AI Act is particularly challenging when dealing with AI that creates rather than just processes data.
• Trust in AI Models: The complexity of GenAI models, which can generate novel insights or scenarios, makes it harder to achieve explainability. Building trust in these systems necessitates more transparent and interpretable models where the logic behind generated data or decisions can be understood. For instance, if a GenAI model suggests a unique investment strategy, investors and regulators need to understand the basis of this recommendation.
• Data Quality and Management: GenAI’s dependence on structured and unstructured data makes data quality critical. Poor data can lead to the generation of misleading or biased insights, potentially causing significant errors in market analysis or investment strategies. Moreover, managing the vast, diverse datasets GenAI uses requires sophisticated systems, and ensuring data privacy under regulations like GDPR becomes more complex when AI can generate new data that might mimic real personal data.
• Skill Gaps and Interdisciplinary Expertise: Integrating GenAI into asset management demands technical AI expertise and an understanding of how to apply these technologies creatively in financial contexts. The unique challenge here is training or hiring professionals who interpret and utilize GenAI’s novel outputs. Bridging this skill gap involves interdisciplinary collaboration, where financial acumen meets advanced data science capabilities, perhaps through initiatives like specialized training or partnerships (e.g., similar to Goldman Sachs’ approach to data accessibility).

What’s next for GenAI in the asset management industry

The future of asset management will be significantly influenced by advancements in GenAI, particularly beyond 2026. Here’s how:

• Advanced Model Capabilities: GenAI will evolve to handle even more complex and large-scale datasets, generating insights and crafting entirely new data points or scenarios more precisely. These models will push the boundaries of predictive analytics, offering unprecedented deep insights into market behaviors and investment opportunities.
• Innovative Investment Strategies: GenAI’s capacity to simulate countless market conditions will lead to the development of novel investment strategies. These strategies could dynamically adjust in real time to market changes, leveraging the technology’s ability to generate and analyze previously unimaginable scenarios.
• Customization at Scale: With GenAI, fully automated investment platforms might emerge, capable of providing hyper-personalized investment plans based on real-time data and individual investor profiles. This personalization will go beyond what current AI offers, tailoring investments to specific client needs, risk tolerances, and preferences.
• Enhanced Human-AI Collaboration: While AI advances, human advisors will continue to play an essential role, particularly in areas requiring intuition, ethical judgment, and nuanced client interactions. The partnership between human advisors and GenAI will deepen, with AI providing data-driven insights and humans offering contextual understanding and relationship management.
• Regulatory Evolution: GenAI’s unique capabilities, especially its ability to generate new data or insights, will likely spur new regulatory frameworks. These will ensure ethical use, transparency, and accountability in finance, focusing on how generated data is used, the explainability of AI decisions, and the prevention of AI-driven market manipulations or biases.
• Global Market Influence: Asia-based hedge funds investing in AI innovation, particularly in China, highlight a trend where regions with advanced AI development will influence global asset management practices. GenAI could become a key differentiator in markets where data is abundant and diverse.

The trajectory of GenAI in asset management points towards a future where investment strategies are not only data-driven but also creatively and dynamically generated. This offers challenges and opportunities regarding regulation, ethics, and human involvement.

Conclusion

GenAI is transforming asset and wealth management; strategic use across key areas like portfolio optimization, risk management, client onboarding, compliance, and personalized insights can unlock efficiency, innovation, and value. However, a well-defined strategy, robust governance, and pragmatic implementation are crucial for mitigating risks and ensuring compliance. Firms successfully navigating this transformation will lead the industry’s digital revolution, but they must always balance technological advancements with irreplaceable human oversight in decision-making.

Generative AI (GenAI) has become a prominent technology in 2024, sparking significant interest among financial institutions worldwide. Beyond its content generation capabilities, GenAI is finding applications in various domains. This article explores the role of GenAI in the digital payments industry.

GenAI is a branch of artificial intelligence that creates new content, such as text, images, audio, or video, that resembles human-generated data. Unlike traditional AI systems, which often follow predefined rules, GenAI models leverage machine learning to generate new content based on patterns learned from extensive datasets. Key features of GenAI include the ability to produce texts, images, audio, and video; a contextual grasp of the input data or environment; and improved performance due to the processing and comprehension of large volumes of high-quality data.

Applications of GenAI in payments

GenAI has the potential to revolutionize payments by enhancing personalization, security, and efficiency, benefiting both businesses and consumers.

From marketing and sales to customer onboarding, KYC, customer service, and risk management, GenAI can offer comprehensive solutions across the entire payment lifecycle.

Here are some key applications of GenAI in payments:

1. Marketing and Sales:

● Personalization: GenAI models can analyze transaction histories and customer preferences to recommend personalized products, services, or payment options. This enhances customer experience and loyalty by providing tailored suggestions and simplifying transactions.

● Content Creation: GenAI can improve marketing and sales effectiveness by generating targeted content for outbound customer communications. Images and content can be customized for specific customer segments. For example, younger demographics can be reached with relatable and eye-catching content promoting specific offerings.

● Dynamic Product Pricing: GenAI models can analyze market dynamics, customer behavior, and inventory data to create dynamic pricing strategies for products and services. This allows banks and fintechs to optimize real-time pricing based on demand, supply, and other factors. Dynamic pricing models can be applied to products like loans, insurance premiums, and investment portfolios, adjusting pricing based on risk assessments, market conditions, and customer preferences.

2. Customer Onboarding:

● Intelligent Verification: GenAI can streamline customer onboarding by automating identity verification and ensuring regulatory compliance, enhancing efficiency and accuracy.

● Document Processing: GenAI can facilitate onboarding through intelligent document processing and real-time KYC/AML checks.

● Personalized Journeys: GenAI enables systems to adapt to consumer preferences and recommend personalized customer journeys, improving the overall experience.

Example: A major American payment card service has implemented a GenAI system that analyzes regulatory documents and provides recommendations for AML and KYC compliance across various regions.

3. Payments Processing:

● Conversational Payments: GenAI-powered chatbots and virtual assistants facilitate conversational payments, allowing users to make transactions, check balances, and receive support through natural language interactions. This enhances customer experience and attracts new customers.

● Fraud Detection and Risk Management: GenAI can develop dynamic risk-scoring models that assess real-time payment transaction risks. These models assign risk scores based on factors like transaction amount, frequency, location, and user behavior, enabling targeted risk management strategies. GenAI models learn typical payment patterns and create synthetic fraud examples to aid anomaly detection systems. They also analyze transactional data and market trends to proactively identify risks, bolster risk management, and prevent financial fraud.

Example: A Nordic-Baltic banking group has used the generative adversarial network (GAN) model to detect fraudulent transactions, reducing false positives.

4. Operations and Delivery:

● Process Automation: GenAI can automate complex middle-office tasks, such as commercial contracts, proposal requests, and account plans, reducing manual effort and streamlining delivery.

● Code Development Acceleration: GenAI can help companies with legacy systems by automating tasks like bug detection, code repair, and user acceptance testing. It can also analyze existing codebases to suggest alternative solutions or approaches.

● Product and Service Innovation: GenAI can accelerate delivery timelines by allowing teams to focus on critical activities. Its computational and documentation capabilities can also assist in developing new product and service designs.

Example: One of the largest private banks in India is rolling out its LLM-powered website in 2024. The bank also plans a private LLM to write credit assessments and business requirement documents.

