Catching UI Fraud Early

How Cross-Matching, SIDES, and Tips & Leads Stop Fraud Before It Starts

Part two of our latest UI blog series looks at the detection layer: the tools that catch fraud before a single benefit check goes out.

The Cost of Finding Out Too Late 

Every fraud program eventually runs into the same math problem. A claim that slips through undetected doesn’t just disappear, it becomes a recovery case, and recovery cases are slower, more expensive, and far less certain than catching the same issue before payment. Notices go unanswered, claimants move, and by the time an agency has confirmed fraud after the fact, a meaningful share of the money is often already unrecoverable. We touched on this dynamic in part 1 of this series, where fund stewardship, not just claims processing, became the frame for the whole conversation. 

That math is why the agencies gaining ground aren’t necessarily the ones with the largest investigations units. They’re the ones that have shifted investment toward the point of entry, catching issues at intake and during the claim window, rather than relying on downstream audits to clean up what got through. Three tools do most of that work: wage cross-matching, SIDES integration, and a tips & leads process that actually routes what it receives. 

Wage Cross-Matching and Work & Earnings Issue Detection 

Work-and-earnings fraud, a claimant collecting benefits while working, or misreporting income to inflate a benefit amount, is one of the most common issues agencies face, and one of the hardest to catch by manual review alone. Automated wage cross-matching solves this by comparing reported wage data against active claims on an ongoing basis, rather than waiting for a scheduled audit cycle. 

When a mismatch surfaces, it’s flagged immediately, while the claim is still open and the facts are still fresh, instead of months later when a claimant may no longer be reachable and the agency is left reconstructing a paper trail. The practical effect is fewer improper payments reaching claimants in the first place, which also means a smaller downstream caseload for the recovery and adjudication teams we’ll cover in later posts. 

Identity verification plays a related role here. As we’ve written elsewhere, modern identity proofing and AI-driven fraud detection close a gap that cross-matching alone can’t: confirming that the person behind a claim is who they say they are, before a mismatch even has the chance to occur. 

SIDES: Faster Employer Response, Fewer Blind Spots 

A lot of fraud detection depends on getting accurate information from employers quickly, and that’s historically been one of the slowest parts of the process. Manual information requests sent by mail or fax routinely sit for weeks, during which a determination that should have taken days instead stalls. 

SIDES integration replaces that bottleneck with structured electronic exchange, so employer responses come back faster and in a consistent, system-readable format. The agency isn’t just moving faster on the cases it already has; it’s closing the blind spot created by employers who simply never got around to responding, because the request is now built into their existing workflow rather than an extra manual task. 

Turning Tips Into Action 

Tips and leads still matter. The public, employers, and other agencies routinely surface fraud that no algorithm would catch on its own; a claimant bragging about double-dipping, or an employer noticing a suspicious pattern in their own records. The problem most agencies run into isn’t a lack of tips, it’s what happens to them after they arrive. 

A tip that lands in a shared inbox and waits for someone to have spare time to review it is a tip that’s effectively been deprioritized, whether anyone intended that or not. Automated intake and triage changes that by routing each lead to the right queue immediately, based on the type of issue and its risk level, so tips get the same fast, consistent handling as an automated cross-match flag. 

Taken together, cross-matching, SIDES, and tips & leads intake give an agency three different early-warning systems feeding the same pipeline. None of them is a complete solution on its own. A cross-match can miss a scheme that hasn’t yet generated a wage discrepancy, SIDES only helps once a request has already been sent, and a tip is only as good as what happens to it next. The value comes from running all three together, so a gap in one is covered by the others. 

Making Detection Actionable, Not Just Accurate 

Accuracy alone doesn’t close a fraud case. A detection layer that generates the right flags but drops them into an unmanaged queue still leaves staff to sort out what matters, and that sorting work is exactly where backlogs form. A well-designed case management layer prioritizes flags by risk and dollar exposure, so a high-value fictitious employer signal doesn’t sit behind a dozen lower-priority work-and-earnings mismatches simply because it arrived later. 

This is also where visibility pays off at the leadership level. When cross-matching, SIDES responses, and tip intake all feed into a single dashboard, program managers can see where the caseload is actually building, rather than relying on anecdote from individual investigators. That visibility makes it far easier to justify staffing decisions, or to spot a new fraud pattern early enough to respond before it scales across the state.