It is no secret that the loan industry is a multi-billion dollar industry. Lenders make money by charging interest on loans, and borrowers want to get the best loan terms possible. In order to qualify for a loan, borrowers are typically required to provide information about their income, assets, and credit score. This process can be time consuming and frustrating for both lenders and borrowers. In this blog post, we will discuss how AI / machine learning can be used to predict loan eligibility. As data scientists, it is of great importance to understand some of challenges in relation to loan eligibility and how machine learning models can be built to address those challenges. We will provide examples of how machine learning can be used to improve the loan application process for both lenders and borrowers
What is loan eligibility and why is it important?
Loan eligibility is simply the process of determining whether or not a potential borrower meets the criteria for a particular loan. This can be based on factors such as credit score, employment history, and income level. Loan eligibility is important because it helps to ensure that borrowers are able to repay their loans. Default can lead to a variety of negative consequences, including damage to one’s credit score and difficulty obtaining future financing. As such, lenders take great care to assess loan eligibility before extending credit. By understanding the factors that lenders use to determine loan eligibility, borrowers can improve their chances of being approved for financing.
There are a variety of different methods and techniques that can be used to determine loan eligibility, and lenders will often use multiple methods in order to get a complete picture of the borrower. One common method is to pull a copy of the borrower’s credit report. This report will show the borrower’s current level of debt, as well as their payment history. Lenders will also look at public records in order to get an idea of the borrower’s financial history. In some cases, lenders may also require documentation such as tax returns or pay stubs in order to verify the borrower’s income.
To implement above methods, AI / machine learning and big data technologies can be used.
How can machine learning be used to predict loan eligibility?
We will look into some of the current challenges faced to determine loan eligibility and how AI / machine learning can be used to address those challenges. The following are some of the current challenges:
- Credit Scoring: One of the most important factors in loan eligibility is credit score. Credit scoring is a statistical method of assessing the credit risk of a potential or existing customer. A borrower’s credit score is a numerical representation of their creditworthiness. In order to get a accurate picture of the borrower’s creditworthiness, lenders will often look at multiple factors such as payment history, credit utilization, outstanding debt, length of credit history, credit mix, new credit inquiries, employment history, current financial status, etc. Payment history is the most heavily weighted factor in most credit scoring models, followed by credit utilization. Other factors such as length of credit history, credit mix, and new credit inquiries may also be considered in some models. However, manually assessing all of these factors can be time-consuming and expensive. Additionally, human lenders are often biased, which can lead to inaccurate decision-making. Machine learning can be used to automate the credit scoring process. Credit scoring models are mathematical algorithms that lenders use to forecasting an individual’s credit risk. Credit risk is the probability of suffering a loss due to a borrower’s non-payment of a loan. Credit scoring models are generally based on machine learning algorithms such as logistic regression, decision trees that analyze different features as listed before in order to predict their future behavior. The accuracy of these predictions is important in order to make sound lending decisions. There are many different types of credit scoring models, but the most common ones used by lenders are the FICO score and the VantageScore. Credit scoring models are typically developed using historical data from a large population of consumers. Feature selection and engineering are important steps in the development of a credit scoring model. Once a model is developed, it can be used to score new consumers in real-time using machine learning. This is not only more efficient than manual credit scoring, but it also leads to more accurate decisions. Credit scoring models are constantly being updated and refined as more data becomes available.
- Income Verification: Another important factor in loan eligibility is income. It is a process that lenders use to confirm that an applicant has the financial means to repay a loan. Lenders need to verify the borrower’s income in order to assess their ability to repay the loan. This process can be time-consuming, as it typically requires reviewing tax returns or pay stubs. Additionally, borrowers may falsify their income in order to qualify for a loan. Also, income verification can be a challenge, particularly for those who are self-employed or have multiple sources of income. The information related to income is typically provided in the form of tax returns, pay stubs, or bank statements. And, it is really cumbersome and time taking for humans to do a great job given the volume of request for loan eligibility. This is where machine learning / AI comes to the rescue. Machine learning can be used to automate the income verification process. By using data from previous loan applications, tax returns, bank statements, machine learning models can learn to identify patterns that are predictive of loan default. These patterns can then be used to automatically verify the income of new loan applicants. Classification models can be used to classify whether the income got verified or move the application to exception workflow which can then be processed by the humans. This is not only more efficient than manual income verification, but it also leads to more accurate decisions.
- How much money to lend and at what terms: Another challenge related to loan eligibility is to determine how much money to lend and at what terms. This decision is typically made by looking at the borrower’s credit score and income. However, there are other factors that can be used to assess loan amount and terms. For example, the loan purpose (e.g., buying a car versus starting a business) can be used to determine loan amount. Additionally, the borrower’s employment history can be used to assess loan terms. Machine learning can be used to automate the decision-making process for loan amount and terms. By using data from previous loan applications, machine learning models can learn to identify patterns that are predictive of loan default. These patterns can then be used to automatically determine loan amount and terms for new loan applicants.
- Handling unstructured data: Another challenge to determine loan eligibility is that data is often unstructured. This can make it difficult to extract the information that is needed to assess loan eligibility. For example, a borrower’s credit history may be spread out across multiple sources, such as their credit report, public records, and tax returns. Machine learning can be used to automatically extract this information from unstructured data sources. By using data from previous loan applications, machine learning models can learn to identify patterns that are predictive of loan default. These patterns can then be used to automatically extract the information needed to assess the loan eligibility of new applicants.
In order to understand how machine learning can be used to predict loan eligibility, it is important to first understand what loan eligibility is and why it is important. The challenges to determine loan eligibility are vast and varied, but with the help of machine learning, lenders can get a better idea of who is likely to repay a loan. We hope this article has been helpful in explaining some of the basics around predicting loan eligibility. If you have any further questions, please don’t hesitate to reach out to us.
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I found it very helpful. However the differences are not too understandable for me