Cash flow analysis is the process lenders use to determine if a borrower can repay a loan. A prospective loan applicant’s income, expenses, and debt obligations are key factors in this risk evaluation. Lenders use cash flow analysis to calculate a debt-to-income (DTI) ratio. This formula divides a loan applicant’s total monthly debt obligations by their gross monthly income.
The acceptable DTI ratio varies by lender and type of loan. Fannie Mae, for example, has a 36% DTI threshold for mortgage loans. But that limit can reach 50% if a borrower meets Fannie Mae’s credit score and reserve requirements.
This guide will examine why cash flow analysis is important for lenders, the benefits of cash flow analysis in lending, the types of documents that can be used to evaluate cash flow, and what types of cash flow analysis solutions are available to lenders.
What is cash flow analysis?
Financial institutions use cash flow analysis to evaluate the flow of money into and out of a borrower’s accounts. This includes data related to financing, investing, and day-to-day operations. Lenders typically rely on cash flow analysis software to analyze a loan applicant’s income, expenses, transaction categories, DTI ratios, and trends. Cash flow analytics help financial institutions manage risk and make informed lending decisions.
Cash flow-based financing enables businesses and individuals to borrow money based on their projected future cash flows. Lending decisions reflect the financial institution’s confidence in the borrower’s ability to make payments throughout the loan term.
Mortgage, personal, and small business financing decisions are typically based on future cash flow projections, with whatever is being purchased also serving as collateral. By contrast, larger business loans often rely on asset-based lending that is secured by the liquidation value of a company’s assets.
Why is cash flow analysis important?
Cash flow analysis gives lenders a more accurate tool for assessing risk than simply looking at traditional underwriting criteria, such as a borrower’s credit history, employment status, and savings. The combination of a loan applicant’s DTI and credit history helps to facilitate lending decisions that minimize the risk of default.
Cash flow analysis can be used to determine creditworthiness in cases where borrowers have higher than average DTI ratios, self-employment income, and/or short credit histories. For example, many gig economy workers have strong personal balance sheets. Yet, they don’t meet the requirements for a qualified mortgage (QM). Cash flow analysis helps non-QM lenders fully assess a customer’s financial condition.
According to the non-profit think tank FinRegLab, cash-flow data can expand the market for lenders. In fact, using cash flow for underwriting has the potential to impact lending decisions for more than 45 million U.S. consumers and 30 million small businesses.
Cash flow analysis is a beneficial tool for lenders because it provides data related to the ability of consumers and small businesses to make loan payments. Lenders use cash flow analysis to get a detailed picture of a customer’s financial health. Automation software streamlines the integration of different variables — from revenue and expenses to debt and transaction types — into the decision-making process.
Cash flow lending also enables companies to borrow money based on their projected future cash flows. The result is that a company can secure a loan based on anticipated future revenues.
What are the benefits of cash flow analysis in lending?
Cash flow analysis is an essential component of credit risk modeling. By using automation and machine learning to create a comprehensive profile of loan applicants, lenders can more accurately assess creditworthiness and default risks.
Four of the most important benefits of using cash flow data and risk modeling for lending decisions are:
- Reduced credit risk
- Standardized data analysis
- Lower customer acquisition costs
- More comprehensive credit-risk profiles
Reduced credit risk
Cash flow analysis data provides more detailed insight into a borrower’s repayment ability than traditional credit data. Detailed information about bank transactions, for example, yields an accurate picture of business or consumer cash flow — reducing the credit risk for lenders.
Standardized data analysis
Standardizing cash flow analysis enables lenders to assess a prospective borrower through a unified lens that aggregates past performance, current financial health, and future projections.
Lower customer acquisition costs
Cash flow lending analysis software lowers customer acquisition costs for lenders by automating the initial qualification process. Financial institutions can test potential marketing channels and quickly process loan applications. Automation also reduces application processing costs, making it more cost-effective for lenders to use an omni channel marketing approach.
More comprehensive credit-risk profiles
While a borrower’s credit rating remains an important part of loan underwriting, lenders are increasingly giving more weight to cash flow data — because it is often a better indicator of repayment risk.
What document types and analytics can be used to evaluate cash flow?
A wide range of cash flow data and document types can be processed using intelligent document automation software. These include bank statements, pay stubs, tax forms, and credit reports.
Bank statement analysis
Bank statement analysis is an essential part of the loan underwriting process. Intelligent document processing technology extracts and validates financial information automatically.
