How to integrate cash flow data into the lending process

Credit scores have long been the foundation of consumer lending, but they have limitations. Credit scores only reflect past borrowing behavior, not the applicant's current economic situation. Someone with a high score may have just lost their job, while a young person with a low score (or no score) may have sufficient income and strong financial habits. 

Additionally, 45 million Americans lack a credit score entirely, making it difficult for them to borrow money, open a credit card, or, in some cases, rent an apartment—despite having the ability to repay loans. 

To bridge this gap, lenders are now expanding the use of cash flow data as part of their lending and underwriting processes. This approach analyzes real-time income, spending habits, recurring expenses, and more to offer a transparent, more up-to-date view of a borrower’s financial stability. It enables lenders to make more accurate and inclusive lending decisions, expanding their customer bases while minimizing risk.

In this guide, we’ll cover why cash flow data matters, key metrics to track, and how to integrate it into your lending tech stack.

Why lenders should consider cash flow data

Traditional credit data leaves gaps in assessing borrower risk, particularly for those with thin or no credit files. Cash flow lending helps bridge this gap by offering a real-time view of the borrower's financial health.  

Lenders can more accurately assess an applicant's ability to repay by analyzing deposits, spending habits, income sources, recurring expenses, and more - factors that are not taken into account in traditional credit data. A consumer with a low credit score but a steady income and responsible financial habits may be a better lending candidate than their score suggests. Likewise, cash flow data can reveal early warning signs—such as declining balances or changes in income—that indicate potential repayment risks.

Here’s an example of how cash flow data could be used to assess financial soundness and ability to pay for applicants with different credit scores and risk profiles.

Leveraging cash flow data also reduces delinquencies and improves risk segmentation. Lenders can fine-tune approval criteria, adjust credit limits, and even offer more personalized loan products based on an applicant’s actual financial behavior rather than outdated or incomplete credit bureau data. 

However, this isn’t an either-or decision—cash flow data works best when combined with traditional credit scores. More data leads to better risk assessment, helping lenders make more informed underwriting decisions.

Key cash flow data points and metrics that predict repayment behavior

Credit scores generally offer just a few data points for lenders to consider. Cash flow data offers several key metrics that provide a nuanced view of income stability, spending behaviors, and financial obligations. 

Key cash flow data metrics include: 

• Income stability and variability: Regular paychecks from the same employer indicate reliability, while inconsistent or gig-based income may require deeper analysis.

• Recurring expenses and debt obligations: Fixed costs like rent, utilities, and loan payments show how much of an applicant’s income is already committed.

• Spending patterns and financial behavior: Frequent overdrafts, high discretionary spending, or too many loans may signal financial instability.

• Savings and liquidity:  A borrower with consistent savings habits and emergency funds is in a stronger financial position than one living paycheck to paycheck.

Incorporating these data points into underwriting models gives lenders a more accurate, real-time view of risk—helping them approve more good borrowers while minimizing defaults.

How to incorporate cash flow data into your lending tech stack

The lending landscape is evolving, but many financial institutions struggle to move beyond credit scores due to regulatory concerns and complex legacy systems. This section will cover how to successfully incorporate cash flow data into lending workflows in a step-by-step process. 

Step 1: Choose the right data source and provider 

Lenders can access cash flow data through several methods, including direct bank connections and third-party aggregators. Direct bank APIs provide the most reliable, real-time information but require significant technical resources to implement for thousands of institutions. Aggregation solutions offer access to multiple data sources and often require less technical knowledge for implementation. 

Plaid is a full-stack solution that streamlines data access by connecting to 12,000+ global financial institutions while reducing development work, maintaining data quality, and ensuring regulatory compliance. Additionally, Plaid provides secure and streamlined account linking experiences used by one in two US bank account holders.

Step 2: Review integration considerations and implement

Once lenders choose a data provider, the next challenge is integration with the current lending framework. Cash flow data must seamlessly connect with existing loan origination and decisioning systems, ensuring it enhances rather than disrupts the underwriting process.

There are two primary integration methods:

• API-based integration: Ideal for lenders seeking real-time data access, APIs provide direct, on-demand connections to financial institutions. This allows lenders to fetch cash flow insights instantly, but it can require development resources to implement and maintain.

• File-based transfers: For lenders with more rigid legacy systems, batch file processing offers an alternative. While not real-time, structured files (e.g., JSON, CSV) can still provide valuable cash flow insights to underwriting models at regular intervals.

Security and compliance are critical at this stage. Borrowers must explicitly consent to data sharing, and lenders must ensure compliance with the FCRA and data protection regulations like the GLBA (Gramm-Leach-Bliley Act).