5. Payments Reconciliation:

● Automated Data Parsing: GenAI is a powerful tool for automatically parsing structured and unstructured data, improving accuracy and minimizing errors. Regardless of format, it can extract relevant information from invoices, receipts, and bank statements.

● Payment Pattern Analysis: GenAI can provide valuable insights into payment patterns, helping businesses optimize reconciliation processes.

● Enhanced Exception Handling: GenAI can analyze exceptions to identify root causes and recommend automatic suggestions for alternative approaches when exceptions recur. While this use case is still evolving, it has the potential for widespread application.

6. Customer services and support:
● Smart agent assistant: GenAI can provide real-time suggestions and knowledge repository access to customer service agents, thereby improving human agents. It can also draft personalized communications messages to customers.

● Improved self-service options: GenAI can create clear and concise information and personalize FAQs based on user behavior and past interactions. It can also develop interactive tutorials and guides that help customers resolve queries independently.

● Chatbots and proactive customer reach-outs: GenAI can power chatbots and virtual assistants that assist users with payment-related inquiries, provide customer support, and facilitate transactions through NLP. GenAI and AI chatbots serve different purposes despite using the same technology. The content creation capabilities of GenAI can be used to personalize information and content for service agents. On the other hand, AI chatbots are designed to simulate conversations directly with the users through text or voice messages.

Example: A leading commercial bank in the UK has recently announced that it will use GenAI to improve its existing virtual assistant. This is expected to give customers access to a broader range of information through conversational interactions.

Handling risks associated with GenAI in payments

While GenAI offers significant benefits in fraud detection, personalized user experiences, and operational efficiency, investment in GenAI needs serious consideration as it also presents inherent risks related to data privacy, bias, transparency, and security.

Key Risks:

● Risks Associated with GenAI-Powered Recommendations: While personalized recommendations aim to enhance user experience, they can lead to privacy concerns, algorithmic biases, and transparency issues. Recommendations in sensitive areas like sanctions screening, fraud detection, or exception handling may require human intervention.

● Risks Associated with Real-Time Monitoring: While real-time monitoring benefits cybersecurity and fraud detection, it can raise privacy concerns due to processing sensitive customer information. Balancing real-time responsiveness with minimizing false positives is a significant challenge, as excessive monitoring may delay payment transactions and affect service level agreements (SLAs).

● Risks of Bias Perpetuation: GenAI relies on historical data, which can introduce biases if the training data is biased. This can lead to unfair treatment of specific user groups. GenAI technologies should be implemented cautiously to avoid perpetuating biases.

Drivers for adoption of GenAI in payments

While implementing GenAI can be capital-intensive and disruptive, its potential to enhance efficiency, security, customer centricity, and innovation drives its adoption in payments.

The payments domain is well-positioned to adopt GenAI-integrated systems as it embraces new technologies and infrastructure. The increasing demand for convenience in payments, driven by the digital age, is a significant factor. GenAI’s capabilities align with this demand, making it a logical choice for the payment industry.

Data and quality are essential drivers for the payments industry’s growth. The sector generates vast transactional, customer behavior, and financial data. The introduction of ISO20022 will increase structured data availability, facilitating GenAI integration.

Security is paramount in payments, especially with the rise of new channels. GenAI’s ability to generate synthetic data, manage risks, and detect fraud helps organizations achieve their security goals.

While GenAI can increase productivity and streamline operations, organizations must address the potential for job displacement due to automation. Transparent communication and employee training are crucial to mitigate these risks and ensure a smooth transition.

The successful adoption of GenAI in payments requires a comprehensive approach that addresses these challenges and leverages the transformative potential of AI technology.

Increasing efficiency, enhancing security, and delighting your customers with GenAI requires building in-house capabilities or collaborating with external tech partners to develop advanced fintech products and tailored digital experiences.

In today’s age of remarkable technological progress, artificial intelligence (AI) has become a transformative force in numerous industries, revolutionizing the way they operate. Among the many sectors experiencing its transformative power, the insurance industry stands out as a prime beneficiary.

With the ability to enhance efficiency and accuracy in ways previously unimaginable, AI is reshaping the landscape of insurance operations, from underwriting and claims processing to risk assessment and customer service.

The global AI in insurance market size was valued at USD 6.92 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 24.08% from 2022 to 2028. A recent survey found that 21% of insurance organizations are preparing their workforce for collaborative, interactive, and explainable AI-based systems. It is also predicted that investment in AI insurance will be ranked high on the agenda of decision-makers.

AI is transforming the insurance industry by automating tasks, analyzing data, and enhancing decision-making. This leads to improved efficiency, accuracy, and customer service, which drives customer satisfaction and boosts profitability.

How is AI Changing the Insurance Game

#1 Tackling the claims problem

a) Accelerated claims processing

In today’s digital era, insurance companies have streamlined the claims process through convenient methods like smartphones and web portals. AI-powered bots guide customers, verify policy details, detect fraud, and expedite settlement for prompt resolution.

Insurance companies are using new technologies to accelerate claims processing and improve the customer experience:

• AI-powered chatbots can be used to automate and streamline the claims processing process, which can help reduce costs and improve customer satisfaction.
• AI-driven touchless insurance claim processes can automate the entire claims process. This includes reporting the claim, updating the system, and communicating with the customer.
• Document capture technologies and optical character recognition can efficiently extract text from scanned documents.
• AI-based handwriting recognition software can now process handwritten documents at a speed and accuracy that far exceed human capabilities.
• Automated processes are now being used to handle many aspects of claims processing, from routing claims to approving them.

b) Claims reserve optimization

AI plays a pivotal role in the digital trends insurance industry in claims reserve optimization by leveraging data analytics to enhance accuracy, efficiency, and decision-making. It analyzes historical claims data, identifies patterns, and predicts risks, enabling more precise allocation of reserves while reducing errors and administrative burdens.

The benefits of AI-based insurance solutions for claim reserve optimization:

• Real-Time Claims Estimation: AI and ML technology streamlines the process of analyzing claims data, saving significant time that is typically spent on data preparation. This enables insurers to estimate claims more efficiently and accurately.
• Early Fraud Detection: AI-powered solutions can identify fraudulent activities in insurance claims, reducing the need for manual effort and intensive claim processes. By detecting fraud early on, insurers can prevent delayed claims and improve the overall efficiency of claim processing.
• Enhanced Safety in Hazardous Locations: Leveraging AI in insurance helps assess damages in hazardous locations while minimizing safety risks for claims inspectors. This technology aids in identifying potential dangers and ensures that compensation claims are accurate and fair.

c) Claim fraud detection and prevention

AI plays a crucial role in the identification and prevention of fraudulent insurance claims, thereby enabling insurers to establish a streamlined and effective system for managing claims. By swiftly analyzing vast amounts of data, insurance AI algorithms can identify patterns and detect anomalies that deviate from these patterns.

AI in insurance is revolutionizing fraudulent claim detection and prevention through the following methods:

• Big fraud schemes: AI technology allows insurance companies to cross-reference and analyze data points from internal and external databases, simplifying the detection of fraudulent activities.
• Fraud patterns: AI integration in insurance streamlines fraud detection by identifying patterns. For example, when a claim for a stolen smartphone is filed, AI can quickly search databases for prior suspicious activity, raising a red flag for further investigation.

By leveraging AI, insurance companies can improve their ability to detect and prevent fraudulent claims, safeguarding the interests of policyholders and the industry as a whole.

