Underwriting platforms utilizing artificial intelligence (AI) have seen a rise in popularity in recent years, as the technology offers several advantages over traditional underwriting methods. It reduces costs and speeds up the loan-origination process by eliminating human error which is why so many banks are turning to BankPoint. What an AI-powered underwriter system looks like in practice is described in this article, including what advantages it may have over currently available technologies.
What is Loan Underwriting?
When assessing a borrower’s likelihood of repaying a loan, there is more to their credit report than their credit score and credit history.
While there are different approaches to automating the underwriting process, all of them require the same thing: analyzing borrower data. All of the courses use the same data, but each analyzes it differently.
What is Automated Underwriting Software?
Loan officers use automated underwriting software to assign a score to applicants based on their income, assets, and credit history. It has two major sections:
- The process of determining whether a potential borrower is financially stable before making a loan.
- The borrower’s information on the initial mortgage application is verified using a secondary evaluation tool.
The secondary assessment tool calculates risk ratings based on information such as credit history and debt-to-income ratios when determining a borrower’s eligibility for a loan. By using this software, the lending institution can simplify the underwriting process for loans. Using the database, results can be returned to the lender, which may then assign an underwriter to review the borrower’s application for any red flags.
Different Types of Automated Underwriting Software
Below are a few examples of automated underwriting.
Propensity Score Modeling
Insurers use this software to calculate premiums. Various metrics are used to rank each user’s likelihood of filing a claim. Propensity scores are calculated by taking into account a person’s age, gender, driving record, occupation, and marital status. An event’s likelihood of occurring increases as the model estimates how likely it is that a loss will occur during the specified period. The premium increases as a result.
Loss Ratio Optimization
By calculating the premiums needed to cover expenses while cutting back on wasteful spending in other areas, such as billing and claims processing, this report format determines the insurer’s net income.
Risk Grading
Based on factors such as age, gender, and driving history, clients are divided into risk categories. A business may choose to provide a customer with certain goods or services based on their risk profile, set an insurance premium for them, or offer them certain discounts based on their risk profile.
In this stage, data such as credit scores, debt-to-income ratios, and income are entered into a computerized data entry system (EDRS). Some financial institutions, however, continue to use manual procedures rather than fully automated methods.
As a next step, the automated underwriter validates and tracks the information by comparing it with other borrower files.
As soon as all three conditions are met, the loan application is sent to underwriting. We double-check everything entered so far and make sure no crucial documents are missing.
Closing Words
Automated loan underwriting systems can significantly benefit the financial services industry. By reducing errors, they improve their credibility with clients. Additionally, by analyzing the provided data, the loan application can be completed in a matter of minutes, thereby reducing loan processing times.
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