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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.
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.
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:
Prescriptive Analysis
Predictive 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.
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.