#2 Customer Service and Retention

a) Prediction of customer churn

The insurance industry has higher customer acquisition costs than many other sectors. Retaining existing customers proves to be a more cost-effective approach. Insurance companies are now leveraging AI-based solutions for churn prediction, enabling them to anticipate when customers are likely to churn and proactively implement measures to retain them.

Through the utilization of AI and Machine Learning algorithms, key indicators such as shifts in app usage and rewards program engagement, alterations in the frequency of customer support interactions, fluctuations in income, or changes in life circumstances can be identified. Furthermore, these algorithms can also forecast employee attrition by monitoring changes in work patterns and gauging employee satisfaction.

Consequently, this approach presents a mutually beneficial solution for insurance companies and their customers.

b) Deliver efficient customer support

Insurance companies are increasingly adopting chatbots to improve customer service by reducing response time and thus saving costs. These AI-powered solutions enhance team productivity by quickly resolving simple queries, allowing more focus on complex issues. Implementing virtual agents (chatbots) and personalized interactive videos enables 24/7, multi-channel customer service, positively impacting online experience, loyalty, brand reputation, and revenue generation.

AI in insurance enhances customer service in the following ways:

• Resolving FAQs: AI chatbots address common questions, reducing support tickets and costs. Learning Customer Behavior – AI learns patterns to offer personalized service options based on previous activities.
• Faster Response Times: AI speeds up support by providing agents with relevant information.
• Natural Language Understanding: AI analyzes customer interactions to understand and resolve issues promptly.

#3 Insurance Pricing and Underwriting

AI is transforming insurance by revolutionizing pricing and underwriting. It analyzes data patterns to price products accurately and identifies risk factors from claims data. AI also enhances underwriting decisions by swiftly analyzing applicant information. This potential for efficiency, accuracy, and profitability makes AI a game-changer in the insurance industry.

AI in insurance improves underwriting in the following ways:

• Pricing: AI analyzes vast data to identify patterns and accurately price products based on risk factors.
• Underwriting: AI enhances underwriting decisions by quickly and accurately assessing applicant risk based on various factors.
• Efficient Application Processing: AI automates data collection, extraction, and form filling, streamlining application processing.
• Better Risk Assessment: AI and ML models deepen understanding of customer risk profiles, enhancing risk assessment accuracy.
• Frictionless Customer Experience: AI shortens underwriting workflows, meeting real-time service expectations and improving customer satisfaction.
• Improved Profitability: AI-based automation improves underwriting profitability by reducing costs, customer churn, and retention expenses.

#4 Personalized Recommendations

Meeting the diverse needs, preferences, and lifestyles of customers is crucial in the insurance industry. Personalized policies, loyalty programs, and recommendations tailored to individual attributes and preferences have become essential.

Research shows that engaged and satisfied customers are 80% more likely to renew their policies. To meet this demand, insurers are utilizing advanced technologies like Machine Learning and AI to develop tools for creating personalized insurance plans based on customer data.

By implementing insurance chatbots and virtual assistants, insurers can provide machine-generated insurance advice, ensuring customers receive adequate coverage and a seamless experience. Additionally, chatbots and voice bots can engage customers with personalized offers, preventing customer churn to competitors and offering tailored recommendations and upselling opportunities.

AI Use Cases in Insurance

Insurance companies are increasingly using AI to improve their operations and provide better service to their customers. Here are some examples of insurance companies that have used AI:

• Lemonade: Lemonade is a New York-based insurance company that uses AI to process claims in minutes, while the traditional claims process can take weeks or even months.
• Hippo: Hippo is a home insurance company that uses AI to identify fraudulent claims with 99% accuracy.
  State Farm: State Farm is an American insurance company that uses AI to provide customers with 24/7 support through its chatbot, AskJake.
• AXA: AXA is a French insurance company that uses AI to develop new products tailored to the needs of individual customers.

A leading non-life insurance company in India offers a wide range of insurance products, including motor insurance. The company’s video chat-based claims survey solution uses the Robosoft Video Chat Solution to enable customers to have video chats with claims adjusters to discuss their claims. The solution also allows claims adjusters to record and capture images of vehicle damage. The solution has been a success for the company and has helped to reduce the time it takes to process claims.

As AI continues to evolve, we can expect to see even more insurance companies using AI to improve their operations and provide better service to their customers.

Wrapping Up

The advent of AI has the potential to revolutionize the insurance industry, bringing about unprecedented changes for insurers and their customers. With AI, insurers can offer their customers a more seamless user experience and potentially more affordable rates.

Moreover, AI enables insurance companies to optimize their processes, resulting in increased efficiency and cost savings. By leveraging AI technologies, insurers can make more accurate risk predictions, leading to safer policy decisions. As AI continues to evolve and be adopted by more companies, we can expect further advancements and improvements in the insurance landscape. Therefore, the future of insurance is indeed being shaped by the transformative power of AI, and this trend is only likely to continue in the years to come.

Wealth management was once the exclusive purview of financial advisors who managed the portfolios of a select, affluent few. Personalized portfolio plans and personal relationships drove such advice. The advent of digital technology has democratized many industries – and wealth management is no exception. As we know, in financial services, digital solutions are at the heart of the consumer experience.

The rise of fintech brands, especially those that help manage investments, is dependent more on technologies than on bespoke human advice, as it is all about scale. This has resulted in redefining wealth management as a service. According to a report by FactSet, investors across the wealth scale—from the mass affluent customer with $100 to invest to the ultra-high net worth (UHNW) client worth $10 million—are already embracing online platforms.

The key to digitalization success is targeting the right business areas, bringing in the right skills, and identifying the key processes to maximize value delivery. A comprehensive hybrid-advisory approach leveraging automation, data analytics, digital, and cloud solutions are the need of the hour.

The key pillars of digital experience in wealth management

Rapid technological advancements, changing investor preferences, and increasing financial awareness are prompting wealth managers to reconsider their customer engagement and business strategies. Digitalization helps modern wealth advisors create and understand their client personas better, moving away from “one size fits all” to a more customized approach. The right technology framework will lower infrastructure costs and improve the efficiency, speed, and scalability of the whole wealth management value chain.

Improving customer prospecting through AI/ML and digital onboarding

Digitalization through AI/ML can help wealth managers identify the right prospects and drive customer acquisitions through data-led personalized marketing. Its ability to combine data from various sources enables it to efficiently classify customer segments based on a variety of criteria, identify prospects using real-time data signals from social media, and generate dynamically personalized content for potential clients, all of which help to increase customer acquisition.

Digital Onboarding: Customer onboarding has traditionally required time-consuming manual documentation. However, many broker-dealers and other wealth management companies are digitizing and automating the process to enhance the client experience and save money.

The foundation for a long-term client relationship is established during the wealth management onboarding process, which includes the first serious interactions between an adviser and a client. Client onboarding processes include

• Prospecting
• Product selection
• Regulatory checks
• New Account Opening (NAO)

As a result, businesses are now able to onboard and serve more clients in less time and with fewer resources, maintaining their competitiveness in a market where investors and regulators are driving down fees. Firms with a robust digital onboarding experience will have a solid competitive advantage in the industry.

Achieving investor centricity through data analytics and management

Wealth managers need accurate and real-time data to assess investor sentiments, understand critical market parameters, and produce insights for quick investor decisions. Data can provide timely, pertinent, and actionable insights that can be used to create new (and enhance existing) product and service propositions, optimize channel management, generate higher returns through informed portfolio choices for the investor, and boost customer engagement, and customer retention.