Consumer Report (by Plaid Check, a consumer reporting agency) simplifies integration by delivering structured, categorized cash flow data directly into a lender’s underwriting system. Instead of analyzing massive amounts of raw transaction data, lenders can access pre-analyzed insights on income, recurring expenses, and spending patterns, reducing the complexity of data processing.

Step 3: Collect and incorporate cash flow data into decisioning models 

Once the implementation is completed, lenders will begin collecting consumer-permissioned cash flow data from their applicants. Lenders should determine how the cash flow data can be used alongside traditional credit data to fill in the gaps about an applicant’s financial stability. They can also consider the impact the new data will have on their risk models when assessing applicants. A thorough review is needed before approving and implementing changes to a lender’s underwriting criteria.

When ready, there are three steps to incorporating cash flow data: 

1. Consider hybrid underwriting models: Instead of replacing credit scores, lenders can combine them with cash flow insights to refine decisioning. For example, consistent income deposits may offset a thin credit file, helping more applicants qualify.

2. Adjust risk thresholds: Lenders can fine-tune approval criteria by setting different risk weights for cash flow variables, such as stable income trends or low discretionary spending. This enables more granular risk assessment and better loan pricing.

3. Test and refine underwriting criteria: To ensure cash flow data improves decision-making, lenders should run retro analyses on past applications, comparing outcomes with and without cash flow insights. Alternatively, a shadow test (where cash flow data is collected and analyzed but not used for actual approvals) allows lenders to track its impact in real time before making it a core part of underwriting.

Following these steps, lenders can improve approval rates, reduce defaults, and create a more inclusive lending process without adding unnecessary risk.

Overcoming challenges in the cash flow underwriting process

While cash flow underwriting offers a more complete picture of a borrower's financial health, leveraging this data does come with some challenges. Gaining internal buy-in, ensuring compliance, and managing data consistency are key hurdles lenders must overcome. 

Getting buy-in

Traditional lending teams—especially risk, compliance, and executive leadership—may hesitate to shift beyond credit scores without clear proof of impact. The best way to secure buy-in is to demonstrate the value of cash flow data through a retro analysis or shadow testing to prove the value before making a significant investment. By scoring past loan applications with cash flow insights and comparing outcomes, lenders can showcase how cash flow data improves approval rates or reduces defaults without disrupting the current process. 

Ensuring compliance 

Cash flow underwriting helps promote fair lending by expanding access to credit; however, lenders must ensure responsible use. That means following data protection regulations (such as GLBA) and ensuring cash flow insights are used in compliance with Equal Credit Opportunity Act (ECOA) and Fair Credit Reporting Act (FCRA) guidelines. Fortunately, Plaid Check supports a lender’s FCRA-related needs, from capturing permissible purposes to ensuring data accuracy. 

Ensuring data consistency 

Lenders need a reliable integration partner to standardize and categorize transactions across thousands of financial institutions. Choosing a provider that normalizes messy transaction data into structured insights ensures that underwriting models remain accurate and scalable.

The overall cash flow data implementation timeline can vary based on available resources but generally averages between two and twelve months. By being aware of and planning for these challenges, lenders can streamline and potentially speed up the implementation process.

Measuring the impact of cash flow data 

Once cash flow data is integrated, lenders must track key performance indicators (KPIs) to measure its impact and refine their models. Key areas to monitor include:

• Credit approval rates: Cash flow data helps approve applicants who might have been denied under traditional models and offers a more comprehensive picture of creditworthiness. Tracking approval rate increases—especially among previously unscorable borrowers—quantifies its value.

• Default rates and risk segmentation: By incorporating real-time financial behavior, lenders can more accurately assess risk. Comparing delinquency and charge-off rates for loans approved with and without cash flow data highlights its predictive power.

• Acceptance rates and completion times: Automated cash flow analysis streamlines income verification, reducing the need for pay stubs or bank statements. Tracking application completion and loan acceptance rates can indicate a smoother lending process.

By continuously monitoring these metrics, lenders can fine-tune underwriting criteria, optimize loan pricing, and ensure sustainable portfolio growth.

Leveraging cash flow data: Next steps

Cash flow data transforms the lending landscape by offering a more accurate, inclusive, and real-time view of borrowers' financial health. By incorporating data like income stability, spending behavior, and recurring expenses, lenders can expand access to credit, reduce risk, and improve loan conditions. 

The best next step for lenders looking to get started is a proof of concept (POC). Running a retro analysis or live shadow test allows institutions to measure the impact of cash flow data before investing resources in a launch. 

Ready to modernize your underwriting? Explore Plaid's credit solutions or contact our team to learn how real-time cash flow data can enhance your lending strategy.