What it is
Identify suspicious claims and prevent leakage with predictive AI.
Business problem
Fraud leakage erodes margins and overwhelms SIU teams.
How the solution works
Train supervised ML using historical claims, networks, and behavior patterns to score fraud risk and trigger investigation workflows.
Typical workflow
1) Ingest & normalize data (APIs/files/forms)
2) Extract/transform key fields
3) Apply rules/models with guardrails & exceptions
4) Write back to systems and notify stakeholders
5) Monitor quality and iterate
Expected outcomes
10% fewer fraudulent payouts.
Key success metrics (to validate)
- Fraud losses reduced 10%
- False positives lowered
- SIU capacity prioritized to high-risk cases
Data & integrations
e.g., Policy/Claims Admin (e.g., PAS/claims system), DMS/e-signature, Data Warehouse/BI
Risks & controls
- Data privacy/PII minimization & redaction
- Explainability, audit logs, versioning & rollback
- Model drift monitoring; thresholds for human review
- Experiment governance (holdouts, approval gates)
Implementation path (SprintOps)
- Audit & opportunity mapping (2–4 wks): data readiness + KPI baselines
- Pilot (4–8 wks): narrow scope, measurable KPIs, human-in-the-loop
- Scale (ongoing): expand coverage, harden integrations/MLOps, governance
ROI (example, static)
Inputs to capture: volume, time saved per unit, loaded $/hr, monthly cost
Illustrative formula: Savings = Volume × Time Saved × $/hr; Net ROI = Savings − Cost
FAQ
- Will this replace people? It removes low-value tasks; staff focus on exceptions and customer impact.
- How do you ensure accuracy/governance? Sampling QA, audit logs, change control, and fallbacks.
- How long does a pilot take? Typically 2–8 weeks depending on scope and data readiness.