Wealth managers can make wise decisions and appropriate portfolio modifications by using a quantamental investment technique that leverages sentimental analysis, alternative data, and return analytics. Most wealth managers have advanced their client analytics and advisory capabilities and are in various phases of development.

At present, wealth managers have most of their data locked in product silos and legacy systems. Before using advanced analytics, it is understood that access to precise and complete data is necessary. Wealth management companies need a client-focused, precise master data architecture that combines data from all points along the value chain. By increasing their investment in data management and analytics as part of their digitalization initiatives, wealth managers have a better chance of generating higher returns.

Personalized client experience at the front and center

Personalization is one way that advisors can stay competitive with other firms that may offer lower fees or higher returns on investments. According to a survey, investors are increasingly in need of personalized, goal-based planning and other specialized services. In the next two years, 58% of respondents said they would like personalized financial guidance.

Personalization as its name says is unique to each client. To build solutions that will work with whatever position the clients find themselves in, advisors have for decades always thrived on understanding their clients’ backgrounds and perspectives on risk. For instance, knowing information about a client’s household size, state of residency, and annual income are crucial data points in creating customized options that may be more suited for particular people.

Wealth managers can now offer personalized services at a reasonable cost, enabling them to better compete with firms that offer lower fees or higher returns on investments. Automated rebalancing and custom indexing are two examples.

Advisors can automate trading and rebalancing via automated portfolio allocation. And with the help of automated reporting tools, the adviser can inform a large number of clients about portfolio changes.

Enhancing digital investor management and advisory services

For the wealth management sector, it is crucial to offer a more holistic customer and advisory experience. In addition to the human touch, new-age investors are extremely drawn to digital personalization. Wealth managers may increase client acquisition by creating personalized content for potential investors using AI and data-enabled marketing. By increasing customer engagement, a redesigned digital experience can increase customer retention and give advisers more leverage.

A few of the main touchpoints are-

• Omnichannel engagement experience: Extends “zero-touch” service by using customized solutions built on video conferencing, on-demand virtual meet (with human advisor), and bot-enabled self-service. Portfolio review and building can be performed over user-friendly virtual solutions accessible over multiple channels.
• Data-empowered custom solutions: Includes chatbots and avatars that create a personalized and smoother investor experience, thereby promoting customer retention, upselling, and cross-selling. Many established firms are providing AI/ML-powered offerings to query investor portfolios and their holdings and provide data analytics on the performance of the securities in their portfolio.
• Advisor mobile apps: Enables wealth advisors to organize their activities and handle customer interactions. These apps (for example, MyMerrill) can include functionalities like advisor dashboards and 360-degree visualizations of customers and their risk appetites.

Adopting a cloud architecture to improve scalability and operational efficiency

The Information Technology (IT) landscape within wealth management firms consists of legacy systems that maintain a high volume of financial data, which requires increasing maintenance efforts and costs. An increase in financial data will drive automation processes and solutions as automation and AI/ML become more integrated into wealth management services.

Cloud infrastructure can offer a more reliable alternative to internal legacy systems for handling the increased inflow of data at scale, as well as higher operational efficiency and improved agility/time-to-market. By identifying the migration’s decision paths, which will guide the cloud migration strategy through the assessment, design, build, and migration stages, wealth management firms can optimize their existing application portfolio for cloud adoption.

Robo-advisory: taking the stress out of investing

Robo-advisors use automated, algorithm-based systems to provide portfolio management advice. These services are created with customer-centric thinking, and the technology is developed based on their wants and needs.

Customers are drawn to Robo-advice for a variety of reasons. First of all, it entails lower transaction fees and smaller investment requirements. Secondly, it entails more effective investment management. This is because the majority of Robo-offerings offer portfolio management using algorithmically based automated investment solutions that automatically rebalance the customer’s portfolio’s asset allocation without requiring any activity from the user. Thirdly, it provides less experienced investors with more comprehensive advice. Finally, Robo-advice offers more transparency on each investment and how they are likely to perform. The digital interface of many Robo-advisors makes it easy for an investor to analyze their returns versus benchmarks and progress toward goals.

Robo-advice services, whether new-age start-ups or established ones, also have the potential to widen the availability of investment advice from high net-worth individuals to less wealthy investors. Designing robo-advice services for the mass affluent presents a challenge because the customers may have good investment knowledge or little to no investment knowledge, and there is no human advisor there to make sure that the customer has understood the advice they have received.

Robo-advice services that are well-designed assist customers in receiving the best advice for their financial situation and reduce the likelihood that they will purchase the incorrect product. An agile, customer-experience-led, iterative strategy that designs and tests various interaction patterns is the most effective way to do this; whether that be an interactive Web or Mobile App, Chatbot, or combination of multiple technologies, that is right for the persona of a customer using the service.

Source:Inc42: How Robo Advisors Are Changing The Financial Advice Industry

Enhancing Digital Experience across the Wealth Management Value Chain

Opportunities for digitalization are seen throughout the wealth management value chain. An integrated digital transformation that addresses all the relevant user touchpoints would make it possible for investors and advisers to have a generally improved user experience. Every component of the wealth management value chain can be linked to a digitalization lever (Front office, Middle office, and Back office).

• Customer experience is adversely affected by front-office digitalization. The focus is on seamless engagement and improved digital user experience to reduce the turnaround time, increase process efficiency, and ensure a smoother customer journey by Big Tech and Fintech experience.
• The middle office, which drives the core line of operation in wealth management, is firmly focused on data analytics. However, given the sensitive nature of client data protection concerns and legislation like GDPR and equivalent laws coming into place around the world, a controlled approach to data management and cloudification is the way forward.
• Automation and cloudification are the main digitalization potential in the back office space. The user experience quotient is not very high primarily because the activities are more in-house driven rather than external stakeholder driven.

The Road Ahead

The need to stay digitally connected and have a lasting influence on investors and asset managers have accelerated after the global pandemic. The focus is on creating a digital ecosystem built on tools and measures for a touchless remote experience without compromising on quality, which may have permanently changed how people work.

From a service and product perspective, the focus is steadily moving toward personalization, driven by effective data analysis. The emphasis is now on specialized products, personalized advisory services, and flexible pricing structures for different investment classes. One key factor that unites these shifts is the proactive application of technology and accelerated digitalization, whether inorganically or organically.

Effective use of technology through an omnichannel delivery model is essential for people-centric and relationship-driven industries like wealth management to promote the right level of customer engagement. Firms can look forward to investing in in-house technology and aligning with tech vendors, for the timely implementation of modern investment solutions, keeping relevant to a variety of customer segments, and staying ahead of ever-increasing competition.

As an industry, insurance is often considered to be evergreen in terms of profitability. However, while demand for insurance has grown globally, insurance providers are facing a number of challenges that affect profitability.

According to a McKinsey report, profits reduced by around 15 percent compared to 2019, and premium growth had slowed to around 1.2 percent in 2020, compared with over 4 percent growth in the decade between 2010 and 2020. However, post COVID-19, insurance companies are poised to take advantage of the rebound. Based on a survey of over 424 insurance respondents, the Deloitte Centre for Financial Services reports that insurance companies have an optimistic outlook, while the Swiss Re Institute predicts rising demand worldwide, with premiums for all lines rebounding by 3.9 percent in 2022, as compared to the earlier drop of 1.3 percent in 2020.

