For banks receiving thousands of loan applications on an everyday basis, coming from a mixed pool of customers with different geographic, demographic, and professional backgrounds, ascertaining the loan worthy applications simply based on their FICO scores and certain other related factors could be quite a task. This task is most certainly targeted towards underwriters, who use their tried and tested methods to review if the loan request should be accepted or not. However, there are two issues with this model, which although did not hinder how banks worked and doled out loans earlier, but most definitely matter a lot today when banks are operating in competitive lending markets. These issues would be:
• Missing out on a substantial portion of the market due to incorrect fair lending analysis
• Missing out on a substantial portion of the market due to unfair pricing and incorrect decisions
It is here where a statistical tool like regression analysis can play a huge role in helping banks identify opportunities where they otherwise wouldn’t see any. If you are a lending institution who would like to churn out your data sets to see patterns that could help improve your loan origination numbers, pricing, and the number of returning customers, then a multivariate regression analysis tool is just what you need.
Statistics can be an overwhelming idea when there are no tools involved. However, the insights that a statistical study can provide you with can be instrumental to the decision making and strategizing aspects of your business, especially when it holds key to an entire market which you might have otherwise not addressed.
A multivariate regression analysis can help in considering a number of variables in your fair lending or loan origination decision making routine. In addition to, comparing FICO scores and a few other factors readily available from third party sources, underwriters can use regression analysis to:
• Explain credit sanctioning decisions and find out if prohibitive factoring was used to ascertain the success rate of a loan and its pricing.
• Find creditworthy applicants from the protected class section.
• Find out if the right lending rate, Annual Percentage Rate (APR) was applied to applications and the percentage variation from the tool’s expected pricing or lending rate.
• Find out why certain applications were denied or were priced higher or lower than the tool’s predictions.
Financial institutions and banks that receive more than a thousand loan applications deal in high volume transactions and complex lending will benefit the most from regression analysis tools provided by fintech companies, these days. The larger the data set, the more accurate the insight is as the disparities identified in larger datasets can be filtered through multiple variables thereby bringing out the actual reasons that may cause the disparities.
Banks or lending institutions that already use certain factors for redlining or approving loans, such as demographic, financial, racial, etc. will not benefit much from regression analysis, no matter how large their data set is. However, the same banks, when they see disparities which are not occurring due to their predetermined factors but because of some other non-identified factor can also turn to regression analysis to get to the root of the matter.
Regression analysis is considered important by the regulators who need to keep a tab on the pricing, availability of loans to individuals and SMBs, general demand and supply, create policies, accordingly. At this point, institutions that are being regulated would do well by following regression analysis to price correctly, investigate outliers, service markets which are underserved and stay well within compliance to ensure steady and smooth growth.
Multivariate regression analysis lets you run a large data set through multiple variables thus giving you a non-skewed fair evaluation and judgment to base your pricing and decision on. It makes the complex and resource consuming task of underwriting easier and more reliable by arming underwriters with a tool to ensure fair lending and loan origination. Disparities found in manual underwriting might not always be due to discriminations; apart from keeping room for errors, each underwriter might have their own method and understanding of which loans to originate and which applications to discard. Regression analysis helps underwriters, substantially, by running the numbers through the tool and the variables to help them remove biases and apply the right pricing to a certain loan than they otherwise would.
Regression analysis tools can be used in various loan origination data sets, for example, applications for car loans, credit cards, and consumer or personal loans. The lending industry sees a huge influx of applications for the above-mentioned loans. A regression analysis tool can be a great resource to help sort the loans to approve and the APR they should charge.
The data required to run a proper regression analysis for predicting successful loan applications mainly depends on the goal of the loan applicant and the type of loan being analyzed. In general, in order to find out both underwriting along with loan pricing, you will require the following data:
The type of regression analysis conducted will depend on the type of your institution, your risk appetite, and the market you are servicing. For example, if you are a bank whose auto lending arm is conducting a regression analysis, then you will be conducting either interest rate analysis or decision analysis. In both cases, you will require:
In the era of customer-centric banking, financial institutions often extend offers and promotions based on their relationship with a certain customer, or customers whose profile have undergone individual reviews. While running a regression analysis, you can factor in these considerations to help you decide on and price loans efficiently and correctly.
Regression analysis goes a long way in bringing down disparities and helping banks and financial institutions gain relevant and helpful insights from their applicant pool and available data sets to reduce risk, reduce cost of underwriting, price more efficiently, be in compliance, and last but not the least, help expand their reach by targeting and reaching out to potential customers who would otherwise have been outliers.