Back in the analog days of loan underwriting, credit scores were the most significant borrower metric for lending decisions. While a borrower’s credit rating is still relevant, lenders are increasingly giving more weight to cash flow, which is often a better indicator of repayment risk.
Traditional credit scoring models often fail to include alternative data, such as cash flow, in providing an accurate picture of a borrower’s risk profile. Lenders have historically leaned heavily on credit scores because they expedited the due diligence process, but alternative data is becoming increasingly important. This is helping to level a playing field in which approvals were often skewed in favor of traditional borrowers with strong credit scores.
But now, document processing technology and machine learning analytics provide lenders with the tools they need to activate unstructured content to include cash flow data from borrowers in order to create more accurate financial profiles. Expanding the definition of creditworthiness ultimately means increased access to loans for business and consumer borrowers, and a larger pool of potentially qualified customers for financial institutions.
Automation and Cash Flow Analysis
Cash flow analysis is a beneficial tool for both business and consumer lending. According to a U.S. Office of the Comptroller of the Currency (OCC) report, cash flow is the primary means most small businesses will pay off loans. The OCC advises financial institutions to carefully assess a company’s current and projected cash flows when making loan decisions.
A report from FinRegLab, a non-profit think tank, noted that using cash flow data in underwriting also enables more data to inform consumer lending decisions. In particular, data from deposit and credit card accounts can provide a detailed and comprehensive picture of a borrower’s cash flow.
Recent technological and market developments are facilitating the ability of lenders to access and analyze cash flow information electronically. While the FinRegLab report noted that underwriting models that rely on detailed analyses of alternative data are still evolving, they are part of a trend toward more automated due diligence.
In particular, cash flow analysis is at the intersection of automated underwriting and new data-sharing protocols. New data and analytical techniques are driving the transformation of automated credit underwriting. What’s more, efforts to structure the new data transfer system – using APIs, for example – are empowering borrowers, spurring greater competition, and encouraging innovation in financial services markets.
Cash flow analysis is increasingly part of the overall assessment of a borrower’s financial wellness and ability to repay a loan. As the FinRegLab analysts pointed out, expanding underwriting criteria has the potential to close gaps in providing financial assistance to historically under-served markets.
In addition to better evaluating consumers and businesses who lack traditional credit history, the predictive power of cash flow could enable more applicants to qualify for loans. In particular, this could result in better access to credit for African American and Hispanic borrowers.
Beyond Mortgage Lending
Mortgage lenders are increasingly using cash flow analysis to expand their pool of prospective borrowers to include non-qualified mortgage applicants. Lenders in today’s marketplace understand that some customers are viable mortgage loan applicants – even if they don’t meet traditional underwriting criteria that rely heavily on credit reports.
Automation is making cash flow data more readily available to lenders than ever before. As a result, individual and business liquidity are being given more weight for risk assessments.
For consumers, cash flow analysis can lower the bar for obtaining everything from car loans to short-term lines of credit. When it comes to automotive loans, and other consumer lending, financial institutions need reliable data sources, real-time information, and more segmented and personalized assessment strategies. It is important to get a clear picture of a borrower’s track record. To do that, lenders need to ingest, synthesize, and analyze multiple sources of data in order to understand a customer’s cash flow and capacity to service debt.
For business borrowers, cash flow analysis is often used for cash advances, invoice financing, short-term loans, and business lines of credit. As a report on Financer.com noted, cash flow loans often eliminate the need for property or equipment to be used as collateral. Cash flow loans also can unlock up to 100% of the value for loans that are based on collateralized assets.
Ultimately, lenders need to make loans based on a combination of credit-related data and cash flow analytics. Automation can help lenders more quickly and accurately classify, capture, and analyze a borrower’s financial documents.
Moreover, by understanding the cash flow dynamics of a particular borrower, lenders can significantly reduce their credit risk. This process can be automated and streamlined using document capture technology that transforms unstructured information into clean, structured data. The ability to, for example, compare customer financial statements with bank data feeds is essential for enabling lenders to make sense of cash flow analytics from multiple sources.
Today, lenders can automate the loan review process using machine learning algorithms that can calculate cash flows and analyze personal financial statements. This facilitates risk management, determines credit exposure limits, and helps to assess a customer’s ability to pay. By using automated and informed risk models, financial institutions can expand their loan portfolios without increasing their exposure.