Despite the opportunity, risk factors have increased and insurers are aware of the need to deal with challenges facing the industry and build business resilience. Across the insurance sector, there is a higher claims incidence, leading to increased pay outs and loss of profitability. Insurers need to maintain higher capital because of regulatory pressures. They are also facing increasing competition from new entrants in insurtech.

Property & casualty insurance at a pivotal stage

The US property and casualty (P&C) insurance market is a case in point. Not so long ago it was the pandemic that was keeping insurers awake at night. Now, inflation is putting them to the test. The US holds the biggest market share of P&C insurance and is among the most mature geographies for insurance. However, inflationary pressures are significantly holding down P&C insurance carriers. Although the market grew by a moderate 4.7% between 2018-19, these challenges are dampening future prospects.

Source: McKinsey & Company

Opportunities in P&C are rising, but insurers are weighed down by market volatility, changing customer expectations, and loss of profitability to name a few. Given the uncertainty of tomorrow, P&C insurers need to build business resilience – the ability to endure present challenges and be ready for future shocks and negative events.

The insurance industry is learning from the success of insurtechs and waking up to the advantages of strengthening customer connections by going digital. One of the biggest positives in recent years is the accelerated digitalization that enables non-face-to-face interactions between the insurer and the insured. Today, customers want more intuitive, user-friendly apps that will help them gain quick access to relevant data that will help make the right choices for their insurance needs, streamline processes, and more. They also expect 24/7 assistance in making decisions. Research indicates that a good number of respondents are ready to move away from insurance companies that do not offer these advantages.

Source: Accenture

Building resilience in P&C insurance with technology

As P&C insurance is complexity-ridden and highly regulated, the way forward needs to be holistic and future-ready. Resilience is key to countering market volatility, inflationary pressures, and intense competition – that means becoming nimble, responsive, and well-prepared for all the odds. These challenges, considered together, call for a major shift in strategy by traditional insurance players.

Technology can provide P&C insurance companies with not just the chance to become more future-ready but also to differentiate themselves using technology. Insurers can adopt digital to create a wider platform that can connect customers, partners, and employees all at the same time. Tech innovation finds application across the value chain from marketing through distribution, products and services, and throughout backend processes from underwriting to claims. Technology can also help design and create memorable user experiences, engaging with customers more deeply with empathetic, hyper-personalized interactions at various touchpoints of the customer journey.

Source: Aite Group

Streamlining claims with automation, AI, and straight-through processing

Automating claims processing, typically a long and tedious task, reduces processing time and cost by programming tools to handle repetitive tasks.

Source: Altexsoft

Self-service in insurance is also becoming popular. One such example is that of easy claims filing online by customers themselves. Apps offer features allowing customers to take photos and upload them while lodging claims for auto insurance from the site of the event itself, enabling remote inspection. This advantage is offered by Bdeo’s AI-enabled platform on their mobile app. In addition, their chatbot uses Natural Language Processing to get reliable information on the accident by ensuring claimants share photographic evidence of acceptable quality on the platform. The insurer uses these inputs as part of their inspection and investigation of the incident remotely. This helps make more accurate evaluations and improves the claim processing experience for both the insurer and claimant.

AI will make it even easier to identify fraudulent claims, as well as provide predictions and estimates of possible damages and extent of losses. In fact, studies indicate that AI has great potential in disrupting core processes including underwriting, claims, marketing by enabling more human-like interactions with customers. Here we illustrate the use of AI in streamlining content extraction that can speed up the processing of claims dramatically while making communication and other value-added services easier to achieve.

Source: Whatfix

Straight-through processing is another solid investment pathway for insurers – it can be done without any manual input, reduces operational costs, and helps insurers take pricing volatility in their stride. At the same time, policy holders can choose how they want to receive claims settlements in a convenient manner.

Strategic decision-making bolstered by Robotic Process Automation (RPA)

Property and insurance carriers tend to be skeptical about RPA, however RPA has proven to drastically reduce errors, improve operational efficiencies, enhance scalability, and optimize headcounts at financially viable numbers. The benefits of RPA multiple when the insurer decides to automate non-strategic tasks while retaining humans to perform value-added tasks. For example, in auto insurance, gathering and validating information around accident claims from various sources such as police reports of accidents, driver’s licenses, photos of damage to the vehicle(s) involved etc. can be tedious and time-consuming, but RPA can comb through data from various sources quickly.

The true potential of RPA is visible when it is collaboratively applied with other technologies to augment strategic decisions made by the human workforce. In the underwriting process, which is again typically lengthy, bots can leverage AI and analytical capabilities to glean information from both external and internal sources, fill up application forms with appropriate data, assess loss runs, evaluate the claimant’s past track record of claims and settlements, and offer pricing based on this deep well of insights – all in quick time. With RPA, it also becomes easier for insurers to keep up with multiple regulatory standards, which are still evolving and ensure compliance.

Driving responsible customer behavior using telematics

Arriving at the optimal price is foremost for 52% of auto insurance customers and 50% of home insurance customers, as per an Accenture study. Giving their customers optimal pricing will instantly create a competitive advantage for insurers. Telematics applications are already being used to great effect for pricing in the automotive insurance space. Now there is scope to use these applications such as vehicle tracking to instruct and control driver behavior and conditions such as drowsiness, for example.

Smart household or property devices that come alive with the Internet of Things (IoT), will come in handy for closely monitoring data such as humidity and temperature, which could potentially cause damage or depreciation to the home infrastructure or property over time. Data and analytics extracted from these devices can be used to plan risk assessment and inspection of large commercial properties with associated risks. Visuals captured via satellite can be used to draw attention to potential risks or causes of damage to property in a cost-effective manner without requiring an in-person visit from the insurer. These insights can be used by insurers to both monitor and suggest pre-emptive measures around potential risky behavior or lifestyle of policy holders.

Smartening up insurer-insured relationships through smart contracts

As early as 2018, BCG research had predicted that smart contracts could help P&C insurers with savings of more than $200 billion per year in their operational costs.

Here’s why smart contracts are the smart option. These are formulated in lieu of physical insurance policies and all associated claims management information using blockchain technology, which stores transactions as lines of code. Tracking, management, and cloud storage of policies, records of physical assets, and claims-triggering events can all be automated and also takes care of user authentication and detection of fraudulent activities. Smart contracts are easily accessible by insurers, reinsurers, brokers, and other parties thus reducing duplication of effort as well as manual intervention and programmed for claims processing actions automatically.

Unlike physical contracts, smart contracts can track insurance claims and hold both parties accountable. These contracts are, thus, inherently more secure and transparent for users, settle payments on approved claims quickly, and go a long way in lifting the trust factor as well as efficiency of back-end operations.

Ensuring omni-channel customer experience with bots and tools

Keeping up a persistent line of communication with policyholders and prospective customers is important for the insurer in terms of business and keeping customers interested. Chatbots can be introduced early in the discovery or pre-sales stages to initiate conversations about product offerings that might be suitable to customers and prospects. Likewise, the customer’s need for 24/7 assistance and quick responses can be met by chat-bots rather than wait for a human team member who can be freed up to address core service areas. They are already proving popular in the auto and home lines of P&C insurance. For example, Lemonade, who is a leading online P&C insurer, extensively uses app-based chat-bots supported by AI. Their bots are able to devise insurance policies and quotes that are tailored to each customer and available directly on the app. They also interact with and field customer queries while helping process claim applications promptly.

