Reimagining the Future of Banking with Generative AI
Banking sector, despite being highly regulated, has always embraced innovation with open arms. The digital payments revolution, meteoric rise of FinTech’s, open banking, and the growing interest in crypto and CBDCs is testament to the industry’s desire to reinvent and stay ahead of consumer needs. Now banking stands at the cusp of another revolution, one fueled by Generative AI, that can add a jaw-dropping $340 billion in annual value to the industry. The promise is huge, but can banks unlock this potential? A typical bank has over 1500 customer journeys. Which of these use cases stand to gain the most? Here are our top picks.
Four generative AI use cases that can deliver immediate business impact
1. Profitability with unparalleled customer experience
Consider Sam reaching out to the bank via their mobile app and complaining about a transaction fee. The traditional AI chatbot can only provide a standard response about the fee structure (and so does the service desk agent), eventually leading to a human assistant or abandonment. resulting in a broken client experience. If the bank deploys Generative AI, it can analyze Sam’s value to the bank and her transaction history and provide a dynamic solution like waiving the transaction fee or recommending a different account type based on the bank policies. This immediate personalized response with the best possible outcome for both Sam and the bank makes her feel valued, enhancing customer experience.
2. Bridging trust and access gaps in financial advisory
The banks’ advisory function is a high value service catering to customers who are generally in the high net worth group as the cost of building trust is high. With generative AI at play, the cost of educating all income groups is significantly lowered and expands the potential customer base.
Imagine Jane receives an annual bonus of $1,000 and asks the bank for tips to invest. The bank agent can typically only spend so much time listing feasible options. But Generative AI analyzes her past spending, financial goals, saving patterns, and economic conditions in seconds and provides Jane with a comprehensive strategy to invest her hard-earned bonus. It is like having a CFO personally assisting you.
3. Supercharge preparedness to navigate risk
News of operational risk events directly impacts the share value of a bank. Over the last six years, global banking faced 65000 operational risk events losing over $600 billion. Testing is a critical process that addresses this challenge. However, from data virtualization, obfuscation, and golden copy creation to synthetic data generation, test data management is a tedious and time-intensive process.
The current capability of banking technologies is limited to using historical data to assess risks limiting their ability to prepare for uncertainties that have never occurred. Generative AI, with its potential to create synthetic data, can simulate economic events like war, a rise in oil prices, and a pandemic occurring all at once. With this simulation of different outcomes, banks can identify vulnerabilities in their portfolios and take corrective measures.
4. Fraud defense beyond pattern recognition
Banks have come a long way in fraud detection, flagging suspicious transactions, and 2-factor authentications. And yet just last year, banks recorded a $20 billion loss in synthetic identity fraud. To keep up with the growing intelligence of fraudsters, banks need generative AI. The usual fraud detection flagging is limited to known patterns like high amounts and unusual locations. But Generative AI can learn to flag events even before they occur, like many small transactions occurring quickly, preventing these huge losses, thus enabling preventive fraud management
Opposites to opportunities: Balancing act of banking with Generative AI
Banking goals are a unique set of opposites. On one hand, they need to be fast, flexible, and provide better customer experiences. On the other hand, they need to carefully adhere to strict statutory and regulatory requirements. There are possibilities where Generative AI can make things worse instead of better.
Generative AI operates as a black box, which makes interpreting their decision-making process difficult. This lack of transparency justifiably creates trust issues among experts and customers. Generative AI can also drive-up market volatility by reacting too quickly to market fluctuations. Bad actors might gain unauthorized access to sensitive data. And then, there is evidence of unethical events – Generative AI based its decision on biased data and credit scored a customer low based on their race. Every technology brings a set of challenges in its wake. The question is, how prepared are you to face the challenges head-on and bring a new era of transformation yet again to the old-as-time banking industry?
