AI-Driven Fraud Detection for Claims
Sapnity implemented an AI-driven fraud detection layer on Power Platform and Azure that sits above multiple claims and policy cores — surfacing high-risk patterns in health and motor claims, orchestrating investigations, and reducing leakage for a 450+ FTE GCC servicing 12+ countries.
1. Business Problem — Fraud Hidden in Plain Sight
The client is a multi-line insurer running a large GCC in India for claims operations, servicing health, motor and property portfolios across 12+ markets. While they had invested in modern claims platforms, fraud controls were still fragmented and largely manual.
- Suspicious indicators were scattered across Excel sheets, emails and legacy tools.
- Frontline claims handlers relied on “gut feel” and informal blacklists to flag cases.
- Special Investigation Unit (SIU) investigators used separate systems for case tracking.
- Fraud rules were hard-coded inside individual claims cores by vendor teams.
- Regional CEOs saw aggregate loss ratios, but not which fraud patterns drove leakage.
The GCC knew fraud was eroding margin — but lacked a single fraud brain to ingest signals, score cases and orchestrate consistent investigations.
2. Sapnity’s Mandate
Group leadership asked Sapnity to design a fraud layer that would:
- Aggregate fraud signals from multiple claims and policy cores without a big-bang replacement.
- Combine machine learning, rules and watchlists in one controllable engine.
- Provide a single SIU workbench for investigators across lines of business.
- Surface clear KPIs on leakage avoided, hit rates and investigator productivity.
- Establish a pattern that can be replicated across new markets and products.
3. Before — Fragmented Fraud Controls & Blind Spots
Every portfolio had its own way of handling suspicious claims. Health operations used nursing reviews and manual threshold checks; motor claims teams maintained e-mail-based garage watchlists; property teams tracked unusual patterns via ad-hoc SQL queries.
- Alerts were raised late — often after payout — making recovery difficult.
- No unified scoring — the same claimant could look “clean” in one line, suspicious in another.
- Rules were buried inside vendor-maintained systems, making changes slow and expensive.
- Investigators had to juggle 3–4 tools just to assemble a basic case file.
The insurer was paying for data and analytics, but fraud decisions still lived in spreadsheets, inboxes and individual memory.
4. After — Sapnity AI Fraud Detection Hub
Sapnity introduced an AI Fraud Detection Hub built on Power Platform and Azure — a single orchestration layer that ingests claims data, scores risk, and routes high-risk cases into an SIU workbench with full auditability and analytics.
FNOL & Claims Intake
Claims created via portals, agents, branches and partner systems.
Fraud Signal Ingestion
Data from claims, policy, payments, providers, garages, devices and external watchlists.
AI Risk Scoring Engine
Machine learning models + expert rules + pattern libraries generating risk scores per claim.
SIU & Claims Workbench
Power Apps workspace where handlers and SIU review cases, document actions and manage outcomes.
Core Claims & Payments
Dynamic integration patterns to hold, pay, or partially settle claims based on investigation status.
Fraud Intelligence & Governance
Portfolio-level leakage avoided, pattern heatmaps and rule / model performance dashboards.
The same pattern is now the reference design for new markets and products, with local configurations in Dataverse — not hard-coded in vendor systems.
5. Implementation Story
Phase 1 — Fraud Landscape & Baseline
- Worked with Group Risk, Claims and SIU to catalogue historic fraud cases, patterns and investigation workflows.
- Analysed 3 years of claims and policy data to identify high-leakage segments and suspicious networks.
- Defined a baseline of fraud leakage, hit rates and investigation throughput for key lines.
Phase 2 — Fraud Hub Blueprint
- Designed a common fraud object model spanning claims, parties, providers, garages, intermediaries and networks.
- Agreed a unified alert taxonomy: suspicion reasons, severity levels, actions and outcomes.
- Mapped how new AI scores would plug into existing claims decision points without breaking SLAs.
Phase 3 — AI & Rules Engine Build
- Built supervised ML models in Azure ML for key fraud types (e.g., opportunistic, organised, provider fraud).
- Implemented expert rules and threshold logic in Dataverse tables to keep business in control.
- Wrapped both in Power Automate-based orchestration flows emitting a single, interpretable score per claim.
Phase 4 — SIU Workbench & Pilot
- Delivered a Power Apps SIU workbench for triage, investigations, document management and outcome tracking.
- Ran a controlled pilot on one health portfolio and a subset of motor claims to tune thresholds.
- Coached SIU on documenting outcomes to create a feedback loop into model retraining.
Phase 5 — Scale-Out & Continuous Learning
- Extended fraud detection coverage to additional markets and lines via configurations, not new builds.
- Set up quarterly model performance reviews and drift monitoring with Group Risk & Analytics.
- Embedded fraud KPIs into executive dashboards, closing the loop between fraud controls and underwriting decisions.
6. Technical Architecture — Layered View
7. Reusable Fraud Detection Pattern for New Markets
The output was not just an AI model, but a fraud detection pattern the insurer can roll out market by market. New countries or products plug into the same pattern, reusing the data model and SIU workbench while customising rules and thresholds locally.
8. Outcomes & KPIs
| KPI | Before | After Sapnity Fraud Hub |
|---|---|---|
| Estimated fraud & leakage (portfolio-level) | 3.5–4.2% of GWP | 2.2–2.6% of GWP within 18–24 months |
| False positive rate on fraud alerts | ~72% | < 38% after AI + rules tuning |
| Average time to assign case to SIU | 10–14 days from FNOL | < 2 days for high-risk claims |
| Investigator productivity (cases closed / month) | Baseline (100%) | +35–45% with unified workbench |
| Time to onboard a new market / product | 12–18 months of fragmented initiatives | 10–14 weeks using the fraud hub pattern |
The fraud hub is now part of the insurer’s core risk fabric — informing claims, pricing and underwriting decisions instead of sitting as a separate “analytics experiment”.
9. Sapnity Differentiators
- Insurance GCC reality first: Designed for complex, multi-country portfolios, not just a single domestic line of business.
- Responsible AI, not black-box AI: Combined interpretable rules and model explanations with clear governance to keep regulators and auditors comfortable.
- Pattern over point solution: Delivered a reusable fraud hub pattern that can be replicated across markets with configurations, not re-builds.
- SIU-centric design: Built the workbench around how investigators actually work, with case timelines, document trails and collaboration embedded.
- ALM & lifecycle baked in: Managed solutions, model versioning and Dev→Test→Prod pipelines that fit into existing change governance at the GCC.
For this insurer, Sapnity did not just “add AI” — we installed a fraud brain that continuously learns from outcomes and scales as the portfolio grows.