Familiarizing customers and employees with digital adoption platforms

Not all employees are tech savvy. Neither are customers. It makes sense for insurers to invest in training to improve agent performance and customer experience. Integrating training tools with the tech stack adopted by the insurer, will help their workforce and customers effectively use the various tools and processes that are now available to simplify the myriad processes that go into insurance. These include walk-throughs, self-help widgets, quick guides, and AI-enabled assistants for employees.

In conclusion

P&C insurance companies have to work through a maze of challenging situations and gaps as they go about strengthening relationships with customers. AI/ML, IoT, telematics, blockchain, and other emerging technologies can offer a wider, more personalized spectrum of benefits by harnessing data, shaping new offerings like social insurance, and more.

In short, technology can be the bridge that makes interactions meaningful and productive for both insurers and those seeking insurance. It remains to be seen how far P&C insurance players are ready to leap in building the bridge that holds the key to the future.

In a world of unpredictable yet unavoidable change, individuals, companies and even governments turn to the insurance sector to be prepared. Insurance needs are changing in many ways, such as including risks related to climate change like floods or bushfires, or even novelties like ‘hole-in-one’ insurance (where a golf tournament insures itself to pay the prize bonanza on the off chance that a hole-in-one is achieved). With an increasingly online generation, even as traditional insurers bank on the credibility and trust they have accumulated, the new age InsurTech companies chip away at the market with digital experiences and new models.

When it comes to innovating with technology, InsurTech companies have an advantage over traditional insurers. They are nimble and flexible with their offerings, able to quickly establish low-cost digital platforms and new operating models. InsurTech companies’ biggest advantage is the hold they have over the customer’s pulse. With smartphones becoming commonplace, customers find that InsurTech offers comfort, convenience, and speed in making important insurance-related decisions and transactions.

Research indicates that 28 of the 280 FinTech firms that have turned unicorns to date have played an instrumental role in driving innovation or disrupting the way insurance is done. From claim management to reinsurance, asset management, customer onboarding, and engagement, InsurTechs have earned their colors across the insurance value chain and are here to stay.

Challenges to InsurTech

Despite these gains, experts believe that InsurTech market growth will have to endure several constraints. Foremost among them is a lack of awareness about the value InsurTech can deliver, and dearth of professionals who can expertly work with advanced technologies. These factors could restrict InsurTech companies from scaling their technology capabilities to the extent desired.

However, it is undisputed that the future of insurance will be tech-driven in the form of embedded ecosystems, AI & ML, blockchain, low code technology, and more. 85% of insurance companies recognize the need to prioritize digitalization, so it may not be long before traditional market leaders catch up with their technology capabilities or look to buy out smaller players. InsurTech start-ups have their work cut out in gaining the kind of trust and credibility enjoyed by established insurers. Now, they must rethink strategy to retain their technology advantage.

If we look at the US which is the global market leader, InsurTech is expected to grow at more than 7% CAGR over the next five years. The competition is definitely heating up here.

Read more: The Rise of FinTech in Asia: Success Stories and Learnings

Business opportunities for insurance will continue to flow in as the world becomes increasingly digital. More aspects such as health, travel, auto, and home will be included under the umbrella of online insurance.

InsurTech companies will need rely on their strength – technology – to offer a wider, more personalized range of benefits shaped by data, new offerings like social insurance, and cost saving tools like virtual agents powered by conversational AI etc.

McKinsey research opines that five rapidly advancing technologies will significantly redefine the future of insurance. These include applied AI, distributed infrastructure, future of connectivity, next-level automation, and trust architecture. By putting the full force of their tech advantage here, InsurTech players can solidify their business and expand their portfolio.

1. Powering up core processes with AI

Since the pandemic, at least a quarter of life insurers in the US have expanded their automated underwriting practice to simplify the application process. From reducing claims processing time and cost to improving fraudulent claim detection and claim adjustment processes, AI and automation are proving be invaluable.

Take for example, the AI-enabled platform offered by Bdeo, available on their mobile app. It comes with a chatbot that uses Natural Language Processing principles to liaise with claimants, get first-hand info on the accident/damage that has occurred and helps them share photographic evidence of acceptable quality on the platform. The insurer can use the app to inspect and investigate the incident remotely using computer vision models. Doing so helps avert errors in evaluation and improves the overall claim processing experience for both the insurer and claimant.

Studies predict that AI will disrupt underwriting, claims, marketing, distribution and other core processes by enabling more human-like interactions across various customer touchpoints. There is a plethora of opportunities that can be exploited. For example, the associated customer data can be used for predictive analysis and forecasting, which can in turn, inform the development of new product and service lines.

2. Enabling intelligent insurance with distributed infrastructure on the cloud

Many core insurance processes that have been weighed down by legacy systems are finally modernizing. This allows insurers to leverage cloud-native infrastructure, ramp up to manage workloads without impacting customer experience and speed up their innovation efforts. Thanks to cloud computing, they will be better placed to harness the massive amounts of claim-related data available to benefit their customers and increase profitability.

This is a huge opportunity for traditional insurers to collaborate with InsurTech to form partnerships that leverage their strengths and quickly enable plug-ins, distribution channels, and other value-adds. For instance, InsurTechs can offer digital solutions to efficiently sift through vast historical data of established insurers, to identify and interpret customer patterns and insights to determine the kind of new product/service lines to be developed. In fact, at least 75% of insurers were found to be seeking out InsurTech collaboration to improve their customer experiences according to a Capgemini survey.

3. Developing insurance products using telematics

Telematics technology is increasingly being used to monitor, interpret, even influence consumer behavior. For example, innovation stimulated by IoT adoption is being applied in connected home devices to track humidity, temperature and other parameters, which potentially cause damage to property. Insurers can leverage the data generated on these devices to estimate risk over time. Similar innovations are being explored across the domains of insurance to life, health, auto, manufacturing, commerce etc. The advent of 5G will enable real-time data sharing and make it possible for insurers to turnaround services faster than ever.

For example, being covered against ride cancellations is a value-add for customers and digital solutions can be developed to enable this as a timely service using real-time availability of data. Another example of value-add is the coverage against bodily harm to earners and riders of every trip offered by Uber in partnership with a leading insurer.

4. Enabling human decisions via bots

While robotic process automation (RPA) has proved its worth in automating back-office functions in the insurance industry, there’s a lot it can do in terms of next-level process automation that will shape the future of insurance. For example, the IoT-enabled, real-time monitoring of factory equipment can predict maintenance needs and prevent repair or damage that result in insurance claims.

RPA also has a distinct role to play in supporting human decisions in a cost-effective and timely manner. As an example, it can expedite claims processing wherein photos of the damage to a vehicle are automatically assessed and verified for authenticity without requiring an in-person visit by a claims adjuster to the damage site. Likewise, building optical character recognition features into RPA will help extract text from claim applications in large volumes and ensure that the information it contains is distributed to the right functions for further processing.

5. Laying the foundations for trust with blockchain

Increased digitalization of insurance is raising security concerns due to the sensitive nature of customer data that is being shared across the insurance ecosystem. Building customer trust will be a priority for insurance players, which is where blockchain comes to the rescue.

