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.
