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