Inside the Bank’s Crystal Ball: The Art of Predicting Loan Approvals
Imagine you’re on the brink of a milestone, be it buying a car, pursuing higher education, or taking that leap into entrepreneurship. You’ve got the vision, the plan, and the determination. But to turn these aspirations into reality, you need that golden ticket — a loan approval.
Ever wondered how banks make that decision? It’s not merely a roll of the dice or a flip of a coin. Instead, it’s an intricate puzzle solved by a blend of number-crunching, technology, and a touch of foresight.
The world of loan approvals is no longer confined to stuffy offices and stacks of paperwork. It’s a dance between data and sophisticated algorithms. Think of it as the bank’s crystal ball, trying to foresee whether you’ll be that reliable borrower or just another risky bet.
In the age of smartphones and instant messages, banking has become more than just a trip to the local branch. Now, it’s a high-tech ballet, where data calls the shots and algorithms do the heavy lifting. You might think it’s just about your credit score, but there’s a whole lot more to this story.
Understanding the Dynamics of Loan Approval:
Credit history, income, and assets—the standard criteria for loan approval — have long served as the cornerstone of banking judgments. However, the terrain is changing. In order to evaluate an applicant’s creditworthiness, banks are now using advanced algorithms that take into account a wide range of factors, not just the traditional markers.
Data Preparation:
Dataset contains 14 columns and 5000 rows. Description of the columns are as follows:
- ID: Customer ID
- Age : Customer Age
- Experience : Customer Experience
- Income : Income of the Customer
- ZipCode: Customer’s residence zipcode
- Family : No of Family members of the customer
- CCAvg: Credit Card Average Score
- Education: Education of the customer
- Mortgage: Mortgage taken or not taken by the customer
- Personal Loan: 0 = No personal loan given , 1 = personal loan given
- Securities Account : Having or not having a Securities Account
- CD Account : Having or not having a CD Account
- Online : Having or not having online banking
- Credit Card : Having or not having a credit card
PredictEasy Analysis:
Using the Google Sheets add-on PredictEasy a classification model was built. In order to learn more about how to use the tool, please refer to my previous blog posts.
We start by putting every variable in X and the target variable in Y. After doing this, we see the summary:
The predictive model achieved an accuracy of 0.99, indicating that it correctly classified 99% of the instances.
The confusion matrix determines the performance of the classification model and shows us the errors while predicting:
We can see that only very few out of the majority of observations tested gave wrong results and the final model had an accuracy very well close to the initial model. Education, income, and family size are the most important factors influencing the decision to take a personal loan.
The ranking of these features is also shown here:
Based on the feature scores, the Loan approval depends on the following features:
Education:
Greater financial responsibility and financial literacy have been linked to higher educational attainment. Those with advanced degrees may be seen by lenders as more responsible with money, which could improve the likelihood of loan approval.
Education may be a sign of future income growth prospects as well as job path stability. Higher-educated people may have greater job opportunities and more consistent incomes, both of which are factors that lenders take into account when approving loans.
Income:
Repayment Capacity: A steady and sufficient income is crucial for loan approval. It demonstrates the borrower’s ability to repay the loan amount along with interest within the specified timeframe.
Debt-to-Income Ratio: Lenders assess the debt-to-income ratio, comparing the amount of debt an individual holds against their income. A lower ratio signifies a healthier financial situation, increasing the likelihood of loan approval.
Family Dynamics:
Financial Responsibilities: Disposable income may vary depending on the number of dependents and family responsibilities. When determining whether a borrower can afford to pay back a loan in addition to their current debts, lenders take these responsibilities into account.
Stability and Support: The stability of a family has an impact on the stability of finances. A solid family setting, for example, may imply both financial and emotional support, both of which might enhance a borrower’s creditworthiness.
Credit Card Average Score:
Evaluation of Credit Behavior: A person’s credit behavior, including payment patterns, credit usage, and general credit management, is reflected in their credit card scores. An elevated credit score is indicative of conscientious credit management, hence augmenting the likelihood of loan authorization.
Risk assessment: Credit card ratings are used by lenders to determine how risky it is to provide a loan. A low default risk is indicated by a high credit score, which may increase the chance of loan approval and favorable terms.
Conclusion:
In the world of loan approvals, education, income, family, and credit card scores act as essential players in the financial orchestra. Education signals financial understanding, income establishes stability, family ties reflect support, and credit scores paint a picture of reliability.
Like different musical notes, these elements combine to form a melody that lenders carefully listen to. The stronger the harmony between these factors—solid education, consistent income, strong family support, and a trustworthy credit history—the more likely the tune ends with a positive loan approval, paving the way for individuals to achieve their financial goals.
