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History of credit risk model

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68 08.10.2019
Before that, I spent 15 years in a research lab and another 25 years as an academic. Credit risk is generally defined as the risk from a borrower failing to meet its obligations to repay a loan according to agreed upon terms. Examples include primary and secondary mortgages, home equity line of credit, auto loans, business loans, student loans, credit cards, and so on. Therefore, it is critical that the banks model and quantify their exposure to risk.
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Credit Risk Definition

In this article we provide a fresh perspective on how one can improve credit risk assessment in lending to help reduce non-performing assets NPAs. We notice that there are some fundamental changes required in how one uses and builds credit scores to improve the overall risk assessment. We begin with explaining the distinction between credit scores as they exist today in most parts of the world and credit risk assessment estimates. We propose a simple yet extremely effective approach towards credit risk decision making via two quantitative signals, one that provides a view of the past and another which provides an estimate of the future. The delinking of the past and the future while may not be necessary mathematically for making a good decision, plays an important role in addressing the behavioural loopholes that persist. A credit score is traditionally expected to be indicative of whether a person is creditworthy or not, potentially to extend a new credit line in the future.
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Credit Scoring Model

It's not restricted to retail customers but includes small, medium and big corporate houses. In news, you might have heard of Kingfisher Company became non-performing asset NPA which means the company had not been able to pay dues. High NPAs lead to huge financial losses to the bank which turns to reduction of interest rate on the deposit into banks. Serious honest borrowers with good credit history credit score would have to suffer.
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Credit risk modeling is a major requirement for banks and businesses in the financial sector. With the emergence of technologies like artificial intelligence and machine learning in lending , the aftermath is mostly automated with reduced chances of defaults. There are numerous factors such as the financial health of the borrower and historical trends in default rates, etc.

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