Along with its advantages of transparency and efficiency, blockchain will play a leading role in helping carriers safeguard customer data from cyberattacks and data breaches. It will also simplify user authentication, identity management, and fraudulent claim detection etc. Through blockchain-based smart contracts, policies can be converted to decentralized lines of codes that will make consumer’s data immutable and easily available for immediate verification in the event of any claims made to the insurer. If it proves to be fraudulent, the contract will immediately be discontinued, and the premium amount paid returned to the insured. This kind of data transparency and responsiveness of the system will help build trust between all concerned parties.

The future of digital insurance paved by tech-led design

As insurance becomes more digitally driven, user experience (UX) will be all the more crucial for branding. While an omnichannel insurance experience is the norm today, creating memorable user experiences at all possible touchpoints will be paramount to carving out stronger market positions for the InsurTech brand.

For example, the silver agers generation are no longer the most dominant consumers of insurance. In fact, studies that the interest now being shown by millennials and Gen-Zers towards insurance products exceeds that of the older generations.

Or, as this chart indicates, nearly half the individuals in the 65+ age category are unlikely to use an insurance app. If the insurer wants to attract more consumers from this cohort, they will need to leverage data to understand preferences, simplify interfaces, customize their offerings and so on.

According to the World InsurTech Report 2021, half of the insurance customers are willing to explore solutions offered by new-age digital players. The insurance market will experience disruption and a new order will emerge. Traditional insurers are more likely than ever to engage in partnerships with InsurTechs to stay relevant. Niche players and start-ups in InsurTech will not only need to leverage emerging technologies but also understand the complexities of insurance better and closely follow changing needs of their target demographics.

Led by research, data analytics, and empathic and intuitive design of user-centric interfaces, InsurTech players will be able to create market differentiation that can help them explore opportunities to build partnerships with traditional players so that both survive and thrive.

It’s challenging for traditional insurers to stay competitive in today’s dynamic digital market. Insurtech companies however are growing in the current experience economy, where consumers value experiences and emotions over goods and services. These forward-thinking, technologically driven businesses are providing great customer service by adapting to and exceeding the ever-changing demands and expectations of today’s customers.

According to a recent IDC InfoBrief sponsored by Liferay, customer experience (CX) will account for 37% of IT spending in the insurance sector by 2024 and grow at a CAGR of 17.5% to reach 50 billion USD. The IDC InfoBrief also states that “providing an excellent and personalized experience to new digital customers is a must for any insurer to build loyalty and long-term relationships, with 60% of insurers saying attracting and retaining customers are their top priorities”.

It is evident that insurers would face difficulty acquiring and retaining customers without a customer-focused strategy. Insurance companies must rethink how they engage with clients if they want to improve customer experience.

Increased Expectations for a Seamless CX

The largest customer segment for insurance was typically thought to be the silver agers generation. However, a survey by Allianz indicated that after the pandemic, millennials and Gen-Z were more interested than their older counterparts in upgrading their insurance coverage. However, younger customers are used to a clear and smooth digital experience, from social networking to mobile banking to music streaming and e-commerce services. Expectations from the insurance industry are no different. According to a study, customer experience (CX) accounts for nearly 60% of brand loyalty, and 96% of consumers believe that customer service is essential for maintaining brand loyalty.

The insurance sector is finding it challenging to meet these expectations. In a survey from the IBM Institute of Business Value, 60% of insurers acknowledge that their company does not have a customer experience strategy. Although the insurance sector has developed and offered some specific insurance products in response to these shifts in customer expectations, it is still trailing in providing clients with the comfort and ease that mobile technology can give.

Digital Trends Insuring a Better CX

The use of mobile phones and the internet has increased significantly worldwide. And when it comes to leveraging digital technologies to scale their business model and work toward providing a hassle-free experience, the insurance sector is not far behind. Some of the digital trends in the insurance industry are listed below:

1. Omnichannel insurance customer experiences

EY Global Insurance Consumer Report revealed that during the COVID-19 pandemic, digital interaction with agents was preferred by 43% of consumers in Europe markets, up from 28% before the pandemic.

In addition to preferring digital interaction, audiences, particularly younger ones, expect consistent, integrated experiences across all communication channels. For instance, if a customer begins a claim submission procedure over the phone and wishes to complete it through the web customer portal, he or she must be able to do it without having to input specific information once more. To meet such demands and preferences of the customer, there is an increasing desire for more integrated and holistic experiences that offer a unified, connected experience flow across channels.

Insurers thus need to assist the customers at every stage of their journey by establishing an omnichannel ecosystem for marketing, sales, and customer support. It helps them gain consumer loyalty along with giving them better control over the CX. These omnichannel experiences can be created by:

● Assisting customers on their preferred web channels

● Connecting customers’ offline and online experiences

● Tailoring the content of websites or mobile apps for various screens

● Following client activity across channels with the help of advanced monitoring technologies

● Using progressive profiling and autofill forms to prevent users from frequently filling out forms while logging in from various devices or platforms

● Making use of client information to retarget individuals with tailored campaigns across platforms, etc.

2. Self-service options

A self-service portal that allows customers to manage their policies, make payments, submit claims, etc. is something that more and more customers are expecting from their insurance service providers. According to the World Insurance Report, 72.7% of tech-savvy customers prefer to renew or apply for coverage digitally while 57.6% of non-tech-savvy customers agree that digital policy management is important. Statistics indicate that most customers are equipped for self-service policy management, regardless of their level of digital skills. For insurance companies, self-service capabilities have several advantages:

● Lower customer acquisition costs

● Increased customer retention and loyalty

● Rapid claim processing, etc.

3. Personalized insurance apps

Over the past few years, the number of customers who would transfer insurance providers owing to poor UI UX rose by 80%. Custom insurance applications are currently the most popular channel for customer account service because a huge chunk of insurers allow customers to manage their policies via mobile apps. When providing personalized services, insurers saw an 81% increase in customer retention and an 89% increase in customer engagement.

Source: Capco Global Insurance Survey

The below qualities should be present in the customized app:

● A user-friendly interface that is compatible with both computers and mobile devices

● Workflow automation and analytics capabilities

● Payment processing

● Personalized dashboards for simple policy management and monitoring

● Other features include an embedded knowledge base, electronic signatures, and downloadable documents.

4. Internet of Things (IoT)

The insurance industry will undergo major changes in the future as a result of IoT. These technologies can be used in the four largest digital insurance ecosystems: connected cars, smart homes, connected health, and commercial lines.

The IoT makes it possible for insurance companies to use data from internet-connected devices to improve operational effectiveness. These interconnected devices communicate with one another automatically, allowing for more precise predictive analytics, snap decisions, and seamless process automation.

IoT has made it possible for insurers to collect data on policyholder behavior and quickly alert them about accidents. In this approach, IoT helps with both customer relationship management and claims processing. While customers might forget to recall and submit event details, IoT records everything. As a result, insurers are better equipped to precisely analyze damage, pinpoint the exact cause of accidents, and determine fair compensations.

5. Chatbots

Another effective technique that insurers of today should use to meet client expectations is chatbot technology. These are bot-powered chat widgets that have been added to the insurer’s website, messaging service, or client portal. Insurers must set up chatbots for many reasons:

● They assist prospects by providing quotes or addressing queries

● They improve customer experience by offering 24/7 help

● They free up agents by handling repetitive customer queries

● They generate leads by collecting visitors’ contact information

● They qualify leads and automatically identify the right plans for them

● They can be used to automate the claims process

When it comes to processing insurance applications and claims, a good chatbot can almost entirely take the role of a real person. This cutting-edge strategy enables insurers to provide excellent customer service while enabling agents to concentrate on more difficult responsibilities.

