ChatGPT, a ground-breaking generative AI, has captivated the world with its extraordinary capabilities. Recent developments and a surge of announcements from tech giants like Google, AWS, and Microsoft regarding their own versions of generative AI software indicate that this field is rapidly gaining momentum. Through extensive training on vast datasets, these software systems consistently deliver impressive and highly relevant results. While occasional doubts may arise regarding the accuracy of generative AI, it undeniably presents immense potential for lucrative business prospects.
The lending industry is currently experiencing a significant transformation as banks and financial service providers embrace digital technologies to revolutionise their lending operations. Today, consumers can conveniently apply for loans from the comfort of their homes at any time of the day and receive disbursements within a matter of hours. With changing consumer expectations, rapid technological advancements, and modern regulatory environments, the lending landscape has evolved dramatically compared to a decade ago. In this context, the potential impact of generative AI, such as ChatGPT, on lending operations is tremendous, offering a wide range of possibilities.
By leveraging behavioural, transactional, and locational data, generative AI has the potential to play a crucial role in optimising lending operations. Few of the use cases of generative AI in lending operations are:
- Customer onboarding: In the current era of online banking, the absence of a sales representative during the loan application process poses a significant challenge in guiding consumers to choose the right loan product. Generative AI can fill this gap by effectively acting as a virtual sales representative during the loan onboarding process. Based on the borrower’s submitted information, Generative AI can swiftly present a range of loan options for the borrower to choose from. Whether benefiting the lender or the customer, Generative AI offers numerous advantages, including a seamless borrowing experience, expedited onboarding, and an accelerated customer acquisition process.
- Credit scoring: Generative AI can be trained on credit data to generate a scoring model that predicts the likelihood of a borrower defaulting on a loan. This enables lenders to make more accurate lending decisions and reduce default rates.
- Fraud detection: Generative AI can be utilised to detect fraudulent loan applications. By training on data from previous fraudulent applications, the AI can identify patterns and anomalies that suggest fraudulent activity.
- Loan approval: Generative AI can automate the loan approval process. By analysing a borrower’s financial history and credit rating, the AI can recommend a loan amount and interest rate that are appropriate for their financial situation.
- Loan servicing: Generative AI can enhance the loan servicing experience by providing flexibility and personalisation. Through analysis of customer data and interactions, the AI can make personalised recommendations and offer tailored advice regarding EMI payments, helping borrowers navigate unforeseen financial exigencies.
- Delinquency management: Generative AI can improve collections efficiency and reduce the cost of collections for lending organisations. By analysing real-time data to identify predictive features and patterns of loan defaults, the software can help lenders track borrowers who may be moving towards delinquency.
- Cross-sell and up-sell: Generative AI can derive intelligence from large datasets to predict cross-sell and up-sell opportunities. Over a customer’s lifetime, they may require loans to address diverse needs such as mortgage, education, auto, personal, medical, and more. Lenders can leverage generative AI to proactively identify cross-sell and up-sell opportunities and present favourable loan offers to customers.
Financial institutions like DBS Bank, Southeast Asia’s largest bank, have invested in leveraging AI for their banking operations. By focusing on becoming an AI-fueled bank, DBS Bank has positioned AI at the centre of its operations and enabled AI-driven processes across the organisation.
When lenders start implementing generative AI within their organisations, the opportunity to leverage such software with a curated knowledge base, like systems of records, becomes immense. The potential to transform business processes and even introduce new business models is within reach. Given this ongoing AI revolution, banks and financial institutions should ask themselves whether their existing systems and technological environments are modular and adaptable enough to embrace the ever-changing world of AI and unlock business value.
