Fraud and risk teams in banking, financial services, and insurance have spent years running on rule-based systems — and those systems have a well-known problem: they catch what they were explicitly told to look for, and generate a flood of false positives for everything else. SAP Business AI doesn't replace those rules; it sits alongside them, using data that's already inside SAP to catch patterns rules were never written to see.
Here are five ways we see BFSI organizations putting this to work in practice.
Transaction-level anomaly scoring
Instead of flagging transactions purely against fixed thresholds, SAP Business AI can score transactions against a customer's own historical behavior — catching activity that's unusual for that specific account, even if it falls within generic "normal" limits.
Reducing false-positive fatigue
One of the most immediate wins isn't catching more fraud — it's catching fewer false alarms. Layering AI-driven scoring on top of existing rules lets teams prioritize the alerts most likely to be genuine, so analysts spend time on cases that matter instead of clearing a backlog of noise.
Claims triage and routing
For insurers, SAP Business AI can assess incoming claims against historical claims data to route straightforward cases toward faster auto-approval paths, while flagging claims with unusual patterns for closer human review — speeding up the majority without cutting corners on the exceptions.
Credit and underwriting risk scoring
Embedded machine learning models can incorporate a broader set of signals already present in SAP data — payment history, account behavior, relationship tenure — into risk scoring, supplementing traditional credit models with patterns specific to your own customer base.
Regulatory reporting anomaly checks
Before regulatory reports go out, AI-driven checks can flag figures that deviate sharply from historical patterns — catching data quality issues or reporting errors before they become an audit finding, rather than after.
Where to start
None of these require ripping out existing fraud or risk systems. They're additive layers built on data that's typically already sitting in SAP — which is exactly why SAP Business AI tends to have a faster path to value in BFSI than a standalone AI platform would: the data foundation is largely already there.
The starting point we usually recommend is narrow and specific: pick one use case — transaction scoring or claims triage are common first choices — and prove it against real data before expanding further.