How Banks Are Using AI to Tackle Traditional Barriers in Lending

In the last couple of decades, the banking sector has embarked upon a fundamental change in its approach to delivering services. They have transitioned from a model that puts their operational rigidity as the main focus area into one where the customer’s financial well-being is the priority. The explosive growth of self-service options across web and mobile channels is a testimonial to this shifting paradigm of banks. However, despite the advancements in their technology, banks still face a major hurdle when it comes to the lending experience customers face.

Lending is still a major physical activity

While banks have digitized their core lending activities from an operational perspective, the customer experience front still has major bottlenecks. Back-and-forth visits to the branch, heaps of documents, conversations with credit officers, etc. are some of the hassles that customers face repeatedly when they try to avail of loans. However, there are interesting developments in the lending landscape of banks thanks to the rapid growth of artificial intelligence (AI) in the banking sector.

 

Transforming the lending experience with AI

Customers often face hassles in the early stages of the lending cycle itself. From rejections without reasonable explanations to mistaken evaluation of documents, the pain points are several. The fundamental reason behind such experiences is the inability of officials to understand the intricacies of customer’s financial health, demands, and ability to repay the debt. This could be attributed to the poor analysis (or inferences) made from data that the bank has about a potential loan applicant.

This is where AI can make a huge difference.

AI can deep-dive into truckloads of information and insight about customers and build a more accurate lending persona for them. This is a win-win situation for both banks and customers.

Let us explore a few use cases where banks can use AI to tackle traditional barriers in lending:

·      Fair credit assessment

In the traditional loan cycle, the first check that a loan officer does before proceeding with an applicant is on their credit score. In this approach, however, a customer who is relatively young or new to the formal banking system is placed at a severe disadvantage. They may not have enough of previous lending history to facilitate a desirable credit score. But they may have the financial muscle to repay the loan easily.

With AI, banks can create an alternative credit assessment workflow that is tied to the realistic financial behavior of the customer irrespective of their credit score. AI systems can quickly process information like transactional history on the customer’s bank account, their pay slips, investment or saving habits, spending behavior, family tree, and much more. The AI system can build a highly reliable credit score that reflects the actual capacity of the applicant to repay the loan amount demanded.

·       Eliminate Bias

Adding to the previous problem, a human bank underwriter or loan officer may fall prey to biased judgments and intuitions while evaluating loan documents. As a result, a trustworthy candidate may get rejected owing to a missed judgment or the inability of the official to understand the intricacies of the context the person is in. The same situation can happen in the opposite direction as well when a risky loan application gets approved through biased decision-making.

With an AI-based loan assessment, however, there is no room for biased judgments. Every loan application would be scrutinized with clear data-driven judgments. Besides, the evolution of Generative AI can help in building systems that can recognize the true contextual scenarios behind loan documents and capture their essence. Applicants can be provided with detailed insights into why their loans are rejected so that they can make necessary corrections in the next attempt.

·       Personalize lending

As AI-based lending workflows derive their decisions from heaps of data about customers, they can easily personalize the lending experience. It can gauge the need for added assurances to protect either party. For example, a customer with a decent risk profile could be mandated by the system to opt for an insurance policy to be taken along with the loan.

On the other hand, a customer with very promising potential could be offered a better deal on interest. This will help in building long-term loyalty and reputation for the bank. It could be perceived as an integrated marketing strategy controlled purely by insights generated by AI. The best part is that both these use cases can be offered as a self-service option to customers. All interactions and engagements could be handled by an AI-powered chatbot that can engage with customers on the web, mobile, or even in their social media instant messaging services.

·       Banking the Unbanked

AI can bring about transformational change in how banks approach their core lending business. With the right technology and guidance, they can easily streamline and scale their lending business to accommodate far more customer demographics than ever before. The best part is that this can be done in a controlled risk-free manner thereby proving to be a major asset for the bank’s growth.

What banks need is a technology partner like Verinite to help them embrace innovations like AI in their technology stack. Get in touch with us to know more.