[.green-span]Can Data Aggregation Speed Loan Approvals? How It Works for Lenders[.green-span]

Can Data Aggregation Speed Loan Approvals?

Yes. Data aggregation can speed loan approvals by replacing manual document collection and data entry with automated, permission-based data pulls. How much time it saves depends on how lenders orchestrate that data and set their decision rules.

In the 2019 interagency alternative data statement, five federal agencies addressed alternative data. That term means information not usually found in a credit report, such as bank account cash flow. They said using it lawfully "may improve the speed and accuracy of credit decisions."

Speed comes from the full workflow, beyond the data pull itself. Lendflow reports, in its Lendflow platform results, that pre-qualified offers hosted on Lendflow drive an average of 42% faster speed to funding. That figure reflects the full Lendflow workflow, not aggregation alone.

What Data Aggregation Means in Lending

Data aggregation is the process of collecting a borrower's financial data from many sources into one view. In lending, that usually means bank transactions, balances, and income, pulled with the borrower's consent.

Data aggregators are the companies that connect borrower accounts to lenders. This consent-based data sharing is often called open banking. For a deeper look, read our guide to financial data aggregation for underwriting.

Aggregators reach bank data in two main ways:

• APIs: An application programming interface (API) lets two systems exchange data directly. The bank sends structured data through a secure connection.
• Screen scraping: The aggregator logs in with the borrower's credentials and copies data from the bank's website. It is an older method that depends on stored login details.

Open banking is already mainstream. The CFPB estimate of open banking use found at least 100 million consumers had authorized a third party to access their account data (2022 data). The CFPB also estimated that about half of that access ran through APIs, with scraping making up most of the rest.

Where Traditional Underwriting Slows Down

Underwriting is how a lender reviews an applicant's risk before approving a loan. In a manual process, collecting and checking information eats much of that time.

Common bottlenecks include:

• Document chasing: Lenders request bank statements, tax returns, and IDs, then wait for borrowers to send them.
• Manual data entry: Staff retype figures from PDFs into spreadsheets or loan systems, which takes time and risks typos.
• Statement review: Analysts read months of transactions line by line to estimate revenue and spot red flags.
• Sequential pulls: Credit, identity, and bank checks run one after another instead of at the same time.
• Back-and-forth: Missing pages or unclear deposits trigger new requests, which restart the clock.

Bank timing varies widely. In the FDIC small business lending survey, three-quarters of banks reported approving the typical small business loan in two weeks or less (2022 survey, bank-reported). Slightly more than half of large banks could approve a small, simple loan in one business day or less, versus 29% of small banks.

The FDIC says that gap may be due to large banks' greater use of automated lending technology or credit scoring. Automation, in other words, is already linked to faster bank decisions.

How Data Aggregation Cuts Time to Decision

Aggregation shortens the path from application to decision in four main ways. Each one removes a manual step or a wait.

Automated Data Retrieval

Permissioned bank connections replace uploaded statements. The borrower logs in once, and the lender receives structured transaction data instead of a stack of PDFs.

That step removes the document chase and most manual entry. Analysts start with clean, categorized data from the first day.

Parallel Data Pulls and Real-Time Decisioning

Automated systems can pull credit bureau, bank, and KYB data at the same time. KYB, or know your business, is the check that confirms a business is real and identifies its owners.

Decision rules can then score the file as soon as the data arrives. Our guide to intelligent credit underwriting shows how parallel pulls feed automated decisions.

Faster Verification and Fraud Checks

Identity, KYB, and fraud checks can run in the same flow as the data pull. Bank data can also reveal mismatches, such as deposits that don't match the revenue an applicant reported.

With automated screening, clean files move ahead and only suspicious ones go to a human reviewer. Teams spend review time where the risk is highest.

What the Evidence Shows

Research links digital data collection with faster processing. The best evidence, though, is older and mortgage-specific. A 2018 NY Fed fintech mortgage study found "FinTech lenders process mortgage applications about 20 percent faster than other lenders."

Those lenders collected borrower account statements electronically, and faster processing did not come with higher defaults. The study covered 2010–2016 mortgages and did not separate aggregation from other automation.

In the 2025 Fed Small Business Credit Survey, 64% of online lender applicants cited speed of funding as a reason for their choice. Only 29% of large bank applicants said the same. Only 8% of online lender applicants reported a long wait for a decision or funding, compared with 21% of large bank applicants.

Those survey figures are self-reported and come from a non-random sample. No study we found isolates aggregation's own effect on approval time. The evidence shows an association—the size of the gain depends on the rest of the workflow.

Cash-Flow Data: Faster Decisions for More Borrowers

Cash-flow data shows how money moves through a bank account: deposits, spending, balances, and overdrafts. It is one of the most common types of alternative data in lending.

This data can carry real signal. In 2019, FinRegLab cash-flow underwriting research found cash-flow predictiveness "generally at least as strong as the traditional credit scores" it studied. The data came from six non-bank lenders, so results apply best to similar products and borrowers.

Cash-flow data also matters for access to credit. Credit invisibles are adults with no credit record at a nationwide credit bureau. The CFPB's corrected credit invisible estimate put that group at 2.7% of adults, or 7.0 million consumers, as of December 2020 (published 2025).

