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.

Legacy modernization is the process of transforming outdated business technology systems, known as legacy systems, into modern infrastructure and functionalities. It is the process of updating or replacing outdated software using modern programming languages, software libraries, or protocols and giving a makeover for the digital age. This article outlines the blueprint for modernizing legacy systems and lists key benefits that accrue from this initiative.

Assessment Methodology for Legacy Modernization

The legacy modernization exercise commences with an assessment of the existing application landscape to determine the ability of the existing technology systems (application and infrastructure) to support evolving business needs. A detailed roadmap is drawn up subsequently to complete the exercise. The assessment involves gathering data points around different aspects of the technology landscape. This is supplemented with structured interviews with key stakeholders representing business and technology to understand current pain points and future requirements.

Quantitative Data Points

Quantitative data delves into various platform dimensions, including application stability, business criticality, technology stack, process discipline, infrastructure, and non-functional requirements. Specific data points might include outage frequency, unresolved ticket counts, planned enhancements, technology stack details, interface protocols, data volumes, compatibility of software development tools, adherence to best practices, hosting configurations, disaster recovery plans, and performance scalability metrics.

Qualitative Data Collection

Qualitative data gleaned through stakeholder interviews sheds light on future business goals, technology preferences, regulatory constraints, and pain points. This input enriches the quantitative analysis, painting a holistic picture of the current and future aspirations.

Analysis and Roadmap

Armed with this comprehensive data, we embark on the analysis phase. This involves meticulously examining software code, database structures, and the interplay between quantitative and qualitative inputs. The culmination of this analysis is a robust blueprint for the legacy transformation exercise.

The blueprint addresses critical challenges and proposes targeted solutions, each delivering distinct benefits. For instance, monolithic architectures plagued by high ownership costs can be transformed into loosely coupled microservices, enabling simpler deployments and improved scalability. Performance bottlenecks can be tackled by introducing auto-scaled middleware and databases, paving the way for future business growth. Similarly, implementing caching layers and monitoring tools can enhance performance and operational efficiency.

Benefits of Legacy Modernization

Some of the key benefits are listed below:

• Improved functionality and security: Modern technologies offer better performance, scalability, and security features compared to older systems.
• Reduced costs: Maintaining outdated systems can be expensive, while modernizing can lead to cost savings on maintenance, licensing, and energy consumption.
• Enhanced agility and flexibility: Modern systems are easier to adapt to changing business needs and integrate with new technologies.
• Better user experience: Modern interfaces are more user-friendly and accessible, leading to improved employee and customer satisfaction.

Conclusion

Legacy modernization holds lessons for the entire financial services industry. It demonstrates the power of technology to unlock economic potential, empower migrant workers, and strengthen local communities. By embracing innovation and adaptability, financial institutions can thrive in the competitive landscape and contribute to a more inclusive and equitable global economy. By partnering with a reliable and proven digital transformation partner, legacy modernization in one market can inspire and pave the way for similar advancements across the globe, ultimately benefiting communities and individuals.

As digital banking is evolving at a rapid pace, it continues to be a necessity in the modern financial industry. Almost 78% of Americans opt for banking through mobile apps or websites rather than traditional banking—in-person visits to a bank branch. Customers expect seamless, secure, and user-friendly experiences across various digital platforms they use on a day-to-day basis. Technological integration has led to innovative solutions such as Internet banking, mobile wallets, and mobile digital banking. However, with this evolution comes the need for continuous improvement and optimization. The rapid adoption of digital banking solutions has led financial institutions to constantly seek ways to optimize their technology infrastructure and stay ahead in the competitive landscape.

This is where the technology benchmarking of digital banking platforms comes into play, offering financial institutions a way to measure their performance of the digital banking platforms against industry best practices and identify growth opportunities. Comparing the digital banking platform performance, processes, and outcomes against industry best practices – technology benchmarking – is essential. For digital banking platforms, this means assessing aspects such as current IT applications and services—internet and mobile banking applications, mobile wallet services innovation, remittance system efficiency, security protocols, and overall customer experiences.

Leveraging Technology Benchmarking for Future-proof Digital Banking Solutions

The banking industry is witnessing an influx of disruptive technologies reshaping customer expectations and experiences. From seamless mobile wallet integrations to swift and secure remittance services, customers now demand convenient, user-friendly, and technologically advanced solutions. Banks or financial institutions must leverage digital banking benchmark analysis for technology enhancements to continuously align their offerings with industry-standard practices by addressing shortcomings and adapting their digital banking platforms to deliver superior customer experiences.

Adapting to the evolving landscape also involves understanding the changing regulatory environment by being vigilant of the latest industry standards. Compliance and security are paramount in the digital banking landscape, and digital banking benchmark analysis for technology on a level with industry standards helps banks and financial institutions ensure that their platforms meet stringent requirements and prevent or mitigate risks effectively.

