The Potential Of Machine Learning In Credit Risk Assessment

Credit default risk is simply the possibility of a lender incurring a loss as a result of a borrower's failure to repay a loan. Credit analysts are typically responsible for assessing this risk by thoroughly analyzing a borrower's ability to repay a loan — but the days of credit analysts are long gone; welcome to the age of machine learning! Because of their unparalleled predictive power and speed, machine learning algorithms have a lot to offer the world of credit risk assessment. In this article, we will use machine learning to predict whether or not a borrower will default on a loan and their probability of default.

 

What is Credit Risk? 

 

The possibility of a loss due to a borrower's failure to repay a loan or meet contractual obligations is referred to as credit risk. It traditionally refers to the risk that a lender will not receive the owed principal and interest, resulting in a disruption in cash flows and increased collection costs. Excess cash flows can be written to provide additional credit risk protection. When a lender faces increased credit risk, it can be mitigated by offering a higher coupon rate, which results in higher cash flows.

 

Although it is impossible to predict who will default on obligations, properly assessing and managing credit risk can help to mitigate the severity of a loss. A lender or creditor receives interest payments from the borrower or issuer of a debt obligation.

 

Understanding the concept of credit risk assessment necessitates a thorough understanding of credit risk. So, exactly what is credit risk? Credit risk is simply the risk of borrowers failing to meet their debt obligations. It happens when borrowers fail to meet their contractual debt obligations and are unable to repay the borrowed funds within a specified time frame.

 

Credit risk can result in significant losses for money lenders such as banks and other financial institutions that lend funds to borrowers at a fixed interest rate. Credit risk can disrupt the lending party's cash flow. It occurs when money is not repaid within the agreed-upon time frame. Lenders' risks and losses include lost interest and principal.

 

Credit risk is classified into three types: credit default risk, concentration risk, and sovereign risk. Concentration risk occurs when lenders focus on providing funds to a single sector or industry. There is a high credit risk in the form of capital concentration in the event of an economic downturn in these specific sectors or industries. Sovereign or country risk refers to credit risks that arise as a result of unique circumstances in a given country or state.

 

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What is Credit Risk Assessment?

 

Assessing a borrower's credit risk is critical to the lending institution's overall profitability. A proper credit risk assessment process helps to weed out defaulters and only provide loans to those who fall within the lending party's desired credit risk limit. It enables organizations to determine whether a borrower will be able to repay his loan within the timeframe specified.

 

A borrower's credit risk profile is determined by several factors. These elements include collateral or security, repayment capacity, credit history, capital requirements, and loan criteria. These important factors are used to assess the suitability of loans and the associated credit risk.

 

Credit risk quantifies the likelihood of loss when lending money to a borrower. Credit risk assessment is a complicated process with many variables at play. The ever-changing market condition adds to the uncertainty of return and must be considered when lending out funds. Credit risk modeling is a useful tool for predicting the likelihood of loss based on historical data from a large number of borrowers. When conducting a credit risk assessment of a borrower, the 3C method is quite popular. 

 

Let's take a quick look at these crucial aspects of the credit risk assessment process:

 

1. Credit History: The credit history component evaluates the borrower based on his previous borrowing history. It assesses the borrower's ability and intent to repay the loan based on his financial history.

 

2. Collateral: Good collateral serves as a safety net for the lender and can expedite the loan process if a borrower offers to pledge collateral in exchange for money. If the borrower fails to repay the loan, the lender's losses can be recovered by selling the collateral pledged.

 

3. Cash flow: It is a measure of the borrower's ability to repay the loan amount. If the borrower has a consistent and sufficient cash flow, they can easily repay the loan amount. 

 

Credit risk assessment entails estimating the likelihood of loss due to a borrower's failure to repay a loan or debt. It traditionally refers to the risk that the lender will not receive the principal and interest.

 

The CRA has a significant influence on interest rates. The higher the perceived credit risk, the higher the interest rates on capital. If the risks are too high, creditors/banks may decline loan applications. In a nutshell, borrowers with higher credit ratings receive lower interest rates.

 

Two of India's most well-known credit rating agencies are CRISIL (Credit Rating Information Services of India Limited) and ICRA Limited.

 

Also Read | Credit Risk: Types, Assessment, and Reduction

 

Machine Learning and Credit Risk Assessment

 

AI and machine learning technology could have numerous applications in the BFSI sector, with risk management at the top of the list. The number of organizations using AI more than doubled between 2017 and 2018, with 40% of financial services firms using it to manage risk. This is because AI and machine learning could add genuine value throughout the credit value chain, from initial underwriting to risk measurement and analysis to determine the final maximum exposure. The following are some of the key use cases that would be addressed:

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Machine Learning in Credit Risk Assessment

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1. Risk assessment for individual customers:

 

For example, the vast majority of applicants in any borrower group will be non-defaulters, with only a small number of defaulters. As a result, if banks use traditional analytical models, a sample of bad customers entering the credit dataset could cause imbalance and skew results. As a result of the degradation in performance caused by this gap, the predictive insights are inaccurate, and the bank misses out on viable business opportunities.

