Claims Surge Analysis: Detecting Coordinated Deepfake Fraud After Natural Disasters
A technical approach to batch analysis of surge claims — detecting coordinated deepfake fraud through pattern recognition across hundreds of simultaneous.
When a natural disaster strikes, the claims that follow aren’t random. Legitimate claims cluster geographically along the disaster footprint, correlate temporally with the event progression, and distribute across damage severities in patterns that reflect physical reality. Fraudulent claims — particularly coordinated campaigns using AI-generated evidence — deviate from these patterns in detectable ways.
The challenge is detection at scale. A surge event may produce 10,000 to 200,000 claims in days. Individual review is impossible. Manual cross-referencing is impractical. What’s needed is systematic batch analysis that identifies coordinated fraud patterns across the entire claim population.
This article outlines the technical approach to surge claims analysis — the data points, correlation methods, and detection signals that distinguish coordinated deepfake fraud from legitimate catastrophe claims.
The Structure of a Legitimate Surge
Before detecting anomalies, you need to understand the baseline. Legitimate catastrophe claims follow predictable patterns.
Temporal distribution
Claim lodgement after a disaster follows a characteristic curve. There is an initial spike in the first 24 to 72 hours as affected policyholders report damage. This is followed by a sustained high volume for 1 to 2 weeks as the full extent of damage becomes apparent and access is restored. There is then a long tail extending weeks to months as delayed damage (e.g., mould from flooding, subsidence after ground saturation) manifests and is reported.
The Insurance Council of Australia’s catastrophe data shows that approximately 60% of claims are lodged within the first 7 days, 85% within 21 days, and 95% within 60 days. The exact distribution varies by disaster type — bushfire claims tend to lodge faster (damage is immediately visible) while flood claims may extend longer (hidden damage emerges over time).
Geographic distribution
Legitimate claims correlate with the physical disaster footprint. Flood claims concentrate in areas that actually flooded — along waterways, in flood plains, and in low-lying zones. This can be verified against Bureau of Meteorology flood mapping, local government flood studies, and satellite-derived inundation mapping.
Wind damage claims correlate with measured wind speeds from weather stations and modelled wind fields. The relationship between wind speed and damage follows established vulnerability curves — Category 2 cyclone winds produce different damage distributions than Category 4.
Bushfire claims follow the actual burn perimeter, verifiable through satellite hotspot data (Sentinel, MODIS/VIIRS) and state fire agency mapping.
Damage severity distribution
Real damage follows statistical distributions. Not every property in a flood zone sustains the same damage. Severity varies with elevation, construction type, floor height, proximity to waterways, and dozens of other factors. The resulting distribution of claimed amounts follows a roughly log-normal curve — many small-to-moderate claims, fewer large claims, rare total losses.
Claimant demographics
Legitimate claimants are existing policyholders with established histories. They typically have policies predating the event by months or years. Their contact details, banking information, and property details are consistent with their policy records. Their communication patterns (portal logins, phone calls, email correspondence) reflect genuine distress and information-seeking behavior.
Detecting Coordinated Deepfake Fraud
Coordinated fraud deviates from these baselines. The deviations may be subtle in individual claims but become statistically significant when analyzed across the batch.
Signal 1: Temporal anomalies
Coordinated fraud rings often exhibit abnormal temporal patterns.
Late-cluster submissions: While legitimate claims peak in the first week, coordinated fraud may cluster in the second or third week — after the fraud ring has had time to identify the disaster parameters, acquire target property data, generate synthetic evidence, and coordinate submissions. A statistically significant secondary peak in lodgement rates, particularly from claims sharing other correlated features, warrants investigation.
Unnatural regularity: Legitimate claims arrive at irregular intervals reflecting individual policyholder circumstances. Coordinated submissions may show mechanical regularity — claims arriving at consistent intervals suggesting automated or batch submission.
Off-hours clustering: Legitimate claimants submit claims during business hours and evening hours in the affected time zone. Claims arriving in clusters during overnight hours, or from time zones inconsistent with the disaster location, suggest remote operation.
Detection method: Time-series analysis of submission timestamps, segmented by geographic zone and claim characteristics. Apply change-point detection algorithms to identify secondary surges that deviate from the expected decay curve. Flag temporal clusters that share additional correlated attributes (submission channel, document formatting, image characteristics).
Signal 2: Geographic inconsistencies
Coordinated fraud may target properties outside the true disaster footprint, or within the footprint but with claimed damage inconsistent with the actual conditions at that location.
Footprint mismatch: Claims for flood damage at properties above the verified flood level. Claims for wind damage in areas where measured wind speeds were below damaging thresholds. Claims for bushfire damage outside the mapped burn perimeter.
Micro-geographic implausibility: Within a flooded area, damage severity should vary with micro-elevation, distance from waterway, and drainage characteristics. A cluster of claims from a single street all reporting identical maximum-severity flooding — when the actual flood extent varied significantly along that street — indicates fabrication.
