From SIU to Settlement: The Claims Investigation Workflow with AI

Insurance fraud investigation is a process with defined stages, decision points, and evidence requirements. AI-powered deepfake detection doesn’t replace this process — it strengthens every stage of it.

This article maps the complete claims investigation workflow from initial triage through resolution, showing where AI detection integrates and how it transforms each stage.

Stage 1: Detection and Triage

Before AI

Claims are flagged for investigation through:

Rules-based triggers — claim filed within 30 days of policy inception, claim value exceeds threshold, claimant has prior claims history
Adjuster suspicion — something about the claim doesn’t feel right (inconsistent story, unusual documentation, claimant behavior)
Tip-offs — information from external sources (law enforcement, other insurers via NICB or IFBA, public reporting)

The gap: None of these triggers analyze the evidence itself. A fraudulent claim with AI-generated photos, realistic documents, and a plausible narrative passes all three screens.

With AI Detection

AI media analysis adds a fourth trigger — and often the most decisive one:

Automated media screening at intake. Every photo, video, document, and audio file submitted with a claim is analyzed before the adjuster reviews it. Detection results are attached to the claim record.

Triage output:

Detection Result	Claim Routing	Adjuster Action
Clean — no manipulation indicators	Standard processing queue	Review claim normally; detection result logged for audit
Low risk — minor metadata anomalies	Standard queue with advisory note	Review note; may request additional evidence
Medium risk — suspicious indicators detected	Priority review queue	Review forensic findings; assess whether escalation is warranted
High risk — high-confidence manipulation detected	SIU referral queue	Do not process; refer to SIU with forensic report

Key improvement: Instead of relying solely on claims data patterns and human intuition to identify suspicious claims, every claim is screened for evidence manipulation. Fraudulent claims with normal data patterns but fabricated evidence are caught at the earliest possible stage.

Metrics at This Stage

Detection coverage: % of claims with media analyzed before adjuster review (target: 100%)
Alert rate: % of claims flagged (typical: 2-5% for medium + high risk)
Triage latency: Time from submission to detection results available (target: < 5 minutes for images)

Stage 2: Initial Assessment

The Adjuster’s Enhanced View

When the adjuster opens a flagged claim, they see:

Standard claim information:

Claimant details, policy information, incident description
Submitted media (photos, videos, documents)
Claims history, coverage verification

Plus forensic intelligence (new):

Detection summary: Overall risk level with brief explanation
Finding details: Specific manipulation indicators identified, with confidence levels
Visual evidence: Heatmap overlays showing exactly where manipulation was detected
Metadata analysis: Camera model verification, timestamp consistency, GPS check

This transforms the adjuster’s initial assessment from “does this claim look right?” to “this claim has specific forensic findings that require attention.”

Decision Points

Based on the combined standard and forensic information, the adjuster decides:

Proceed normally. Clean detection result + normal claims data = standard processing. The detection result is logged for audit purposes.

Request additional evidence. Suspicious but inconclusive findings. The adjuster requests:

Additional photos from different angles
A video walkthrough of the damage
Original files (not re-compressed through a messaging app)
Supporting documentation from an independent source

Each additional evidence request increases the cost and complexity for a fraudster — and provides additional material for detection analysis.

Escalate to SIU. High-confidence manipulation detection + other fraud indicators = formal referral. The adjuster documents the specific findings that support the referral.

Avoiding False Positive Friction

Not every detection alert indicates fraud. Legitimate causes of detection alerts include:

Heavy compression from older devices (metadata anomalies)
Photos taken by someone other than the claimant (using a friend’s phone)
Screenshots or photos of photos (legitimately poor quality)
Photos edited for non-fraudulent reasons (cropping, brightness adjustment)

The adjuster’s role is to assess detection findings in context. A metadata anomaly on an otherwise straightforward claim from a long-standing customer warrants a different response than the same anomaly on a new customer’s high-value claim. Human judgment remains essential.

Stage 3: SIU Investigation

Investigation Initiation

When a claim is referred to SIU, the investigator receives:

Traditional referral package:

Claim file with all submitted documentation
Adjuster notes and referral rationale
Claims history and database cross-references

Enhanced with AI forensics (new):

Full forensic report for all submitted media
Visual heatmaps for every analyzed image/video
Metadata analysis for every file
Cross-claim analysis if pattern matching identified related claims
Specific findings that triggered the referral, with confidence levels and methodology descriptions

This means the investigator starts with evidence, not just suspicion. The forensic findings direct the investigation — showing exactly what to focus on and what additional evidence to seek.

Investigation Activities

Evidence deep analysis. The investigator can request deeper analysis of specific evidence items:

Frame-by-frame video analysis at maximum depth
Detailed frequency domain reporting for specific images
Cross-referencing specific images against the full claims database
Audio analysis of recorded statements

Additional evidence gathering. Based on forensic findings, the investigator seeks corroborating information:

Independent verification of the incident (police records, weather data, third-party witnesses)
Physical inspection of the claimed damage (if applicable)
Recorded statement from the claimant (now with voice analysis)
Contact verification with document issuers (hospitals, repair shops, police departments)

Network investigation. If cross-claim analysis identified related claims or shared entities, the investigator expands the scope:

Other claims from the same claimant, address, or device
Claims involving the same repair shops, medical providers, or legal representatives
Claims submitted from the same geographic region with similar manipulation patterns

