Tax & Financial Data Processing · Regulatory AI
Reviewers Now Handle Exceptions Only, Saving an Estimated $3M a Year
A tax and financial data processing firm screens billions of records against 2,000+ regulatory sub-processes. We built a validation layer that uses artificial intelligence (AI) to check every record before the calculation engine runs, catching inconsistencies manual sampling would miss. It takes validation from about 25 reviewers to 5, an estimated $3M a year.
$3M/yrValidation headcount saved, about 25 reviewers to 5
BillionsRecords validated against thousands of rules
0Reruns from input errors after launchThe Challenge
The firm processes billions of records against thousands of regulatory rules. Inconsistencies surfaced only after the calculations had already run, which meant re-running a large, complex processing engine at real cost every time. Manual sampling could never reach full coverage, and full coverage by hand would have needed a validation team costing millions a year. At billions-of-records scale, even a tiny error rate means millions of wrong data points flowing into regulatory reports. Sampling-based review could never cover everything, so the risk of an undetected error stayed live no matter how many people the firm added. That is a regulatory exposure headcount alone could not close.
What We Did
We built an AI validation layer trained on the regulatory rules themselves. It screens billions of records and surfaces the inconsistencies that matter before any calculation runs, so errors are caught at the input stage rather than found in the output. We built the validation into the pipeline rather than bolting it on afterward, so it runs alongside the processing engine and nothing waits on a separate checking step. The pipeline also produces more than 200 structured Excel report types across more than 2,000 sub-processes, ready for regulatory use.
The Solution
An AI validation layer now screens every record against the regulatory rules before the calculation engine runs. Reviewers see the inconsistencies that matter, not a sample. Input errors are caught up front, so the engine does not need re-running.
The Outcome
Full coverage by hand would have taken around 25 reviewers at $150K each, $3.75M a year, and sampling still could not guarantee it. The AI layer reaches that coverage with about 5 reviewers on exceptions only, saving an estimated $3M a year. Since launch there have been zero reruns caused by input errors. The reviewers who remain work on exceptions rather than sampling, and the regulatory exposure that headcount could never close is now covered by a check that runs on every record.
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