Bringing real-time fraud prevention to government benefits

Bringing real-time fraud prevention to government benefits | Databricks Blog

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Federal benefit programs are often the focus of fraudulent behavior because they are optimized for delivering aid quickly with integrity checks happening after the payments have been made.

Databricks has been helping federal agencies detect fraud based on years of experience working with banks, credit card companies, and insurers.

More than 80% of U.S. federal executive departments already use Databricks, and tools like OpenSharing and Clean Rooms let agencies share fraud signals across silos without exposing raw data, catching schemes no single agency could spot alone.

Asked to do the impossible

Fraud and improper payments cost federal benefits programs hundreds of billions of dollars each year, affecting Medicare, Medicaid, disaster relief, student aid, food assistance, unemployment insurance, and many other public programs. The Government Accountability Office estimates federal fraud losses at $233 billion to $521 billion a year, with roughly $186 billion in improper payments reported in fiscal 2025 alone.

That isn't a failure of oversight. It's the byproduct of a real dilemma. These programs are designed to deliver aid quickly and at enormous scale. At that volume, deeply vetting every transaction before the money goes out is nearly impossible without delaying aid to the people who actually need it. So detection has traditionally happened after the fact, in a "pay and chase" model. But once a fraudulent payment goes out, recovering the money is slow, costly, and rarely successful. That's why stopping fraud in real time matters so much. Fraudsters exploit exactly that gap with shell companies, stolen identities, and phantom claims, and they switch tactics the moment a scheme is shut down.

The good news is that agencies no longer have to choose between speed and better decisions. Advances in AI, real time analytics, and access to more complete data make it possible to evaluate transactions as they happen, without slowing the delivery of benefits. Rather than relying primarily on after-the-fact investigations, agencies can use intelligent risk scoring, entity resolution, behavioral analysis, and machine learning to identify suspicious activity before funds are disbursed. Databricks provides the data and AI platform to build and operate these systems, enabling agencies to make faster, higher quality decisions in real time while getting critical assistance to legitimate recipients without unnecessary delays.

Fraud detection is what we do

At Databricks, real-time fraud detection is a subject we know very well. We've built fraud detection systems for banks, insurers, and online retailers, scoring transactions and blocking the bad ones while still allowing legitimate activity to flow through quickly. These systems routinely evaluate billions of transactions each year, often making decisions in just tens of milliseconds. If you've ever received a text message about a suspicious purchase being blocked before it was approved, you've likely seen this technology in action.

The same capability can protect federal aid: a claim can be scored the instant it's submitted and held before a dollar moves.

A layered approach to catching fraud

The most effective systems are layered, escalating from simple to sophisticated:

Rules engines are the first line of defense. They catch the most obvious cases quickly and cheaply, like a payment issued to someone who has died, the same claim submitted twice, or a single bank account collecting benefits meant for dozens of different people.

Machine learning detects the patterns of fraud and scores risk far more precisely than fixed rules can. It can flag a surge of unemployment claims routed to one bank account, a small business requesting a far larger relief loan than its peers, or a provider billing well above others in the same field.

Adaptive and generative AI bring the most advanced capabilities to fraud detection. Neural networks continuously retrain and adapt as criminals change their tactics, learning what normal behavior looks like so they can quickly identify suspicious activity. Generative AI performs deep reasoning across structured and unstructured data to detect subtle patterns, explain its findings, and help investigators make faster, more informed decisions.

Running on one platform, the layers reinforce each other instead of living in disconnected tools.

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Visualizing rules engine outputs, entity resolution, and graph analytics: a single provider mapped across six separate agency datasets, revealing the cross-organizational connections that initiate fraud alerts. By integrating data across agencies, we expose hidden schemes that single agencies cannot see. All names, agencies, and figures shown are synthetic demo data.

Why Databricks is the right place to run it

Over 80% of federal executive departments are already using Databricks today, so the hard work of data engineering is often already done.

But the deeper reason platform choice matters is that fraud, by its nature, doesn't respect agency boundaries. The same bad actor can walk into a different agency, or even a different program in the same agency, and look like a first-time, low-risk applicant. Fraudsters already operate this way, moving fluidly across programs while the agencies chasing them are stuck in silos.

A better model at any single agency is only a partial fix. Every layer of the detection system, from the simplest rule to the most advanced neural network, is only as sharp as the data it learns from. When each agency sees only its own slice of activity, every layer is working with an incomplete picture.

Cross-agency sharing closes that gap. Every agency contributes fraud signals in real time, sharpening the rules, refining the models, and increasing the detection rate. The more agencies that plug in, the smarter the whole system gets, catching schemes no single agency could have spotted on its own.

Databricks is built for exactly that kind of sharing without compromising data privacy or ownership. OpenSharing exchanges live data without copying it, data masking hides personal details while preserving analytic value, and Clean Rooms let agencies collaborate on protected records without exposing raw data to each other, all under full governance and access controls.

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A live view of data moving between agencies without ever being copied. OpenSharing and Clean Rooms let each agency contribute records to a shared fraud investigation while keeping its raw data protected, under full governance.

Taking action when fraud is detected

Detection only matters if you can act on it. On a unified platform, the same system that flags a bad actor can drive the response: holding a suspicious payment, adding a confirmed fraudster to the Do Not Pay list, and assembling a prosecution-ready evidence packet with every violation mapped to its statute, an AI summary, and a digitally signed audit trail. A human stays in the loop, confirming flagged cases before any action is taken.

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Instant generation of a trial-ready evidence file, where every violation is linked to specific laws and verified with a cryptographically signed history. By automating the referral packet, investigators can focus on enforcement instead of manual documentation.

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Every decision remains under human oversight. Referrals are only bundled and transmitted once designated personnel provide formal authorization, with each phase documented within a permanent audit history.

The bottom line

With Databricks, what was once impossible is now proven at scale. The same technology that screens 160 billion card payments a year, scoring each one for fraud in milliseconds, can be used for federal benefits, using the data and AI platform agencies already have. Fraud gets caught the instant it appears, legitimate aid keeps flowing, and real-time protection finally operates at national scale. This isn't a someday capability. It's ready to implement today.

See how Databricks helps government agencies detect and stop fraud in real time. Explore our public sector solutions and the real-time fraud detection Solution Accelerator, or contact our team to scope a pilot.

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