The growth of the banking industry is directly proportional to its ability in managing credit risks. Every country has its own credit scoring analytics like CIBIL in India, FICO in the US, etc. This score decides whether the borrower is a defaulter or a non-defaulter. Banks provide credits to the borrower having low credit score by increasing the interest rate leveraging the customer’s need to their profits in order to reduce their credit risks. Thus, they balance their overall credit status. Now that banks are slowly recovering from the pandemic and adjusting to the new regime, it becomes highly essential that the credit scoring system needs to be accurate to avoid further credit risks.

Factors affecting the credit risk

The first step in analysing and calculating the credit score is to understand the factors that affect credit risk. There are many factors that affect the credit risk both with respect to the borrower and lender. Some of them are:

Probability of Default (POD/PD)

POD is the tendency that the borrower might fail to repay the debt. POD for individuals is calculated based on the debt-to-income ratio and credit score. POD for businesses is calculated based on stats like corporate bonds, etc. High POD will lead to high interest rates and high down payments.

Loss Given Default (LGD)

Consider this instance- two borrowers have a similar credit score and debt-to-income ratio. The first borrower takes a smaller loan while the latter takes a bigger loan. Here, the second borrower creates higher risk probability to the lender as compared to the first borrower.

This is because the amount lent to the second borrower is higher with respect to the first borrower and in case of default, the borrower with higher amount will cause higher loss to the lender compared to the other borrower. This is precisely LGD. There are no particular factors to calculate LGD and it is estimated by reviewing the portfolio of loans.

Exposure at Default (EAD)

EAD is the total loss exposure a lender is exposed to at any given point of time. It is based on outstanding balances that the lender receives before default.

Money Laundering

Some borrowers will have proper credit score and will be listed as non-defaulters. Still, there might be chances of credit risks as there are chances that such borrowers might have had false collaterals or capitals. In order to do that, proper Anti-Money Laundering (AML) regulations have to be followed.

Banking areas Vulnerable to Credit Risks

Some of the areas where credit risks are high include:

  1. Loan

Loan processing includes home loan, mortgages, small business, etc. Credit risks are assessed here based on five C’s: Credit History, Capacity to repay, Capital, loan Conditions, and associated Collateral (Source: https://www.investopedia.com/terms/c/creditrisk.asp.) Most lenders consider having an asset in case of failure to repay the amount.

  1. Insurance Policy

This includes Life Insurance Policy and Policies for goods and assets. In case of any disability or any loss of the goods, the insurance claim is made. This calls for credit risks as some fraudsters try to false claim their insurance policy.

  1. Credit Card

Based on the income of an individual or the net worth of any business, banks provide credit cards with maximum limit to withdraw. There is a time period provided to pay this money.

  1. Fiduciary Business

Fiduciary businesses like Investments, securitizations, etc., are trusted group to carry out operations which are in accordance with the contract made.

Why AI for Credit risk Management?

The traditional method of credit risk analysis is based on past experiences of banks with the borrowers. However, presently, there are different credit lending ways and increased number of applicants. To add to this is the recent pandemic and therefore, the manual job of predicting credit risk is insufficient with respect to accuracy and speed.

And that calls for the need to adopt an accurate credit scoring system which provides analytics based on data rather than experiences. As of 2017-18, the number of financial institutions using Artificial Intelligence is doubled and 40% of those institutions have applied AI to calculate credit risks (Source: https://cfo.economictimes.indiatimes.com/news/optimising-credit-risk-analysis-with-the-power-of-ai-machine-learning/72239074). Slowly, most enterprises are taking the assistance of AI for their lending services and this is because AI provides data-based credit scoring system with accurate decision-making ability.

Here are some advantages of having AI for Credit Scoring System:

  • Faster Credit decisions

From manually getting the customer’s data to verifying their profile, credit decisions are quite time-consuming. Implementing AI in this area can significantly reduce efforts and time. 

  • Increased Efficiency of the credit scoring system

One of the major reason to implement AI in credit risk management is its ability to provide early warning signals in case of any discrepancies in the overall credit system. 

  • Enhanced Customer experience

In order to increase the accuracy of the credit system and to improve overall customer experience, AI provides required customizations for the customer by analysing their data.

  • Efficient calculation of Creditworthiness of customer using real-time data

AI-based credit risk management system continuously monitors the data of the customers. This way, it can provide precise results of creditworthiness of a customer at any given point of time.

  • Meet all the compliances

It is a must for every bank to meet all the regulatory compliances that requires accurate and transparent data. With AI in Credit Scoring System, the data provided is accurate, real-time, and transparent.

How AI can help in Credit Scoring System

AI with the help of ML has different classification models, which provide the required credit risk analytics. Some of the classification models are:

  1. Support Vector Machine (SVM)

It uses the concept of Structural Risk Minimalization (SRM). By using the linear model in a high-dimensional space, SVM implements non-linear class boundaries. It calculates the margin hyper-plane in high-dimensional space which can classify the decision classes to the possible extent. Using this principle, SVM helps to analyze credit risks.

  1. Decision Tree (DT)

Decision Tree has a root with leaf nodes and internal nodes. A leaf node will not have any edge while the internal node will have at least one. Each internal node will have a predicting label called the splitting attribute. To find the predicted value (credit risk,) one should start from the root and follow the internal nodes until one finds the leaf node. 

  1. Neural Networks (NN)

Neural Networks are massively parallel processors, which simulate human brain to collect the observed evidence and store the knowledge. It consists of three layers: the input layer, hidden layer, and output later cumulatively called MLP (Multilayer Perception.)

  1. Metaheuristic Algorithm (MA)

Metaheuristic Algorithm is an automated process which finds the nearest optimized solution for a problem. It consists of several categories and calculates based on Genetic Algorithm (GA), Genetic Programming (GP), Ant Colony Optimization (ACO), Simulated Annealing (SA), etc.

Although, there are several such algorithms, which help analyze the credit risk, it is difficult to select one among them as the best for credit decisions as it depends on the scenario. For each credit risk scenario, one needs to compare the algorithm with factors like accuracy, misclassification rate, and computational time and then apply the best suitable algorithm. Therefore, decision-making becomes a fundamental aspect in getting an accurate credit score.

 

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Aishwarya Iyyengar

Aishwarya was once a Quality Assurance Engineer then moved to Oracle SQL Development. But her love for writing shifted her to the world of Content Marketing and she hasn’t looked back since then. Her interest ranges from food topics to trending technologies. She loves reading books and her favourite genre is Mythological Fiction. Apart from that, she is a South Indian filter coffee lover.