Fintech has become the latest industry buzzword. Today, Fintech is helping lenders to access and use the most robust and real-time financial data of their customers/borrowers.
How can Fintech and data streamline your small business loan origination process, boost the productivity of your loan team and improve your customer experience?
In the current small business lending industry, there are many credit assessment and loan origination applications and software that serve the requirements of banks, credit unions, and other financial institutions. Due to cut-throat competition in the lending industry, every financial institution is increasingly mindful of improving their practices in these areas to boost efficiency, speed up decision-making, and make their customers happier.
Why are so Many Small Business Lenders Struggling to Improve the Loan Origination Process?
Many small business owners use old-school (manual and paper-based) loan approval methods that seem outdated with a digitized world. Therefore, they have a very slow and complicated loan origination process that customers want, and an internal data management issue that generates more work for lenders.
Spreadsheets are one of the most common methods that are used in the manual loan underwriting process. Although spreadsheets are great tools, using them for underwriting can be challenging and time-consuming. Customers’ data entered into a spreadsheet is sometimes reentered directly into a lenders’ other core system, making typo errors and creating duplicate records of the same data. From storage, retrieval, credit decision, and review perspective, this method has serious flaws. Moreover, this outdated underwriting practice lacks consistency, relevance, and accuracy.
How Data and Technology Can Help Improve the Loan Origination Process?
The perfect blend of data and technology can allow lenders to streamline the disparate system, provide reliable, accurate, and real-time data to quicken the loan origination process.
Data Collection and Analysis
The first step in any loan decision is gathering the different financial information of a potential borrower. Today, it takes a lot of time and effort to complete this task. There is always a high possibility of inaccurate data being stored when information is contained in electronic and paper documents.
Technology can mitigate the inconsistency and delays of manually gathering various financial data points of borrowers. Today, many financial technology companies use predictive analytics modeling techniques, and AI and ML-based systems to collect, standardize, normalize and transport the most robust and real-time data through an API to lenders’ loan origination platform. When lenders become data-driven, lenders can automate the next step in the process, differentiating borrowers that are eligible for loans and borrowers that require more documentation.
Credit Analysis
Credit analysis is one of the most important things to offer the right loan to the right customer at the right time. In the past, lenders relied on historical data points such as FICO score and 5Cs to determine a borrower’s creditworthiness and reduce risk.
Since this historical data doesn’t contain items like income, expenses, cash flow, and anything about the business’ future ability to repay, historical data is not enough to determine the creditworthiness of borrowers.
Today, lenders can leverage state-of-the-art technologies to access and analyze borrower’s past, present, and future data to know their customers better. A financial data API allows lenders to see deep critical insights into a borrower’s financial health and determine how likely a business is to default on a loan than is to pay it back.
Today’s most advanced API enables lenders to extract their customers’ most valuable data from multiple systems such as accounting, banking, cash flow, and other financial data sources. Later, lenders can use this data to pre-screen, score the borrower and provide a perfect credit decision in a matter of minutes. Ultimately, it helps lenders to streamline loan application processes, choose the deserving borrower and provide fast and easy funds.
Credit Decisioning
Technology in the small business loan approval process is about collecting the most accurate, relevant, and precise customers’ data and transforming it into actionable insights to make a credit decision. Being able to automate your lending process from applying to approval, captures the benefits of accuracy, near real-time data, boost efficiency, and reduced decisioning times.
Read Also: 3 Ways Financial Technology (Fintech) is Reshaping the Lending Service Industry
After accessing your customers’ most valuable financial information, spreading the running the ratio analysis, performing some projected scenarios, and undertaking a risk rating, most financial institutions have a good idea of what their lending appetite looks like. By reviewing and analyzing the data, small business lenders can better assess the ability of a borrower to repay the loan amount and make data-informed lending decisions. Ultimately, better and automatic credit decisioning helps improve the loan underwriting process.
Data and technology are already transforming numerous industries worldwide, and the lending industry is no exception. To gain a competitive advantage, boost customer trust and loyalty, and become a trusted financial expert, lenders need to improve their loan underwriting process. Small business lending is transforming, adopting data and technology can help lenders automate the loan origination process, reduce credit risk, enhance customer relationships, improve customer experience and make smarter lending decisions.
Sources:
- https://www.data-axle.com/resources/blog/data-driven-strategies-to-accelerate-underwriting-and-claims-processes/
- https://belvo.com/blog/how-improve-underwriting-decisions-with-open-banking/
- https://blog.clik.ai/big-data-analytics-ready-to-play-a-bigger-role-in-loan-underwriting
- https://www.moodysanalytics.com/articles/2018/maximize-efficiency-how-automation-can-improve-your-loan-origination-process
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