In the process of taking a business loan, the business owners do a lot of research into the type of loan, interest rate, repayment period, and other terms. This research increases the chance of getting the maximum benefit from the loan. At the same time, the lenders also conduct research on the clients to make sure that these clients will be able to pay back the loan amount and interest on time. Otherwise, the lenders will be liable for the default, and their business will incur a loss. This research by the lenders is called underwriting. Now, with technological advancement, the lenders can do AI-powered underwriting. The AI business lending process saves time and performs an accurate evaluation of the customer.
The Process of Underwriting
Underwriting evaluates the borrowers’ financial position and credit history to make sure that they are eligible for the loan and have the capability to repay the loan. If you are seeking funding for expanding your business, purchasing new equipment, or supporting your working capital, then you must present some important documents to the lender for proper evaluation. Some of these are mentioned here:
♦️ Financial Statement
You must submit your financial statements, which include the income statement, balance sheet, and cash flow statements. These documents will give a good picture of your business’s financial performance. The lenders will analyze financial ratios to assess liquidity and other aspects of your business.
♦️ Business Plan
The lender has to know why you are applying for a business loan. It’s your responsibility to give a solid business plan to convince the lender that you really need the money for your business growth or survival. In the business plan, you must include the company’s goals, market analysis, and strategies.
♦️ Collateral
If you want to take a large loan, then you will need to provide collateral as your personal guarantee to repay the loan. Collateral can be in the form of real estate, equipment, or inventory. The lender will assess the collateral to find out whether it will cover the loan amount.
♦️ Business Stability
The longer you are in the business, the more your chances are of getting the loan. The lenders prefer businesses with a long history of stable financial condition. So, you must provide the necessary documents to show the stability of your business.
♦️ Debt Service Coverage Ratio (DSCR)
Lenders calculate the DSCR, which measures the business’s ability to cover its loan payments. It’s the ratio of the business’s net operating income to its debt obligations. A higher DSCR is considered to be better.
AI-Powered Underwriting by Fintech Lenders
The fintech lenders conduct business underwriting to find out the risks associated with lending a loan to a business. The risk assessment methods used in underwriting can vary among the lenders. In the past, manual underwriting processes were used to determine the borrowers’ financial condition.
As data became more complex, the underwriters started using AI-powered methods to evaluate their clients. This method of underwriting provides an in-depth analysis of the borrowers’ creditworthiness. Therefore, credit risk management improves, and the decision-making process becomes easier. This process also increases operational efficiency and gives the customers a positive experience. This method is faster and more accurate than the manual one.
AI business lending uses predictive models and real-time data for evaluation and applies automation to the decision process. AI-powered underwriting scans multiple documents for fraud detection and other assessments. It can handle thousands of applications simultaneously.
The AI-powered underwriting uses different machine learning models to evaluate lots of variables like spending behavior, geographic risk factors, and others to complete an accurate credit risk classification. The AI models have built-in rules that are aligned with the regulatory requirements. Thus, the process is transparent and supports third-party reviews.
How AI Business Lending Underwriting Works
The AI-powered underwriting is a multi-step process. Each process is carefully designed to ensure accurate and speedy processing. Here is a breakdown of the process.
🔶 Data Collection
The AI underwriting systems collect data from various verified sources such as bank statements, public reports like property ownership or tax filings, credit bureau reports, e-commerce activity, utility bill payments, and others. The system analyzes these documents to find out real-time income stability, credit worthiness, and spending behavior.
🔶 Data Cleaning
The amount of data collected is huge. These data are cleaned through various methods. OCR is used to convert scanned invoices and other financial documents into a machine-readable form. NLP is used to get data from emails. Validation checks are done to remove duplicates and fill in missing fields.
🔶 AI/ML Model Scoring
This model is used to predict loan repayment behavior. It includes a classification algorithm to designate low-risk or medium-risk lending depending on behavioral patterns. Regression model variables like late payment frequency, transaction errors, and debt-to-income ratio to ensure that the borrower is worthy of getting the loan; it provides a risk score.
🔶 Decision Logic
After getting the risk score, the AI-powered underwriting system applies predefined thresholds to approve or decline a loan application. Tools like LIME and SHAP quantify the input variables that resulted in the final decision. It shows irregular income patterns, usage of high credit, and other things to highlight what factors contributed to the decision making.
🔶 Integration With Loan Origination Systems (LOS)
The decisions are fed directly to the lender’s LOS to show the real-time status of the application. This allows full transparency to the process of AI business lending.
🔶 Large-Scale Lending
AI-powered underwriting can process thousands of applications every minute without compromising on compliance or accuracy. This process learns from real-time data and allows automatic adjustment of lending terms.
Conclusion
There is no doubt that AI improves the underwriting process. However, there are some challenges. For example, as AI models have to go through a large volume of data, there may be outdated data, conflicting records of missing data. There is concern for the protection of sensitive financial information, tax records, and credit history as well.
If these obstacles can be managed, then both lenders and clients will benefit from AI-powered underwriting. While the AI takes care of the routine evaluations, the underwriting teams can work on building up relationships with high-value clients or optimizing portfolio. This makes the entire underwriting system efficient and productive.
