Banks and other lending institutions will often use credit underwriting to assess customer’s financial capability before approving or rejecting them for a loan or credit card. Although the practice itself isn’t too difficult to understand, there are many elements of credit underwriting that can be puzzling and sometimes even intimidating if one doesn’t know what to expect.
This article will give you some helpful insight into this practice and how it’s evolving in the age of automation.
What is credit underwriting or credit analysis?
Credit underwriting is simply a process of taking personal, financial, and/or business information, analyzing it for what it says about one’s ability to repay loans or other debts, and then accepting or rejecting that person for that purpose.
For lenders, evaluating an applicant’s creditworthiness is one of their most important and at the same challenging tasks. Before making a loan decision, they examine each borrower’s financial documents—typically, tax returns and financial statements—looking for information that will indicate whether or not they are likely to repay their debt. This process done in a systematic way is called credit analysis or credit underwriting. Some loans only require an in-depth review of basic data—such as income and current debt—while others require in-depth analysis of several years of data.
Banks and other financial institutions utilize credit reports generated through the manual examination of the customer’s financial data or hire an agency expert in underwriting or use an automated system to underwrite the loan processing.
Role of bank statement analysis in credit underwriting
A good way to get a feel for a person’s financial situation is by doing the Bank Statement Analysis and that is how credit underwriting starts.
For many consumers, their bank statement is a detailed accounting of their spending habits. A review of these statements can give us clues as to how financially responsible they are and whether or not they are trustworthy with money.
If we see regular but unexplained withdrawals or deposits, it could be indicative of someone who would struggle to pay back loans on time.
If we see frequent ATM withdrawals it may be an indication that they live paycheck-to-paycheck and likely cannot maintain large monthly payments like those on a mortgage.
These details might not seem important at first, but if they appear throughout multiple statements then it’s likely something that should come up in discussions during mortgage underwriting discussions and that is how the bank statement comes into consideration during the underwriting process.
Problems in the manual Credit underwriting process
The goal of a creditor is simple – lend money and recover it. For a lender, only one thing matters – risk. Whenever a lender extends a loan, he takes into account two factors: the probability that the customer will default on his obligations and the cost of covering that loss if it happens. This applies to both consumer lending (mortgages, auto loans, credit cards) and commercial lending (commercial real estate loans, corporate business lending). The way creditors assess their borrowers varies across different financial products but they all work in similar ways. Like any other business operations risk is at its lowest when operating in areas where there are many successful players who have proven demand for a particular product or service.
As we know now, creditworthiness is decided by crunching data based on a number of factors including your employment history, current income level, savings levels and more to create a picture of how likely someone is able to repay a loan while lending at minimal risk. That is where the real problem occurs, crunching financial data, typically it’s an examination of an exhaustive list of data and statements and doing it manually becomes cumbersome and proves to be time taking and costly. Therefore financial institutions and credit agency experts have become reliant on technology and let automation do the work.
Automating bank statement analysis and thereby credit underwriting
Credit scores can be generated through an automated system that uses algorithms to give each applicant a score. These systems can then be used for decisions, such as loans, credit card approvals or mortgage pre-approval. Financial data is particularly attractive for companies because it’s so detailed and rich with information, including both purchases and personal habits.
Bringing Automation in Financial assessment during the credit underwriting of a loan which otherwise is a manual process increases the turn-around-time (TAT) of Loan disbursal and is also prone to in-accurate assessment. With the help of Advanced Analytics combined with the power of Artificial intelligence (AI) and Machine learning (ML) to solve this problem hence can support large institutions with minimal operational support. Following a roadmap philosophy, including other documents as well, it develops a complete robust underwriting process.