Built AI automation for fraud detection/investigation workflows — looking for teams still doing this manually [discussion/feedback]

Spent the last several years building fraud analytics systems at scale — detection models, investigation workflows, case management pipelines — mostly in financial services but the underlying problems translate across industries.
I’ve started taking on a small number of consulting engagements where I help teams automate the painful manual parts of fraud operations using AI. Looking to connect with people who are dealing with any of these:
E-commerce / Marketplace
Manual review queues that are drowning your ops team

Chargeback disputes being handled case-by-case with no pattern recognition

Seller fraud or fake review detection that’s still rule-based and brittle

Insurance
Claims fraud that goes to a human investigator for every single case

No automated narrative generation — analysts writing the same paragraph 50 times a day

Subrogation or duplicate claim detection done in spreadsheets

HR / Recruitment
Fake candidate profiles, credential fraud, résumé fabrication at volume

No system to flag anomalous application patterns

Healthcare
Billing fraud / upcoding that requires manual chart review

Prior auth abuse patterns being missed

Gaming / Digital Platforms
Account takeover, promo abuse, bot detection still handled by manual reports

No feedback loop from banned accounts back into detection logic

What I actually help with:
Automating investigator workflows so your team reviews instead of writes

Building feedback loops from human decisions back into models

Replacing brittle rule engines with adaptive ML pipelines

Cutting manual review queue depth without increasing headcount

Not here to sell anything — genuinely looking to understand what problems teams are sitting on. If any of this resonates, drop a comment or DM. Happy to talk through your specific situation first before anything else.