Can Advanced Analytics for Credit Scoring Change the Lending Market?

Credit Scoring Change the Lending Market

Anyone who has ever applied for a loan or a credit card knows the importance of maintaining a good credit score.  Building a good consumer credit score involves a lot of financial planning and discipline and is a time-consuming process. In case you have no idea, a credit score is a three-digit number assigned by a credit bureau to every lendee, indicating their ability to repay a debt. In India, the credit score ranges from 300 to 900, where a score above 650 considered as good. This score helps the banks and lenders decide whether to pass you a loan and at what interest rate. A consumer credit score takes into account various factors such as total debt, payment history and the length of credit history.

Credit Score - A traditional approach towards loan lending

Risk is an integral part of the lending business. Naturally, the lenders have always tried various ways to minimize their risk. As such, they have traditionally relied on credit scores to minimize their credit risk while lending a loan. (Credit risk is the chance of a borrower defaulting on a debt by failing to make the required payments.) To predict the likelihood of a customer defaulting on a loan, lenders analyze the past data to guess the future behavior of the customer. But what if the customer does not have enough past data? This could include lower-income households and small scale enterprises or any first-time applier. As lower-income customers often have no access to formal financing, there is no record of past borrowing behavior. With such customers, lenders often struggle with little to none data that they might use to establish their creditworthiness. With traditional models, lenders won’t usually consider lending to people having scores less than 600. Today, it is estimated that around 2.5 billion people have unmet financial needs. Ensuring that these people and groups have access to affordable financial services, lenders must adopt newer ways of approaching this class.

Alternative Data as a solution

One way could be that instead of looking primarily at past behavior, lenders could look at how current behaviors are trending, and predict future behaviors based on these trends. Contrary to ‘traditional’ credit scoring, there can be an ‘alternate’ credit scoring model to ascertain the credit risk. The alternative data could be transactional data coming from mobile phones, interactions on social media platforms such as your comments, who you follow, what places you visited, etc.  Information can be gathered from other apps you use such as your frequency of using them, etc. This information may open up interesting insights into the lifestyle and behavior of individuals. Lenders can use the assistance of Machine Learning and Artificial Intelligence to calculate the creditworthiness based on current factors.

Leveraging the benefits of Machine Learning and Predictive Analytics

ML can be used to build new models and analyze the above data to determine which consumers are safe for lending money. CRIF’s Big Data Analytics is a platform that enables lenders to leverage data and predictive analytics models to make critical lending decisions and develop their own application scorecard, behavioral scorecard, and collection scorecard. The predictive models use the existing data to extract information and predict future outcomes. Advanced credit risk analytics is a key component and integrated part of many of CRIF’s offerings including the credit management platform products like StrategyOne and other services. These tools can help save costs and enable faster response to allow consistent credit risk management.