Remember the early days of the internet? If you wanted to find a job, an apartment, or a used bicycle, you went to one place: Craigslist. It was a massive, one-stop-shop that did everything.
Then came the "Great Unbundling." Specialized platforms emerged—like Zillow for real estate, Indeed for jobs, and Airbnb for rentals—each offering a superior, focused experience. They didn't kill Craigslist, but they carved out its most valuable functions, piece by piece.
Credit Bureaus are experiencing competition from data microservices which offer better, tailored results to improve Underwriting effectiveness and efficiency
A similar, quieter unbundling is happening right now in the world of financial underwriting, and the monolith in the crosshairs is the traditional, all-in-one credit report. The force driving this change is a powerful combination of cost-efficiency and the rise of automated re-underwriting engines.
Too much data?
For decades, the underwriting process has been straightforward. To assess a borrower's risk, a lender would pull a comprehensive credit report. This single, expensive document contains a vast amount of information: payment history, credit utilization, public records, inquiries, and more.
Monitoring individual data-points can trigger a more comprehensive, expensive due diligence
While thorough, this model is incredibly inefficient. It's like paying for an all-you-can-eat buffet when all you want is a slice of pizza. An auto lender, for instance, might only need to verify income and check for recent bankruptcies, but they still pay for the borrower's entire 10-year credit history. This one-size-fits-all approach is slow, expensive, and loaded with data that is often irrelevant to the specific decision being made.
High-signal data
Today, lenders are shifting from the monolithic credit report to a more agile, à la carte model built on specialty microservices. These are lightweight, API-driven tools that provide a single, specific piece of data, often in real-time and at a fraction of the cost.
Instead of one giant data pull, an underwriting team can now construct a "data stack" by making precise calls for the exact information they need:
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Income & Employment Verification: Services like Argyle and Truework connect directly to payroll or bank accounts to provide instant, verified data, bypassing the need for manual document uploads.
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Cash Flow Analysis: Tools from companies like Ocrolus, MX, and Plaid can analyze bank transaction data to assess a borrower's actual financial health and ability to pay, offering a much more current picture than a traditional credit score.
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Identity Verification: To combat fraud, specialized services from firms like Socure and Jumio use AI and biometrics to confirm an applicant's identity with greater accuracy and speed than ever before.
- UCC Lien & Borrowing Activity: Services like Springstreet.io, provide nation-wide access to Uniform Commercial Code (UCC) filings in a simple API interface allowing underwritesr to instantly see a business's borrowing history and identify any existing claims on assets like equipment or inventory
- Alternative Data: Companies now specialize in gathering and analyzing data not found in traditional credit files. Providers like MicroBilt offer insights on underbanked consumers, while services like Esusu report rental payment history to build credit profiles.
Alternative datasources can provide more comprehensive datasets vs. larger Credit Bureaus
Improved unit economics
This shift to microservices isn't just about modern technology; it's about fundamentally changing the cost structure of lending. Both underwriting (originating the loan) and servicing (managing the loan) are major cost centers. A tremendous return on investment (ROI) can be achieved, but only when the unit economics favor efficiency and accuracy.
Traditionally, the high, fixed cost of a full credit report meant every application, approved or denied, incurred a significant expense. This is where microservices provide their greatest value:
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Lowering Hard Costs: Instead of a single, multi-dollar fee for a full report, a lender can use a "waterfall" approach. They might first spend cents on an identity verification call. If that passes, they spend a bit more on an income check. A full credit report is only pulled for applicants who pass these initial, low-cost screenings. This dramatically lowers the average cost per application and makes the entire top-of-funnel process more profitable.
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Boosting Operational Efficiency: Time is money. Microservices deliver clean, structured data directly into a Loan Origination System (LOS) via an API. This eliminates hours of manual data entry and the time underwriters spend searching dense PDF reports for key information. This automation means each underwriter can process more applications per day, directly reducing labor costs and speeding up the time-to-decision.
When you multiply these per-loan savings across thousands of applications and the entire loan lifecycle—from initial underwriting to ongoing portfolio monitoring—the ROI becomes massive. It frees up capital, allows for more competitive pricing, and enables lenders to profitably serve a wider market.
Microservices provide greater, fine-tuned control to improve Underwriting efficiency and unit economics
A world of APIs and automated underwriting
The real game-changer accelerating this trend is the rise of automated re-underwriting engines. Lenders are no longer content with a one-time risk assessment at origination. They want to continuously monitor their entire loan portfolio for changes in risk. Is a borrower suddenly missing payments elsewhere? Has their income dropped?
This continuous monitoring is only economically viable with low-cost, targeted data. You simply cannot afford to pull a full, expensive credit report for every customer in your portfolio every month. It would be prohibitively expensive.
This is where microservices shine. An automated engine can be programmed to perform routine, low-cost checks. For example, it can ping a specific microservice once a quarter to ask a simple question: "Does this customer have any new public records?" If the answer is no, nothing happens. If the answer is yes, the engine can trigger a full credit pull for a manual review. This "tripwire" approach allows lenders to manage portfolio risk proactively without breaking the bank.
Underwriting decision engines can help mitigate future risk by automatically, reunderwriting portfolios to help trigger targeted, additional due diligence.
This isn't to say the traditional credit bureau is going away—just as Craigslist still exists. In fact, the major bureaus are adapting by launching their own suites of API-based verification services. But they are no longer the only game in town. They are becoming one component in a much larger, more dynamic ecosystem of data providers.
For modern lenders, the future is clear. The monolithic, one-size-fits-all credit report is a relic. The winning strategy is to build a flexible, cost-effective underwriting process by leveraging a constellation of specialized microservices—the very same forces that unbundled Craigslist.
For more information on how you can reduce risk and increase originations, please check out Springstreet.io