The Role of AI in Enhancing Credit Scoring Models for Loan Approvals

With a shift towards digitalisation, Artificial Intelligence (AI) plays a transformative role in reshaping how loan approvals are conducted. Conventional credit scoring models that count heavily on a borrower’s past credit history, salary slips, and employment status are rapidly being improved and, in some cases, replaced by AI-powered credit scoring systems.

AI leverages vast datasets and complex algorithms to evaluate creditworthiness more accurately, fairly, and in real time. Let's discuss how these algorithms work and their relevance to borrowers today.

How Does an AI Model Work for Credit Scoring Models?

Here is how AI works in the context of personal or business credit scoring models.

Step 1: Data Collection

The process starts with AI collecting data from both traditional and non-traditional sources. Traditional data includes your income, number of ongoing loans, repayment behaviour, and other related details. On the other hand, non-traditional data covers information on utility bill payments, rent history, frequency of digital wallet usage, and sometimes even your social media activity.

Step 2: Data Cleaning and Preparation

Next, the system cleans all the data by removing or imputing missing values, standardising formats (like dates or currency), and converting categorical variables into numerical form. For example, during the data collection, the model may show your earnings as ‘income to expense ratio,’ which is fixed in this stage.

Also Read : What is a Credit Score - Meaning, Importants and its Calculation

Step 3: Feature Selection

After the data is cleaned, the system selects the most useful parts of the data, which are called 'features.' For example, it records the frequency of late bill payments, your dependency on credit cards, and the number of times you have applied for a new loan within a particular period.

The primary purpose of this step is to help AI understand whether you are likely to repay your loan or not.

Step 4: Model Training

The AI model is trained based on the most relevant information available at this stage. The model analyses the repayment patterns of those who closed their loan on or before the binding due date and those who defaulted. For example, based on its training, it can identify a list of borrowers who frequently use over 60% of their available credit card limit.

Step 5: Model Testing and Validation

Before deploying AI in credit scoring, the model is tested by providing it with a new set of data that it has not seen before, to understand whether it gives correct results. For example, if it predicts that an XYZ applicant will repay but they actually default, then the model is adjusted.

Step 6: Scoring New Applicants

Once the AI model is ready and tested, it evaluates new loan applicants. When you apply for a loan, the system processes your data through the trained AI model and generates a score. This score indicates how likely you are to repay the loan.

Also Read : 6 Credit Score Myths That Most Borrowers Believe

Relevance of AI in Credit Scoring Model

Here is how AI can contribute to the assessment of loan applications.

Reduced Human Biased

Suppose you and your friend have applied for a loan. You run a small business while your friend is employed in a multinational company. In a traditional system, the lender’s executive might favour a salaried employee, assuming they receive a regular salary on the exact date of each month, while you have fluctuating income. The result? You are approved at a higher rate of interest.

However, integrating AI into the credit scoring system does not favour certain backgrounds, jobs, or locations. Instead, it looks at your data, such as how regularly you pay bills or use your credit, without judging your identity.

AI uses patterns from large amounts of data to make fairer decisions.

Also Read : What is a Good Credit Score and 5 Tips to Maintain It

Real Time Monitoring

AI can monitor borrower behaviour and portfolio trends in real-time by using data from various sources like bank transactions, loan repayments, mobile apps, and credit reports. It analyses this data using simple algorithms to spot payment habits, spending patterns, or financial stress changes.

For example, if you, as a borrower, suddenly miss payments or your spending increases sharply, AI can flag it as a risk. Similarly, AI reviews the overall loan portfolio and checks which groups of borrowers are likely to default. It updates this information regularly to help lenders take early action.

Fraud Detection

Consider a person applying for a personal loan using a fake identity. Traditional systems might only check basic documents, but an AI model goes further. It cross-checks the applicant's data, like address, job history, income details, and device information against trusted sources and previous records.

It raises a red flag if the system detects mismatches, such as the same PAN number being used across multiple identities or inconsistent employment history. It can also spot subtle signs, like unusual application timings or IP addresses linked to earlier fraudulent cases. As a result, the loan is flagged for manual review before approval.

Also Read : How to Improve Your Credit Score

Conclusion

An AI-powered credit scoring model is revolutionising the loan approval process by offering faster, fairer, and more accurate assessments. By analysing both traditional and alternative data, these intelligent systems reduce human bias, detect fraud, and enable real-time monitoring. As digital lending evolves, AI stands at the forefront to ensure more inclusive access to credit.

If you are looking for a lender that offers quick personal loan approval using an AI credit scoring model, turn to Hero FinCorp. You can get a loan ranging from ₹50,000 to ₹5 lakhs at an interest rate of 19%, with the added benefit of a flexible repayment tenure to suit your financial needs.

(Note: The interest rates and loan terms mentioned are approximate and may vary at the time of application.)

Frequently Asked Questions

1. What data does AI use to assess credit risk?

AI assesses credit risk using traditional data like credit history, income, and repayment records, along with alternative data such as utility bills, mobile usage, online behaviour, and social media activity.

2. What are the limitations of AI in credit scoring?

AI in credit scoring is not without its downsides. These may include data bias, lack of transparency, and over-reliance on historical patterns. If the input data is flawed, the results may be unfair. Also, AI decisions can be hard to explain, making it difficult for borrowers to understand the reasons behind rejection.

3. Can AI predict creditworthiness better than traditional methods?

Yes, AI can often predict creditworthiness better than traditional methods. The reason is that AI analyses large volumes of structured and unstructured data. It identifies hidden patterns and trends that human analysts might miss.

4. Can AI detect fraudulent loan applications?

Yes, AI can detect fraudulent loan applications by analysing large datasets to spot unusual patterns, mismatched information, or suspicious behaviour.

5. How do lenders benefit from AI-powered credit scoring?

Lenders gain quicker decision-making, reduced risk of defaults, early fraud detection, and the ability to serve a broader customer base through AI-powered insights.

Disclaimer: The information provided in this blog post is intended for informational purposes only. The content is based on research and opinions available at the time of writing. While we strive to ensure accuracy, we do not claim to be exhaustive or definitive. Readers are advised to independently verify any details mentioned here, such as specifications, features, and availability, before making any decisions. Hero FinCorp does not take responsibility for any discrepancies, inaccuracies, or changes that may occur after the publication of this blog. The choice to rely on the information presented herein is at the reader’s discretion, and we recommend consulting official sources and experts for the most up-to-date and accurate information about the featured products.