Traditional lending has largely depended on periodic credit assessments, historical financial statements, fixed credit scores, and predefined underwriting criteria. Lenders evaluate borrowers from data gathered at a single point in time on income, payment history, outstanding debts, credit usage, and financial performance. Such assessments offer a reasonable basis for assessing lending risk, lending eligibility, and creditworthiness. However, currency situations are seldom static between assessments.

Borrowers experience changes in income, revenues, expenses, liquidity, and repayment capacity, while markets experience inflation, changes in interest rates, economic uncertainty, and industry disruption. Therefore, a credit assessment performed today in determining a borrower’s financial position may not be as precise a few months later.

This limitation is very telling, especially regarding banks’ use of outdated information about borrowers in their lending decisions. For example, a company with declining revenues may still enjoy a good credit rating based on its previous successful business operations. Similarly, a person with explosive earnings growth may still be receiving credit offers based on a prior evaluation. This periodic assessment may, therefore, result in risk assumptions made for the lending decisions that do not match the actual financial conditions. This type of traditional credit model is quite a useful tool, but its utility is becoming increasingly dependent upon the rate of refresh of financial data.

Adaptive Credit Networks are game-changers for lending: they dynamically recalibrate the risk rating of the borrower or portfolio as the climate changes. They see borrower risk as not a static qualification but as an evolving financial state that is influenced by borrower transactions and repayment behavior, cash-flow activities, economic events, and new potential risk signals.

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