 

To determine whether a specific customer should be offered a loan, an ML model, such as the Artificial Neural Network, would create discrete clusters of datasets and apply merging methodologies. Rather than simply looking at mean values, ML creates majority and minority clusters and merges them to form a single cluster.

 

2. Credit Risk Analysis Could Be Reshaped by Machine Learning Techniques:

 

AI and machine learning provides a significant advantage over traditional statistical models. Rather than defining a set of rigid instructions to arrive at a specific insight, ML can adapt and "learn" intuitively. The ML model is fed data continuously, is trained to extract insights, and then draws predictive insights on new datasets. This is a cyclical process, which means that the ML improves with each round of credit analysis

 

Brief about Supervised and Unsupervised Learning

 

Supervised learning is a machine learning technique that uses labeled data to train models. The goal is to identify the mapping function that connects the input variable (X) to the output variable (Y) (Y). Y = f(x) (x)

 

The term "supervised" refers to the fact that the algorithms used are not left alone to reduce the relationship between X and Y. Instead, the machine is trained using already labeled data. It's similar to giving the machine some questions that have already been tagged with correct answers and then asking it to find answers to untagged but similar questions.

 

1. Machine Learning Regression:

 

Using training data, regression machine learning predicts a single output (dependent variable) value (independent variables). We can, for example, use regression to model loan repayment risk using a variety of explanatory variables such as average nonpayment rates, employment status, credit history, and other outstanding liabilities.

 

One advantage of machine learning regression over traditional regression is that it allows us to include a larger number of independent variables that can be automatically discarded if they lack explanatory power. LASSO regression, for example, eliminates variables with zero regression power, whereas Ridge regression assigns lower weights to variables in a model that are highly correlated with other variables in a model. We can also start with zero power for all variables and gradually add the variables that have been found to have explanatory power.

 

2. Classification:

 

Data are classified by grouping them into labeled classes. When modeling the likelihood of default, for example, we could have two categories: potential defaulters and non-defaulters. The model would then be trained on how to accurately classify the data into one of the two classes. In binary classification, the model uses only two labels: 0 and 1. (yes and no). The model classifies data into more than one class in the case of multi-class classification.

 

Models are not supervised or trained using labeled data in unsupervised learning. Models, on the other hand, find hidden patterns and insights in given data without the need for human intervention. The goal is to discover previously unknown patterns and the internal structure of a data set with no predefined output categories. When compared to supervised learning, unsupervised learning methods are used to perform more complex processing tasks. A bank, for example, could develop an algorithm to examine customer accounts and identify those with similarities. This could assist the bank in developing a product that is specifically aimed at those account holders.

 

3. Clustering:

 

Clustering is primarily concerned with identifying a natural structure or pattern in a collection of unclassified raw data. Unsupervised Learning Clustering algorithms search the data for notable clusters (groups). Clustering is used in the detection of spam emails, for example. If an email looks like other spam emails, it is likely to be spam as well.

 

The desired number of clusters, k, is predetermined in k-means clustering. The algorithm is then tasked with iterative clustering the data into the k groups. A higher k indicates smaller groupings with more granularity, whereas a lower k indicates larger groupings with less granularity. The goal of iteration is to maximize the difference in means between determined groups. Each group or cluster has a unique centroid (central focal point). If we have two clusters, A and B, and a data point Y is closer to the centroid (mean) of A than to the centroid (mean) of B, then Y is assigned to cluster A.

 

4. Reduced Dimensionality:

 

Dimensionality reduction is used to assess and improve data representation. At the end of the process, the data set should have less redundant information, but the important parts may be highlighted. In exercise, this technique is used to isolate a subset of a large amount of data for closer examination.

 

Deep learning and neural networks, in addition to supervised and unsupervised machine learning techniques, are other branches of machine learning that can be supervised, unsupervised, or semi-supervised. The two are used to model extremely complex relationships between variables, with the goal of better mimicking human decision-making.

 

Also Read | 7 Types of Cost Functions in Machine Learning

 

Conclusion

 

Machine learning and artificial intelligence are poised to transform the banking industry by leveraging massive amounts of data to create models that improve decision-making, tailor services, and risk management. According to the McKinsey Global Institute, this could add more than $250 billion to the banking industry's value.

 

However, there is a drawback because machine-learning models amplify some aspects of model risk. And, while many banks have validation frameworks and practices in place to assess and mitigate the risks associated with traditional models, these are frequently insufficient to deal with the risks associated with machine-learning models.

 

Recognizing the issue, many banks are treading carefully, limiting the use of machine-learning models to low-risk applications such as digital marketing. Given the potential financial, reputational, and regulatory risks, their apprehension is understandable. 

 

Banks, for example, could find themselves in violation of anti-discrimination laws and face significant fines, prompting one bank to prohibit its HR department from using a machine-learning résumé screener. However, improving model-risk management is a better approach, and ultimately the only sustainable one if banks are to reap the full benefits of machine-learning models.