Traditional credit data still largely excludes credit invisibles, underserved individuals, and new borrowers—all of whom need access to financial products now more than ever. Moreover, since traditional credit data is difficult to extract, financial institutions have historically struggled to extend credit data to the unbanked. So, what about the underbanked and people who do not have financial records or bank accounts to prove their creditworthiness?

Solution : Alternative Credit Scoring 

To provide credit access to a wider audience and achieve financial inclusion, loaning institutions must consider a different approach to confirm a loan applicant’s creditworthiness.  This is where alternative credit data comes in.

Alternative data, now mainstream in credit risk analysis

There are an estimated 3 billion adults worldwide who do not have credit and so do not have credit records. And while many of these people are in developing markets with nascent credit infrastructures who have no credit and are unknown to the credit bureaus. Opening that market is a priority for lenders. 

Understanding the creditworthiness of an applicant has turned out to be more than just their credit score. The alternative data system, keeping up with the digital age, has certainly proven to be more efficient in credit risk analysis, since it focuses on a borrower’s behavior and can bring up data points that the traditional methods might have glossed over. An added benefit of using alternative data in risk appraisal is the increased levels of accuracy, compared to the traditional way of credit scoring.   

Major challenges traditional lenders face in their credit risk assessment:

1. Low coverage of credit bureaus: The lack of a bureau record forces the lender to reject the applications of more than 40% of the borrowers

2. High operating cost: Traditional lenders have an operating cost 3X higher than digital lenders, as they must manually perform all the verifications

3. Challenges in sourcing manual data from informal economy sector: The rise in technology and the use of alternate data in the underwriting process has made traditional lending practices obsolete. The era of basing decisions solely on credit scores from bureaus are over. Today custom models work better and more accurately since they use data from a number of sources both internal and external to assess creditworthiness. Such data could include supplier information, account data, customer relationship or other market data. More the data the more accurate and efficient the scoring model becomes.

Should a person with a thin file or no credit history get automatically rejected?

Certainly not. Alternative sources of data are bringing a paradigm shift in the way lending and borrowing is taking place. To provide credit access to a wider audience and achieve financial inclusion, lenders must consider alternative data to confirm a borrower’s creditworthiness.

The rise of alternative credit scoring has led to the creation of broad-based credit scoring methods. Unlike credit scoring mechanisms, this is effective for people who are not exposed to any credit and financing history or ecosystem. This is extremely crucial in a lending environment where a growing number of customers are credit invisible, and the competition for new customers is fierce.

Alternate credit scoring is a financial approach that optimizes numerous technologies such as ML(Machine Learning), AI-based models to gauge various tangents such as the loan applicant’s payment history, overall bank balance, e-commerce transactions, travel range, and expenditure blueprint. 

Boost acceptance rates and lower credit losses

While traditional credit bureau reports are also crucial, ML can go further with scoring models helping them add insights and provide newer business points of view. Lenders can gain access to a more comprehensive understanding of the credit risk associated with the consumer.

• Improved assessment of credit worthiness:  Many lenders reject loan application individuals with a low credit score. With alternative data scoring, they get a better and more comprehensive view of the applicant’s creditworthiness.

• Enhanced customer experience: By using alternative data for credit scoring, lenders can reduce loan origination costs which they can pass on to borrowers in the form of lower interest rates.

• Increased market reach: Financial inclusion can be achieved through alternative credit scoring, which creates a niche for lenders who are willing to extend their products to specific target groups, particularly those who are unbanked and individuals with bank accounts but no credit history.

When it comes to successful loan processing, alternative credit scoring model can offer adequate validation to lenders regarding the creditworthiness, good intent, and capacity of an applicant towards successfully repaying his/her loan.

Contact Us to know how Insight Consultants can help you use alternative data in credit risk modelling.