6. The rise of Insurtech

Insurtech is the application of cutting-edge technology to innovation in the insurance industry. More specifically, big data, AI, blockchain, IoT, natural language processing, and other technologies are what power Insurtech. The most important insurance operations, such as underwriting, fraud prevention, claims processing, etc., are addressed by these solutions.

The core digitization strategies stated above are only the outset of Insurtech. It replaces conventional, legacy-driven insurance procedures by adding these cutting-edge technologies. The most frequent use cases for Insurtech involve risk assessment and mitigation due to the constant emergence of new threats.

7. Predictive Analysis

Predictive Analysis has always been a major aspect of insurance agents’ day-to-day tasks. The role of insurance agents has always included a significant amount of predictive analysis. The data analytics landscape saw a significant transformation in recent years. Agents can now select from a wide range of tools and techniques to carry out a precise Predictive Analysis. If they wish to remain competitive and meet the criteria set by InsurTech leaders, they need to quit depending on manual processes.

Predictive Analytics has been credited by insurers with lower underwriting costs (67%), increased sales (60%), and increased profitability (60%). In 2022 and in the years to come, predictive analytics will become increasingly valuable in the insurance industry.

Predictive Analysis can be implemented in the following tasks:

● Insurance pricing and policy optimization

● Risk assessment

● Fraud detection

● Claims management

● Proactive customer engagement

For instance, prediction algorithms powered by machine learning can enhance insurance plans and present more pertinent insurance products to prospective or existing consumers by analyzing customer behavioral signals and purchase trends. Predictive analytics eliminates the component of the guesswork from the policy pricing process, enhancing customer satisfaction and boosting revenue for insurance businesses.

8. Artificial Intelligence

The insurance sector is actively implementing AI-powered solutions as the technology becomes more and more prevalent and is utilized to power a variety of activities. By 2030, automation powered by AI is expected to replace more than 50% of claim-related processes, according to McKinsey. Insurers need to start their AI journey right away to make it practicable.

By automatically analyzing vast volumes of consumer data, AI processing enables insurers to provide customized client experiences. These innovations significantly alter the entire underwriting process while simultaneously speeding up claim response time.

The following technological solutions give insurers access to AI capabilities:

● AI-enabled insurance chatbots

● Predictive analytics tools

● Fraud detection software

● Document capture technologies

● Risk management software

● Claims processing software

Additionally, AI and similar technologies aren’t susceptible to human error, thus removing associated risks that could have a detrimental impact on insurers’ profitability.

9. Simplicity and speed improve customer CX

Insurance is a very sensitive matter in terms of customer experience. This is because when an accident occurs, the essential quality evaluation by customers takes place at a time of the highest emotional fragility and strain. Complex contracts and a bad claims experience are the two primary causes of friction. Because of this, many customers have negative impressions of their insurance experiences.

Customers report a lack of knowledge, assurance, and trust in insurance, according to the EY 2021 Global Insurance Outlook. This is further supported by the idea that, rather than providing for their clients, insurers frequently seek different justifications and defenses to avoid paying insurance compensation. Such immoral practices will have disastrous CX effects.

Source: EY Global Insurance Outlook

Insurance companies will need to integrate digital channels with back-end systems, automate manual operations, and improve third-party processes to enable faster digital processing to meet client demand for speed. As digital behavior develops and consumer expectations, influenced by digital services from various industries rise, the necessity for simplicity and speed will only expand.

10. Customer-Centered Design

In an era where customer experience is beginning to trump price and product as a brand advantage, it’s critical to not undervalue the importance of personalization and customer-centricity.

Given that the growth of digital ecosystems has lowered the barrier to entry, brought about the rise of new players (Insurtech businesses), and increased market rivalry, the ability to offer customers the best value has emerged as the primary competitive advantage. Corroboration of increasing innovation may be found in operational areas like policy servicing (in life insurance), claims (in vehicle insurance), and back-office operations (in health insurance).

Companies in the Insurtech sector create digital products with the customer at the center and offer disruptive services to the market. When a customer interacts with an insurance business, it’s important to understand their situation and be empathetic. The only approach to develop that level of empathy is to conduct insurance UX research and use the results of that research to design and construct a customer-centric humanized insurance UX/CX. The personalized claims experience and the personalized buying experience are the two major touchpoints in the insurance customer journey.

According to a Capgemini survey, eight in ten consumers are willing to pay more for improved customer service. According to Accenture’s Global Insurance Consumer Study, 69% of customers would give major data on their health, exercise, and driving habits in exchange for lower insurance rates, and 66% would also disclose significant data for personalized services to reduce injury and loss. Good customer service frequently results in returning business and referrals to friends and family.

CX Trends in Insurance: Right Implementation from Best Insurtech Companies

1. Lemonade

Lemonade is a digital-only insurer with a web & mobile platform. The platform offers:

● Chatbot-powered conversational policy purchase process

● Renter’s insurance within the app

● Car insurance is based on driving habits

● Easy switching from other insurers to Lemonade

● AI-assisted claim review and instant payment

● Option to donate unclaimed premiums to the charity of choice

2. Metromile

Metromile is the leading pay-per-mile car insurance in the US offering personalized digital insurance services. The platform offers:

● Rates are based on the user’s driving habits

● Telematics devices that plug into the car’s diagnostics port and driver’s mobile phone to get data on car usage for better CX

● AI-assisted claim review system

● Access to a nearby garage, vehicle rentals, etc. to get back on the road in case of an accident

3. Beam Dental

Beam Digital is a digital-first, preventive-focused dental insurance offering simplified digital insurance benefits. It offers:

● Electric brush connects to their app to help track and improve brushing habits

● Gamified dental care turns good brushing habits into rewards and savings

● Nearby dentist and hospital search

● Online access to coverage and plan information 24/7

4. Luko

It’s a digital home and real estate insurance app offering prevention, comprehensive coverage, maintenance, and repair. It offers:

● Damage assessment by an expert through a video call

● Access to free services to fix damages and a network of certified skilled workers to get advice through video calls to help with minor repairs

● Partnership with quality smart home device service providers and offers a discount on insurance if security solutions are used

● To donate unclaimed premiums to causes chosen by users

5. Thimble

It’s an on-demand business insurance platform made to serve small businesses and self-employed. The offerings include

● Buy a policy online, in the app, or over the phone instantly

● Customized insurance coverage by hours, days, months, or years

● Flexible payment options for premium

● Options to change, pause, or cancel policy anytime

● No direct claim handling hassle as they are handled by underwriting partners

At Robosoft, we have partnered with enterprises across the spectrum of financial services – insurance, digital banking, payment, lending, and more. We craft digital solutions that simplify lives and delight customers seamlessly across consumer touchpoints.

In Conclusion

In order to win the hearts and loyalty of digital customers and remain relevant in the future, insurers must first foster a transformation culture and customer excellence culture to position towards delivering value to customers and solving their real problems.

This will allow them to evolve from “detect and repair” to “predict and prevent”. It also implies making use of the possibilities that emerging technologies bring. This involves upgrading service channels, adopting, developing, and leveraging new technologies, tackling legacy systems, leaving behind inefficient processes, changing company culture, and shifting mindsets.

Digital is a key battleground in the experience economy, with significant opportunities for those that delight consumers. Let’s get to work and simplify digital experiences and the lives of consumers.