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ClaimShield AI

Real-time fraud prevention for insurance claims using Databricks to detect suspicious activity early, reduce losses, and lower investigation costs.

The Problem

“Modernize fraud prevention with ML-powered, real-time claim risk detection”

Organizations face these key challenges:

1

Manual claim reviews are slow, costly, and prone to human error

2

Fraud rings and synthetic identity schemes evade static rule engines

3

Growing threat from deepfakes and digitally manipulated evidence

4

Delayed detection leads to financial losses and poor customer experience

Impact When Solved

Lower loss ratios and reduced fraud leakageFaster, straighter-through claims processing for low-risk casesScale fraud monitoring without linear headcount growth

The Shift

Before AI~85% Manual

Human Does

  • •Manually review and triage most claims for potential fraud indicators.
  • •Rely on experience and gut feel to spot suspicious patterns in narratives, documents, and photos.
  • •Investigate rule-based alerts using ad-hoc queries, calls to other carriers, and manual evidence gathering.
  • •Decide which claims to escalate to SIU and which to pay or deny.

Automation

  • •Basic rule-engine checks (e.g., simple thresholds, watchlists) embedded in the claims system.
  • •Deterministic validation such as data completeness checks, policy coverage rules, and simple duplicate detection.
  • •Batch reporting and retrospective analytics on paid claims (e.g., outlier reports).
With AI~75% Automated

Human Does

  • •Handle complex investigations, legal-sensitive cases, and high-risk alerts that require judgment and context.
  • •Validate AI recommendations on borderline or high-value claims and make final pay/deny decisions.
  • •Refine fraud investigation strategies, labels, and feedback loops to improve model performance over time.

AI Handles

  • •Continuously score every claim, party, and document for fraud risk in real time using ML models.
  • •Automatically flag anomalies, suspicious patterns, and potential fraud rings across carriers, products, and time.
  • •Pre-triage claims by risk level, routing low-risk claims to straight-through processing and high-risk ones to specialists.
  • •Analyze unstructured text, images, videos, and documents to detect manipulation, deepfakes, and synthetic identities.
Operating ModelHow It Works

How ClaimShield AI Operates in Practice

This is the business system being implemented: how work is routed, which decisions stay human, what gets automated, and how success is measured.

Operating Archetype

Detect & Investigate

AI surfaces what's hidden. Humans investigate and judge.

AI Role

Research Assistant

Human Role

Investigator

Authority Split

AI assembles the case; humans do the substantive investigative judgment.

Operating Loop

This is the business workflow being implemented. The four solution levels are different ways to operationalize the same loop.

AIStep 1

Scan

Scan broad data sources continuously.

AIStep 2

Detect

Surface anomalies, links, or emerging signals.

AIStep 3

Assemble Evidence

Pull related records into a working case file.

HumanStep 4

Investigate

Humans interpret evidence and make case judgments.

AIStep 5

Act

Carry out the human-directed next step.

FeedbackStep 6

Feedback

Closed investigations improve future detection.

Human Authority Boundary

  • The system must not deny a claim, stop a payment, or refer a case for legal or regulatory action without human review and approval.

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