Another 9.8% of adults had a credit record but no score. Many articles still cite about 26 million credit invisibles, a 2010 estimate the CFPB has since revised to 13.5 million.

For these consumers, bank data may give lenders something to assess when a credit file can't. The 2019 interagency statement also said alternative data may help firms evaluate consumers outside the mainstream credit system.

Small businesses face a similar gap. Young companies often lack a business credit history, but their bank accounts show revenue and expenses as they happen. Learn which inputs matter most in our guide to SMB underwriting data sources.

To learn how lenders turn transactions into risk signals, read our cash flow underwriting guide. It covers the attributes underwriters review most often.

Aggregation Needs Orchestration to Deliver Speed

Aggregation collects data. Data orchestration decides when to request each source, in what order, and what happens after each result.

Without orchestration, a lender can have fast data and still make slow decisions. Orchestration adds four capabilities:

• Smart sequencing: Request bank data only when bureau data leaves questions, so strong applicants skip extra steps.
• One decision view: Bureau and bank data land in a single response for the rules engine.
• Decline waterfalls: A decline waterfall routes a declined applicant to the next lender whose credit criteria fit.
• Exception routing: Only files that miss the rules go to an underwriter for review.

Bureau and bank data work best together. See how teams approach combining Plaid and bureau data in a single credit decision.

Here is how each step changes as a workflow matures:

Compliance and Risks to Weigh

Faster data does not change the rules. Lenders using aggregated data still need to follow consumer protection laws, such as the Equal Credit Opportunity Act (ECOA).

• Fair lending: Under ECOA, Regulation B anti-discrimination rules protect applicants from discrimination in any aspect of a credit transaction. Test new data for unfair outcomes.
• Credit reporting: The Fair Credit Reporting Act text can cover third parties that regularly assemble consumer information for consumer reports. Get legal review of each source.
• Consent and transparency: Tell borrowers what data you collect, why you need it, and how long you keep it.

Section 1033 is the CFPB's open banking rule for sharing consumer financial data. According to CFPB personal financial data rights, the rule was finalized in October 2024. A federal court stayed its compliance dates on October 29, 2025.

According to the Congressional Research Service's CRS Section 1033 overview, implementation was originally set to begin in April 2026. CRS notes the rule is now the subject of litigation and reconsideration by the CFPB.

In April 2026, law firm Cozen O'Connor reported the rule enjoined during reconsideration. A federal court has barred the CFPB from enforcing it while the agency reconsiders. Check the current status before you build plans around it.

Operational risks matter too:

• Security: Bank data is sensitive, so vet each partner's encryption, access controls, and breach response.
• Connection failures: The CFPB's 2023 proposed rule reported aggregator initial connection failure rates of 28% to 60%, based on non-representative data.
• Data quality: Miscategorized transactions or missing accounts can skew results, so audit how transactions are labeled.

Best Practices for Faster, Safer Approvals

The fastest lenders treat speed and safety as one design problem. Use this checklist to build both into your workflow.

Start with the step that causes the most delay today. Then measure time to decision before and after each change.

How Lendflow Speeds Up Approvals

Lendflow reports $1.5B+ in offers made on its platform in the last 12 months (as of March 2025). Lendflow also reports that pre-qualified offers hosted on Lendflow drive an average of 42% faster speed to funding. Its embedded finance customers operate with 80% smaller teams while converting similar funding volumes.

Lendflow's product suite covers each layer of a faster approval:

• Lendflow Intelligence: Returns traditional and alternative data in one API response for credit decisioning.
• Lendflow Connect: Gives you access to a network of 75+ specialty and bank lenders to power decline waterfalls.
• Lendflow Automate: Runs intelligent workflows, with Trust Score built in to support risk decisions.
• Doc Analyzer: Automates document analysis, so teams spend less time reading paperwork.

Data Orchestration ties these together. Lenders can connect with top integration partners in minutes and build decline waterfalls that keep qualified deals moving.

Skip long build cycles—launch with widgets, landing pages, and APIs that embed financing in your product. See how Lendflow underwriting automation helps teams fund faster.

Ready to cut the wait between application and decision? Book a demo or talk to our team to see how Lendflow can help you scale smarter.

Frequently Asked Questions About Data Aggregation in Lending

Should borrowers connect their bank accounts when applying for a loan?

Connecting a bank account can save borrowers from gathering and uploading statements, which may speed up the review. Borrowers should first check what data the lender collects and how it will be used.

How do lending APIs speed up approvals?

Lending APIs move data between systems automatically, so lenders can pull bank, credit, and KYB data at once. Decision rules can then score the application as soon as the data arrives.

What is alternative data in credit underwriting?

Alternative data is information not usually found in a credit report, such as bank account cash flow or rent payments. Lenders use it to add context to traditional credit scores.

Who benefits most from alternative data?

Borrowers with little or no credit history, including young businesses, may benefit most because bank data shows how they manage money. Lenders benefit from a fuller picture of applicants they might otherwise decline.

What compliance rules apply to aggregated data?

Fair lending laws such as ECOA apply, and FCRA may apply to some third-party data. Lenders also need clear borrower consent and should track the status of the CFPB's Section 1033 rule.

How can lenders fight fraud while moving faster?

Run identity, KYB, and fraud checks in the same automated flow as the data pull. Send only flagged files to human reviewers so clean applications keep moving.