Top 3 Loan Underwriting Challenges (And How to Solve Them)

Top 3 Loan Underwriting Challenges (And How to Solve Them)

January 15, 2019

As regulations in the industry evolve, underwriters play a crucial role in the loaning process. With the power to approve, delay, or deny a loan application, underwriters need to measure the risk associated with a certain borrower: one bad decision can lead to major losses. Hence, underwriters need to accurately verify the credit score of borrowers, evaluate their capacity of repayment, and ensure compliance with the required guidelines. Since there is a high risk of borrowers defaulting in loan repayment, several problems can arise during the loan underwriting process – some of which could delay or even derail the loan process. Let’s look at the top loan
underwriting challenges that could delay the loan from closing or even stop it from ever happening – and how you can solve them:

Challenge 1: Documents discrepancy

Document-related problems are often the most common challenge faced by underwriters. Since any loan approval process involves a variety of document types and formats, precise and accurate verification of documents is critical. However, most often, verification is done manually; this can not only slow the underwriting process but also increase the likelihood of error. Any discrepancy and the closing process could get delayed – sometimes indefinitely.

Solution

Immediate and accurate approval of loan requires financial institutions to verify all the submitted documents in a timely fashion – and in their entirety. From bank statements to income declarations, utility bills to driving licenses, structured to unstructured documents, printed to handwritten forms – an efficient way to overcome documents discrepancy is to use automation to digitize document verification, shorten the underwriting process, and reduce the chances of error. Through a combination of image processing, computer vision, and Natural Language Processing, digitization avoids underwriters to be constrained by specific templates and can process documents of varying quality for quick and efficient closure.

Challenge 2: Credit issues

Credit issues create major roadblocks for underwriters; although an application may appear to meet all lending requirements, it might unearth major issues while confirming the borrower’s credit history. Evaluating the credit score is a major requirement, essential to uncover a pattern of issues and assess the credit-worthiness of individuals. A history of late payments, too many lines of credit, and high balances can all delay the loan process – hence ensuring approvals for applicants who meet the minimum credit requirements is critical.

Solution

Modern AI technology with deep learning capabilities can evaluate a loan application based on a large set of variables and provide a likely outcome of the loan – free from any bias. Unlike traditional methods where lenders only looked at a few metrics such as income and credit score, and where the process was prone to underwriter bias, AI can determine the credit-worthiness of borrowers through detailed analysis of a data across their entire digital footprint – social media data, internet browsing data, mobile data and more – thereby enabling underwriters to make the right lending decisions and eliminate bias.

Challenge 3: Assessing the debt-to-income ratio

Having too much debt is a common problem that is likely to get past the broker or loan officer,but surfaces when the underwriter reviews the loan application file. Since borrowers need to have sufficient income and fewer debts for getting a loan approved, measuring debt-to-income ratio is imperative. However, comparing borrowers’ debt to overall income, as well as measuring their ability to manage monthly payment and repay debts is never easy.

Solution

Modern applications with built-in underwriting modules have the ability to evaluate loan applications based on a large set of variables and learn from actual outcomes of loans to provide a likely outcome. By building its own algorithm through experience, machine learning technology can eliminate bias, and enable underwriters to assess debt-to-income ratio in an accurate manner. When presented with hard facts, it can advise underwriters and auditors on the best decision to take, and identify loans that are more likely to default.

Enable quick closure

Underwriting, although a critical aspect of the loan process, is also a dreaded one. Since it involves the evaluation of a borrower’s ability to repay the loan – based on credit history, employment history, assets, debts, and other factors, several problems can arise during the process. This includes documents discrepancy, evaluating credit-worthiness and assessing debt-to-income ratio. However, the good news is, there are ways to avoid these problems. With the right expertise, tools, and guidelines, you can make highly accurate, low-risk decisions and ensure quick closure of loan applications.