Software enables loan underwriters to accelerate the review of bank statements and verify borrower income. Advanced document capture technology facilitates the use of analytics based on real-time bank data feeds. For example, software can automatically parse data from PDF files, providing lenders with actionable insights about a borrower’s cash flow, including recurring transactions, income, and loan payments. Results can be indexed and processed quickly — often enabling decisions within minutes, rather than hours or days.
Paystub processing
Paystub data capture and form processing speed up the underwriting process and provide the information needed for smarter, faster lending decisions. Automation facilitates data retrieval from paystubs regardless of format or image quality. It also supports data-driven income assessments, expedited underwriting, and fraud detection.
Tax form analysis
Lenders can use intelligent automation to capture data from W2 forms and other tax documents. Automated classification and data extraction of tax forms enables faster, more accurate lending decisions. Tax documents can be classified, extracted, and analyzed in minutes with end-to-end intelligent document processing.
Credit reports
Obtaining credit scores — an essential metric for loan underwriting — is standard practice for lender Fannie Mae, which requires a credit report for each borrower on a loan application. A credit score is a mathematical algorithm that distills information about a borrower’s credit usage and repayment history into a single number. FICO, the most widely-used credit score, helps lenders to quantify loan applicants’ creditworthiness.
What cash flow analysis solutions are available to lenders?
Lenders often use spreadsheets for cash flow analysis. But increasingly, lenders prefer automation tools that include machine learning algorithms and the integration of APIs for data capture.
With cash flow analysis software, lenders can better understand the seasonality of cash flow, measure non-traditional income, assess debt capacity, and identify recurring transactions. Cash-flow-based risk models help lenders precisely predict the risk of default, increasing yields while expanding access to credit.
Automation reduces the time-consuming process of aggregating borrower data and facilitates the process of capturing and analyzing unstructured data. Robotic Process Automation (RPA), for example, saves time, decreases errors, scales easily, and reduces overhead — all of which results in cost savings and improved efficiency.
As McKinsey & Company noted in a recent report, automated credit decision-making is a cornerstone of digital transformation in the financial industry. Best practices include:
- Combining automated cash flow analysis with traditional credit-related data.
- Using RPA to quickly and accurately classify, capture, and analyze a borrower’s financial documents.
- Developing machine learning algorithms to calculate cash flows and analyze personal financial statements.
- Transforming unstructured information into structured data that can be analyzed using artificial intelligence and human auditors.
Ocrolus automation solutions for cash flow lending
Ocrolus uses advanced document capture technology as part of a comprehensive platform for evaluating cash flow and credit risk. Automatically extracting bank statement data into a structured format reduces the need for manual entry and validation.
AI-driven document automation streamlines a lender’s ability to identify file tampering, incomplete data sets, invalid data, or other anomalies in the information supplied by a loan applicant. Having clean, normalized bank statement data provides borrower-level insight to help reduce high-risk loans. It also enables financial institutions to aggregate data for insights into borrower cash flows and income trends.
Automaton software ultimately facilitates fast and accurate financial decisions. RPA and machine learning transform unstructured information into clean, structured data — enabling underwriters to manage risk by evaluating cash flow analytics from multiple sources.
Advantages of automated bank statement processing include:
- Retrieving data from bank statements regardless of format or image quality.
- Verifying bank statements and borrower income in minutes.
- Converting PDFs into Excel or other machine-readable formats.
- Developing robust, data-driven income assessments.
- Increasing originations with an expedited underwriting process.
- Identifying suspicious activity and potential fraud.
Automated cash flow analysis enables lenders to better understand risk and streamline the loan underwriting process. Automating manual tasks, streamlining income calculations, and improving fraud prevention also deliver benefits, including improved efficiency and reduced overhead.
Conclusion
Cash flow analysis is a beneficial tool for both business and consumer lending. Underwriting that relies heavily on credit scores favors traditional borrowers. But alternative data, such as cash flow, provides a more comprehensive assessment of a borrower’s risk profile. Cash flow analysis enables lenders to expand the definition of creditworthiness, translating into a larger potential market for business and consumer loans. Intelligent document processing and machine learning improve efficiency and reduce risk. That improves loan underwriting and, ultimately, provides a competitive advantage for lenders using proven automation solutions.
Book a free demo to find out how Ocrolus can help your company use automation to better assess borrower risk using cash flow analysis.