Evidence Building

Throughout the investigation, the SIU investigator builds a case file that includes:

Forensic evidence:

Detection reports with methodology descriptions
Visual heatmaps with specific manipulation findings
Metadata analysis showing file inconsistencies
Chain-of-custody documentation for all analyzed files

Traditional evidence:

Recorded statements with transcripts (and voice analysis results)
Independent verification results
Physical inspection reports (if conducted)
Database and registry search results
Surveillance reports (if conducted)

Combined analysis:

Timeline reconstruction showing inconsistencies between claimed events and evidence metadata
Cross-referencing of forensic findings with investigation findings
Assessment of the evidence as a whole

Stage 4: Decision

Decision Options

Based on the complete investigation:

1. No fraud confirmed — return to claims. Investigation didn’t confirm fraud. The claim returns to standard processing. All investigation records are retained for pattern analysis and potential future reference.

2. Claim denial — fraud confirmed. Sufficient evidence to deny the claim on the basis of material misrepresentation or fraud. The denial must be supported by:

Specific findings documented in the forensic report
Corroborating investigation evidence
Documentation meeting the insurer’s claims denial standards
Compliance with state/territory regulatory requirements for claim denials

3. Recovery action — pursue the fraudster. For paid claims where fraud is later confirmed, or for high-value fraud justifying civil recovery:

Forensic reports serve as expert evidence
Investigation documentation supports the recovery claim
Evidence meets civil evidence standards (balance of probabilities in Australian and UK jurisdictions; preponderance of evidence in the US)

4. Criminal referral — report to authorities. For serious or organized fraud:

Evidence package assembled for law enforcement
Forensic reports meet criminal evidentiary standards
The Coalition Against Insurance Fraud notes that 43 US states mandate insurer fraud reporting; in Australia, reporting through IFBA and state police is standard practice for serious fraud
The insurer cooperates with law enforcement investigation while maintaining its own civil remedies

5. Regulatory reporting. Regardless of other actions, suspected fraud must be reported to the relevant authority:

US: State fraud bureau (mandatory in 43 states + DC)
Australia: IFBA, state police fraud squads
UK: Insurance Fraud Bureau (IFB), Action Fraud
All jurisdictions: AML reporting through the relevant authority if identity fraud is involved

Stage 5: Resolution and Feedback

Closing the Loop

Regardless of the outcome, the resolution stage generates intelligence that improves future detection:

Investigation outcome feedback. Was the detection finding confirmed by investigation? This feedback is critical:

True positive (confirmed fraud): Validates the detection model. Document the specific manipulation type and generation tool if identified.
False positive (no fraud confirmed): The claim was genuinely legitimate despite triggering detection. Analyze why — was it compression, device artifacts, or a model limitation? Feed this back to the detection vendor for threshold tuning.
True negative (clean detection, no fraud): The system correctly passed a legitimate claim. No action needed.
False negative (fraud detected by other means but missed by detection): If investigation or other triggers identified fraud that media detection missed, this is a critical finding. Provide the missed manipulation to the detection vendor for model improvement.

Pattern documentation. Document the fraud scheme for intelligence sharing:

What generation tools were used (if identified)?
What content was fabricated (photos, documents, audio)?
How was the fabrication detected?
What investigation techniques confirmed the fraud?

Intelligence sharing. Report findings to industry bodies:

NICB (US) / IFBA (Australia) / IFB (UK)
Share manipulation patterns and detection findings (appropriately anonymised)
Contribute to industry-wide fraud intelligence databases

Continuous Improvement

The investigation workflow should generate measurable improvement over time:

Metric	Improvement Indicator
Detection → confirmation rate	Increasing (fewer false positives reaching SIU)
Investigation duration	Decreasing (forensic pre-work reduces investigation time)
Evidence sufficiency at decision	Increasing (better forensic documentation supports faster decisions)
Recovery success rate	Increasing (stronger evidence supports legal action)
Criminal referral acceptance rate	Increasing (evidence meets prosecution standards)

The Transformed Workflow

The complete workflow with AI detection:

Claim Submitted
    │
    ▼
AI Media Detection ← Real-time analysis
    │
    ├─ Clean → Standard Adjuster Review → Settlement
    │
    ├─ Suspicious → Enhanced Adjuster Review
    │       │
    │       ├─ Resolved → Settlement
    │       └─ Escalated → SIU
    │
    └─ High Risk → Direct SIU Referral
            │
            ▼
    SIU Investigation (with forensic intelligence)
            │
            ├─ No fraud confirmed → Return to claims
            ├─ Fraud confirmed → Denial + recovery + reporting
            └─ Serious/organized → Criminal referral + reporting
                    │
                    ▼
            Resolution + Feedback Loop
                    │
                    ├─ Outcome feeds back to detection model
                    ├─ Patterns shared with industry
                    └─ Metrics inform continuous improvement

At every stage, AI detection provides information that wasn’t available before: forensic intelligence that directs the investigation, evidence that supports the decision, and documentation that meets legal and regulatory requirements.

The human investigators remain essential — their judgment, interview skills, and contextual understanding can’t be automated. What AI does is ensure they’re investigating the right claims, starting with the right evidence, and building cases on a forensic foundation.

deetech integrates into every stage of the claims investigation workflow — from automated detection at intake through forensic evidence for legal proceedings. Our platform is designed for the complete lifecycle, not just initial screening. Request a demo.

Sources cited in this article: