Banks/NBFCs (Lenders) and other traditional lending financial institutions find themselves operating in times where it is all about how well they know their customers, and how they can use this knowledge to grow their lending business. Customers’ expectations on the other side of the business are akin to what they might experience when they deal with e-commerce companies such as Amazon. Anything short of this experience and you can see customers gravitating toward the competition. Then there are Digital Natives FinTechs that have new ways of reaching out to customers and understanding them better. This is leading to reduced market share for traditional lenders (banks/NBFCs). Now, we hear Google is all set to enter the lending space. To sum up, the lending space is undergoing a massive transformation and lenders need to think of ways and means to adapt to this transformation.
Embracing Digital Transformation driven by digital technologies can help lenders grow their loan book and acquire more customers. Specifically, under the larger umbrella of digital technologies, Artificial Intelligence (AI) is the differentiator. AI can unearth and learn customer-behaviour patterns that help lenders differentiate themselves from the competition. Let’s look at a couple of high-impact areas that AI can influence significantly in terms of transformation, and help lenders improve their loan books.
Customer Acquisition – AI DRIVEN
Customer buying journeys for lending products have changed dramatically over the years. With customers being online 24/7, they leave a lot of behavioural footprint on digital properties of lenders or their affiliates. AI can help lenders understand this customer behaviour and predict possible business outcomes of this behaviour to lenders. This includes predictions such as whether a customer really intends to purchase a lending product.
What AI does here is model the intent of customer purchase by utilizing the clickstream data, search data, and other such data. Based on the AI model outcome, customers can be bucketed into ‘must reach’, ‘requires more effort’ and ‘not interested’ categories. There can be more categories per business requirements. Based on this categorization of customers/prospects, lenders can reach out to them in a targeted manner, and at a very early stage of the sales funnel engagement.
AI can also help when it comes to the ‘Next Best Offer’ or ‘Next Best Action’, similar to Precision Marketing, where lenders can reach out to customers in a targeted manner through personalized products to nudge the customers to complete the purchase of lending products.
CREDIT SCORING – AI DRIVEN
Customer credit scoring/eligibility for a loan is a major challenge plus a critical business differentiator for lenders. Credit scoring is a major function that decides the customer’s eligibility for a loan, and also is a vital factor to drive the loan book for lenders. Also, this process is currently either manual or rules-driven depending on the lender’s maturity. Additionally, the process is very bank branch driven, where credit managers in branches take a call on the credit. In a nutshell, credit scoring is both an operational and a business driver for lenders. AI can disrupt this space and help banks to grow their loan book. How? AI can enable an ALTERNATE CREDIT MECHASISM for lenders.
AI can use 300-400 data points related to customer behaviour, financial history, income tax history, and other transactions, to derive a credit risk score for a customer. Lenders have been using financial and other data in the past for credit. However, with AI, they can utilize more data points on customer behavior and that gives them a key differentiator.
What are these data points? These include customer behavior on the digital property of a lender or its affiliates, customer social profile – what friends do customers have; to whom is the customer connected on social media across Facebook, LinkedIn, or any other social medium; number of hours of usage of a mobile phone, mobile phone type, mobile call details, mobile battery patterns, mobile apps, educational qualifications, wallet usage data, payments usage data, etc.
AI models can ingest this structured & unstructured data and model this data with an output as a credit score. This credit score can be predicted in real-time or offline, as lenders choose.
With this credit score, lenders can reach out to existing customers in a bid to sell a pre-approved loan product or reach out to new prospects. This helps lenders send out personalized messages to customers /prospects, and in turn, grow their loan book. Such a credit mechanism also reduces risk and brings standardization in issuing loans across branches /offices of lenders.
Lenders are facing a multitude of challenges in growing their loan book. We feel the above-mentioned initiatives will help lenders grow their loan book, while also giving them that competitive edge.