Cost of Manual Work — RegTech Providers

What this means for RegTech Providers

• Model auditability is the platform-level KPI the regulator cares about — the customer's regulator (OCC, FCA, FINRA, FinCEN, MAS, BaFin) will examine the model's decisions, the platform's documentation has to support that examination, and the in-house ML team is not yet operating with model risk management discipline.
• False positives are the customer's biggest operational complaint — every AML alert, every sanctions hit, every fraud flag, every adverse media match the platform produces is investigator time, and the customer's renewal conversation is shaped by the alert-to-true-positive ratio.
• Regulators are still figuring out their own AI posture — every 6-12 months a new SR letter, a new EBA guideline, a new FCA discussion paper, or a new state DFS bulletin shifts the rules, and the platform has to ship into a moving target.

Where it pays to act

• ML for AML transaction monitoring and fraud detection — gradient-boosted, graph, and behavioral models that materially outperform rules-based incumbents on both true-positive capture and false-positive reduction.
• Generative AI for SAR drafting and investigator support — narrative generation, evidence assembly, and suggested-disposition AI that compresses investigator time per alert without compromising the SAR quality regulators expect.