Data Science

How Machine Learning is Revolutionizing Customer Credit Risk Management

Published: 12th April, 2023

Mahima Phalkey

Data Science Consultant at almaBetter

Have you ever wondered why SBI rejected your loan application? Have you ever considered that banks might use machine learning to forecast who they should lend money to and who shouldn't?

Have you ever wondered why SBI rejected your loan application? Have you ever considered that banks might use machine learning to forecast who they should lend money to and who shouldn't? Believe me, the answer to all your questions lies in the vast expanse of Machine Learning.

Let's deep dive into how SBI or any other bank works towards developing an ML model that can identify customer credit risk accurately and promptly to benefit the banks to give loans to only genuine customers.

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What is Customer Credit Risk, and How Can Machine Learning Help?

Customer credit risk worries businesses since it can significantly affect their earnings. Organizations can better recognize and manage credit risks related to clients as machine learning (ML) spreads.

Finding customers likely to default is a primary goal of utilizing machine learning to manage customer credit risks. A machine learning algorithm can analyze customer data, such as transaction histories and credit scores, to determine the risk of a default and to predict whether a customer is reliable. When lenders decide whether to approve or deny loan applications based on predicted trustworthiness, they can make more informed decisions. This can reduce the risk of default and increase the possibility of successful loan repayment, which is our prime objective.

Exploring Different Approaches Used in Credit Risk Management

Machine learning algorithms are increasingly used in this field, providing more accurate predictions than traditional methods. The two most used solutions which can be implemented for customer credit risk are:

  1. Prescriptive Analysis
  2. Predictive AnalysisFrame 23.png

Prescriptive Analysis:

Prescriptive analysis involves data and analytics to identify the best action to achieve a specific goal. In the context of customer credit risk, It can be used to determine the most effective measures to respond to predicted outcomes, such as approving or denying a loan application or setting the appropriate interest rate based on the borrower's risk level.

Predictive Analysis:

Making forecasts about upcoming events or results involves analyzing historical data and statistical techniques. It describes a possible future event to build models that guess a customer's chance of defaulting or not based on a variety of features, including credit score, income, debt-to-income ratio, payment history, and other criteria.

Some ways that prescriptive and predictive analytics can help with customer credit risks:

  1. Improved credit scoring: It can help in the finance domain to improve their credit scoring models by analyzing vast amounts of data on customer behavior, credit history, and other relevant factors, and if the credit score is improved, then there are fewer chances of the borrower being a defaulter.
  2. Better risk management: By detecting high-risk clients and suggesting suitable risk-reduction measures, financial institutions can be able to manage credit risk more skillfully.
  3. Faster decision-making: By automating many of the credit evaluation processes, it helps financial institutions make quicker decisions on credit applications.
  4. Customized credit offers: It also helps financial institutions to tailor their credit offers to individual customers based on their risk profile, credit history, and other factors, which is suitable for marketing.

We now understand what customer credit risk is, as well as prescriptive and predictive analysis. Let's put that into practice. To get a clear understanding, let's see the dataset. The dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005. In the dataset, we have 25 columns and 30000 rows in his dataset. Let's help him choose the features required for our prediction.

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To see the full implementation, you can check the following article "Implementation of Credit Risk Using ML".

Key Takeaways

  1. You now have a better understanding of how machine learning is applied in the customer credit risk and fintech sectors.
  2. How can we choose features for modeling that will produce good results?
  3. You must understand what predictive analysis and prescriptive analysis are.

Best Practices

  1. Data Quality: Ensure that your data is of good quality, comprehensive, accurate, and updated. The garbage in, garbage out principle applies in machine learning too.
  2. Feature Engineering: It is very important to choose the features that would increase the model's accuracy.
  3. Continuous Monitoring: We need to monitor continuously as the model's accuracy may decrease over time due to data changes, necessitating retraining or modifications.
  4. Explainability and Interpretability: Although machine learning models are frequently challenging to understand, you must ensure the model's output is clear to consumers. When a consumer questions the model's conclusion, the capacity to defend the model's reasoning is essential.

Interview Questions

1. What is SMOTE, and how does it work?

Answer: SMOTE, which stands for Synthetic Minority Over-sampling Technique, is a popular algorithm for dealing with a class imbalance in machine learning. It generates synthetic data points for the minority class by interpolating between existing minority class samples. This helps to balance the class distribution in the dataset, which can improve the performance of machine learning models.

SMOTE works by selecting a minority class sample and finding its k nearest neighbors in the feature space. It then generates synthetic data points along the line segments joining the minority class sample and its k nearest neighbors. A user-defined oversampling ratio determines the number of artificial data points generated.

In practice, SMOTE is often combined with other techniques, such as under-sampling the majority class or using a weighted loss function to improve the performance of machine learning models on imbalanced datasets.

2. Can you explain the difference between prescriptive and predictive analysis and provide an example?

Answer: Predictive analysis is a type of data analysis that uses statistical and machine learning algorithms to forecast future trends or events based on historical data. It involves identifying patterns and relationships within data to estimate what might happen in the future. For example, a business might use predictive analysis to forecast future sales or customer behavior based on past sales data and demographic information.

On the other hand, Prescriptive analysis takes the predictive analysis a step further by recommending an action to be taken to achieve a specific outcome. It predicts what might happen in the future and provides a recommended action to optimize the predicted outcome. For example, a supply chain management system might use prescriptive analysis to recommend the optimal shipping route and carrier for a given shipment, considering cost, time, and reliability factors.

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