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For all the innovation in digital credit underwriting, credit reports continue to carry significant weightage in determining the creditworthiness of borrowers. They are created after carefully considering a range of factors like a borrower’s repayment history, credit utilization ratio, number of credit accounts, the age of active credit lines and the number of credit inquiries made about the borrower.
Bureau scores are extremely reliable indicators of creditworthiness built after a comprehensive inspection of the borrower’s credit behavior. It is small wonder that they have become the global industry standard in lending. However, despite all their merits, bureau-generated credit scores by themselves aren’t the sole determinants of creditworthiness.
Need for holistic financial profile analysis
Traditional credit scores, as we’ve established, are built based on insights drawn from a borrower’s credit repayment activity. Their larger financial activity – financial transactions, income, average balances, expenses, etc – are usually left outside the purview of bureaus. Lending institutions have long relied on bank statements fetched from the borrowers directly to close this gap in information by obtaining granular information about their financial behavior.
With the advent of digital lending, bank statement analysis has been digitized and automated. This, paired with bureau data and alternate data insights, has enriched transaction analysis with improved accuracy and speed.
At FinBox, we used bank statement related insights to further enhance underwriting strategy by overlaying our own proprietary score - the FinBox BankConnect Score (FBCS) – on top of traditional credit scores.
How to make credit scoring less rigid
Bureau scores are at the heart of lenders’ most critical business rules. For instance, most lenders grant loans only to customers with credit scores higher than 750. However, such segmentation binaries are extremely rigid. As a result, they may exclude customers shy of 750, but whose bank statements (with their detailed and variegated financial behavior insights) can testify to their ability to repay the loan.
Using intelligence gained from bank statement analysis, we created the FinBox BankConnect Score (FBCS) – a proprietary scoring system that takes insights absent from the scope of credit bureaus. Here’s a list of some of the insights that went into building the FBCS:
Trends of EOD balances of over 6 months
Minimum and maximum balances, duration of minimum balance and inactivity
Monthly credit trends with recurrence and stability
Expense categorisation and average debits by channels and category
Bounce transactions and other risky behavior - cheque and NACH
Bank charges and classification
These insights have been molded by our ML algorithms into a credit score that scales like any traditional bureau scores. This uniformity of format allows us to overlay our proprietary FBCS on top of the bureau score in order to break the myopic yes/no decision making process.
Here’s how -
By itself, bureau score-based decisioning is one-dimensional and looks something like this:
However, once lenders superimpose the FBCS on top of the bureau score, another dimension is added. Now, in addition to purely credit-related insights, lenders can enrich their underwriting intelligence with the FinBox BankConnect Score:
FBCS adds a degree of healthy complexity to risk assessment. Judging creditworthiness based on diverse data pulled from bank statements, in addition to the credit hygiene-focused intelligence of bureau scores, allows lenders to identify good borrowers on the unfavorable side of the business rule.
Consider this table of credit scores drawn from a partner’s portfolio:
Here, by adding FBCS to bureau scores, the lender can segment the entire user base across a gradient, instead of binary categorization. For instance, a bureau assigns a borrower a score between 724 and 740, denying them credit if the business rule mentioned above still holds (<750). But the borrower’s FBCS is higher, say between 760 and 779. In such a case, they should ideally be considered for a loan.
Extend loans to NTC segments with the power of FBCS
Many borrowers are stuck in a vicious cycle that bars them from accessing credit time and again. They have no history of access to credit, which means there is no footprint of their repayment behavior. This, in turn, means that bureaus can’t create a credit score for them. Hence, they are ineligible for a loan.
FBCS, however, scans the applicant’s financial profile in more detail for patterns that can help establish him as a reliable borrower. Thus, breaking the cycle and allowing this new-to-credit borrower to get institutional credit for the first time. In doing so, it also gives him the opportunity to build a good credit report for future loans.
FinBox BankConnect, with its scoring functionality, can help lenders increase the size of their loan books by 10-20% (by admitting while also helping build quality loan books – by admitting . It identifies customers overlooked by rigid business rules and allows them to enter the formal financial fold.
For lenders, growth of loan books doesn’t just mean increased business, but also more diversification. Such diversified loan portfolios are better protected against delinquencies and defaults. Moreover, it allows lenders to align themselves more closely with their financial inclusion imperatives by giving more new-to-credit borrowers access to institutional credit.
Better risk-based pricing
FBCS adds more depth to each credit profile, which allows the lender to not only secure more customers, but also make better decisions around pricing risk for every customer. There are a number of methods that can be used to charge individual borrowers interest corresponding with their risk profile, through dynamic risk-based pricing, creating direct impact to profitability per customer and improving offer take-up rate.
For instance, borrowers can be segmented into buckets based on a combination of their FBCS and bureau score by allowing lenders to reevaluate their business rule thresholds, and proportionately adjust interest rates for each individual borrower with more granularity.
On the one hand FBCS opens up lenders’ loan books to more customers. On the other, it acts as an additional guardrail in risk prediction. FBCS is also well-equipped to deliver relevant insights that can inform pricing decisions, based on the various levels of risk presented by individual borrowers. It also promises to fulfill lenders’ responsibilities beyond business decisions, for instance, by improving credit accessibility and contributing to the cause of financial inclusion.
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