The methodology of the technology benchmarking of digital banking platforms entails analysis of the existing technology landscape with a focus on the following:

• Existing Technology Stack for Scalability and Performance: A comprehensive digital banking benchmark analysis for technology stack can gauge its capacity for scalability and performance in future business needs. The goal is to ensure the platforms can seamlessly handle increasing loads and efficiently cater to growing user bases.
• Service-oriented Architecture: The structural backbone of the architecture of digital banking platforms is scrutinized for its alignment with service-oriented principles. The digital banking benchmark analysis for technology assists in the identification of opportunities for enhancing flexibility and streamlining processes.
• Database Design and Scalability: The digital banking benchmark analysis for technology also involves evaluating the efficacy of the database design with an eye on scalability. By optimizing database structures, digital banking platforms can ensure seamless data management as their operations grow in size.
• Security and Compliance: A paramount consideration in the digital banking benchmark analysis for technology is security and compliance, which entails a rigorous assessment methodology. This dimension involves evaluating the technological measures in place to safeguard sensitive customer data and ensure compliance with the applicable regulations and industry standards. Some key aspects to be considered include encryption protocols, authentication methods, fraud detection, and the platform’s adherence to compliance requirements such as GDPR, PCI DSS, and other regulations.
• Competitor Analysis: A thorough understanding of the competitive landscape is crucial to benchmark digital banking platforms effectively. This involves analyzing key competitors’ market presence, feature gap analysis, technological offerings, and performance. By comparing and contrasting these factors, we can get valuable insights that help fine-tune strategies and differentiate the platform in a crowded market.
• Innovation and Future-readiness: This aspect requires evaluating the digital banking platforms’ capacity for innovation and future readiness. Benchmarking evaluation for this dimension involves advancements in user experience, integration of emerging technologies (e.g., AI, blockchain), agility in adopting advancements, and the ability to meet evolving customer needs and expectations.
• Customer Support and Engagement: A well-functioning digital banking platform enables seamless transactions and prioritizes customer support and engagement. The digital banking benchmark analysis assess the efficiency and responsiveness of customer support systems, including the availability of multiple support channels, response times, issue resolution rates, and personalization. On the personalization front, specific features like spend analysis (which helps customers track and manage their expenses) and associated service recommendations (offering tailored suggestions based on a user’s financial behavior) enhance customer engagement.

Robosoft’s Approach to Digital Banking Benchmark Analysis for Technology

At the core of our approach to the technology benchmarking of digital banking platforms lies the meticulous definition of scope, gathering insights, and providing actionable recommendations. We collaborate closely with our clients to understand their goals and ensure the digital banking benchmark process aligns with their strategic vision. We delve deep into understanding the unique identity of digital banking platform, their customer base, and their expectations.

Our process for evaluating technology benchmarks entails gathering quantitative data via questionnaires and qualitative inputs from key technology stakeholders via stakeholder interviews. By utilizing this approach, we gather essential insights that serve as the bedrock of our digital banking benchmark analysis. We comprehensively analyze the technological facets and operational complexities to view the existing ecosystem against the industry’s best practices. This perspective allows us to provide valuable observations and benchmarking recommendations that empower our clients to make well-informed decisions.

For one of our clients with the requirement of technology benchmarking for digital banking platforms, our methodology involved these facets:

• Assessing Technology Stack for Scalability and Performance
• Architecture and Infrastructure Scalability Assessment
• Database Design Assessment
• Benchmarking Analysis for Security

Assessing Technology Stack for Scalability and Performance

We assessed the technology readiness of the existing IT applications to support the financial services businesses’ long-run scalability and ensure alignment of IT to business strategies. This involved benchmarking the current products or services (online banking, mobile digital banking, and mobile wallets) and the technology landscape against competitors in targeted geographies. Also, the assessment included determining the future roadmap in terms of the technology stack for the remittance application.

Remittance Application: Observations & Recommendations

Digital Banking Suite (Mobile Wallet, Online and Mobile Digital Banking): Observations & Recommendations

Benchmarking Analysis for Architecture and Infrastructure Scalability

At this stage, our digital banking benchmark analysis process involved a holistic assessment of the current architecture and infrastructure for remittance application, mobile digital wallets, and digital banking. Through this evaluation, we focused on understanding the performance benchmarks exhibited by these systems and applications, enabling us to identify potential areas for enhancement and optimization.

Remittance Application: Observations & Recommendations

Digital Banking Suite (Mobile Wallet, Online and Mobile Digital Banking): Observations & Recommendations

Database Design Assessment

We conducted digital banking benchmark analysis to measure the database design within the technological framework for digital banking platforms. Our assessment involved the industry’s best practices by comprehensively exploring various facets that collectively define the structure and efficiency of the existing database design.

We assessed the key aspects such as indexing strategies, query optimization, archive database, partitioning of tables, and database design revamp suitable for microservices. We could gauge the database’s responsiveness, reliability, and overall performance by focusing on these components.

Remittance Application, Mobile Wallet, Online and Mobile Digital Banking: Observations & Recommendations

Benchmarking Assessment for Security

At this stage, our digital banking benchmark assessment for security aspects involved the evaluation of the vulnerabilities or risks of the applications with severity classification for each vulnerability. We conducted the digital banking benchmark analysis to examine the security aspects and pinpoint potential vulnerabilities that could be exploited in the form of attacks, breaches, or unauthorized access attempts.

In this process, we ensured the safety and security of the systems and applications. We utilized industry best practices to assess and fortify our systems against potential threats, upholding high security and safeguarding sensitive data, financial transactions, and user information.

Remittance Application, Mobile Wallet, Online and Mobile Digital Banking: Observations & Recommendations

Wrapping Up

The digital banking industry is constantly changing and characterized by disruption. To keep up, financial institutions need to implement the best practices for the technology benchmarking of digital banking platforms. This allows them to transcend conventional boundaries, innovate, and achieve excellence in digital banking.

Financial institutions can use digital banking benchmark analysis for technology enhancements of their digital banking platforms to create solutions that meet current customer expectations while also being resilient and adaptable to future disruptions. This approach ensures that the platforms are future-proof and can continue to provide quality service to their customers.

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.