Streamlining Credit Risk Models with Machine Learning
Understanding credit risk is one of the oldest mathematical problems in the financial world. How is new technology making it easier?
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Introduction
Machine Learning has become indispensable for the finance industry. It addresses their demand for high-quality data insights and high-volume processing.
Financial applications, especially Credit Risk Modelling, have benefited significantly from the use of Machine Learning (and Artificial Intelligence, to a degree).
In this blog, I will explain how financial institutions leverage ML to improve their credit risk models and its effect on the outcome.
Let’s Understand Credit Risk Management
Credit risk is a term used in finance to describe the risk that results from borrowers (whether people or companies) not paying their debts on time.
In essence, creditors lose both the principal amount and interest on the debt. Due to the imbalanced cash flow, the creditor suffers from interruption in cash flow, inflated collection costs and loss of revenue.
As a result, financial institutions and banks want to reduce their credit risk by extending credit only to parties who can repay the principal amount plus interest. Makes sense, right?
This is where credit risk management comes in: it entails assessing and identifying potential clients that are susceptible to credit risk.
Machine Learning-based credit risk models are created to tackle this exact problem.
What Is Credit Risk Modelling?
At its core, credit risk modelling estimates the probability of someone paying back a loan. It allows creditors to determine the likelihood of borrowers defaulting, or the level of credit risk associated with a borrower.
Despite what the name suggests, credit risk modelling is not limited to loans involving credit cards — it can be used to refer to any loan.
Hence, credit risk models are deployed by a range of organizations; insurance companies, banks, investment companies, government treasuries, etc.
Essentially anywhere money is borrowed on credit.
Credit is what makes the world go round!
(One of the best explanations if you’re unaware of what I really mean.)
This all is to say that credit risk is everywhere and naturally, credit risk datasets are extremely complex, massive and dynamic.
Naturally, we need technologies that excel at analyzing and processing vast volumes of data with accuracy — enter Machine Learning!
How Does ML Benefit Credit Risk Modelling?
Of the two major prediction problems that Machine Learning deals with — regression and classification — credit risk modeling falls under the latter. A credit risk model classifies the potential borrowers into groups based on their predicted credit risk level (for example: low, medium or high risk).
Instead of traditional segmentation based on hard business metrics, financial institutions can group customers into smaller micro-segments using fine-tuned ML algorithms.
Precise credit risk assessments are made possible thanks to improved ML models (for instance, XGBoost, Light GBM, SVMs, Decision Trees and advanced Deep Learning algorithms).
(A Medium writer even built a credit risk model using some of the above-mentioned models — read here.)
This enables financial institutions to calculate primary metrics such as:
- Exposure At Default (EAD): the total value a bank is exposed to when a loan defaults.
- Probability of Default (PD): a percentage the likelihood of default
- Loss Given Default (LGD): the unrecoverable amount after selling underlying assets when a borrower defaults on a loan
Since these are precise metrics, calculated using the previous history of the defaulting client, as well as those of similar customers, the creditors have a better understanding of the expected losses.
Expected Loss: EAD x PD x LGD
This entire calculation can be offloaded to ML models, without compromising speed, accuracy or agility. This is why ML has become a core pillar for credit risk management.
Conclusion
In short, ML algorithms enable lenders to get meaningful insights on the expected losses in case of default, based on historical and current information. ML models can also integrate additional dimensions including the borrower’s credit payment and taxation history, or a business’s liquidity ratio and quarterly reports.
This helps banks and other creditors manage their loans to keep credit risk within their tolerance limits.
Credit risk models have become a necessity for financial businesses and creditors to reduce their exposure to credit risk. ML improves their ability to precisely predict the likelihood of credit risk for each account —eventually driving decisions such as whether a certain loan should be approved or not.
The dimensions of data involved with credit risk models is increasing to include complex factors such as social media profiling, location history, global events, etc.
ML-driven credit risk models are the only way to leverage these non-conventional data dimensions and safeguard your modern business against credit risk.