Detection method: Geospatial analysis overlaying claim locations on verified disaster impact data. For each claim, compute the expected damage severity based on the property’s location relative to flood mapping, wind field modeling, or fire perimeter data. Flag claims where the reported damage significantly exceeds the location-based expectation. Spatial autocorrelation analysis (Moran’s I, LISA) identifies clusters of anomalous claims.
Signal 3: Image generation signatures
Even when each claim uses uniquely generated images, AI-generated photos share characteristics traceable to their generation method.
Model fingerprints: Different image generation models leave different statistical signatures. Stable Diffusion produces images with characteristic frequency-domain patterns. Midjourney has distinct color distribution tendencies. These fingerprints persist even after post-processing and compression. When multiple claims submitted by ostensibly unrelated claimants contain images generated by the same model with similar parameters, it suggests common origin.
Stylistic clustering: Human photographers have individual styles, but the variation between photographers is high. AI-generated images from the same workflow exhibit lower inter-image variation on specific metrics — color palette distribution, texture frequency characteristics, edge rendering patterns. Cluster analysis across all submitted images can group images by likely generation source.
Damage morphology consistency: Real disaster damage is chaotic. No two damaged properties look the same, even when subjected to identical forces. AI-generated damage, particularly when produced from similar prompts, exhibits unrealistic consistency in damage patterns — similar crack geometries, similar debris distributions, similar water line positions.
Detection method: Extract feature vectors from all submitted images using a combination of forensic analysis networks and perceptual feature extractors. Apply clustering algorithms (DBSCAN, HDBSCAN) to identify groups of images with anomalously high similarity in generation characteristics despite depicting different properties. Cross-reference image clusters with claim submission metadata to identify correlated submissions.
For detailed analysis of AI-generated property damage image signatures, see our technical analysis.
Signal 4: Metadata correlation
Claims from a coordinated ring may share metadata characteristics that legitimate claims wouldn’t.
Digital submission metadata: IP addresses or IP ranges used for portal submissions, browser fingerprints, device identifiers from mobile app submissions, and submission session characteristics (time on form, field completion order, copy-paste patterns).
Document metadata: PDF creation tools, document templates, font usage, formatting characteristics. Fabricated repair quotes generated from the same template will share formatting DNA even when the content differs.
Image metadata: Even manipulated EXIF data may share patterns — the same fictitious device model, GPS coordinates with suspicious precision or rounding patterns, timestamps that cluster unnaturally.
Detection method: Extract metadata features from all claim submissions and associated documents. Build a similarity graph where nodes are claims and edges are weighted by metadata similarity. Apply community detection algorithms (Louvain, Leiden) to identify clusters of claims with unusually high metadata correlation. Legitimate claims from the same geographic area may share some metadata characteristics (similar devices, nearby GPS coordinates), so the threshold must account for expected baseline correlation.
Signal 5: Financial pattern indicators
Coordinated fraud often optimises for insurer-specific financial thresholds.
Threshold clustering: If an insurer fast-tracks claims below $10,000, coordinated fraud will cluster just below that threshold. The natural distribution of damage costs is continuous — artificial clustering at specific values indicates threshold-aware fabrication.
Amount precision: Legitimate claims rarely produce exact round numbers. A claimed loss of exactly $8,000 or $12,500 is less likely than $8,347 or $12,891. Coordinated campaigns generating many claims may default to rounder numbers.
Payment destination patterns: Claims from ostensibly different policyholders directing payments to accounts at the same financial institution, or accounts with similar naming conventions, suggest coordination. This is particularly significant when the payment accounts were recently modified.
Detection method: Statistical analysis of claimed amounts across the surge population. Apply Benford’s Law analysis for first-digit distribution anomalies. Histogram analysis for threshold clustering. Network analysis of payment destinations to identify convergence points.
Signal 6: Network linkages
The most powerful detection signal for coordinated fraud is network analysis — identifying hidden connections between ostensibly independent claims.
Shared contact information: Phone numbers, email addresses (including similar email patterns — firstnamelastname123@provider), postal addresses for correspondence.
Shared service providers: Multiple claims using the same builder, assessor, or contractor for quotes — particularly if that provider is newly registered or has limited trading history.
Shared digital footprint: Same IP addresses for online submissions, same device identifiers for mobile app submissions, linked social media accounts.
Policy relationship patterns: Policies taken out through the same broker, at similar times, with similar coverage levels. Recent policy inception or recent coverage increases preceding the event.
Detection method: Construct a heterogeneous graph with claims, policyholders, contact details, service providers, devices, and financial accounts as nodes. Apply graph analytics — connected components, centrality measures, community detection — to identify clusters of claims with hidden connectivity. A cluster of 20 claims connected through shared phone numbers, a common contractor, and similar IP addresses is strong evidence of coordination.
Implementation Architecture
Surge analysis at scale requires purpose-built infrastructure. The volume, velocity, and variety of data during a catastrophe event demand systems designed for exactly this scenario.
Data ingestion
All claim data — submission metadata, uploaded media, documents, structured form data — must flow into an analysis pipeline in real time. This requires integration with the claims management system (Guidewire, Duck Creek, Sapiens, or equivalent) via event-driven architecture (webhooks, message queues, change data capture).
During surge events, ingestion rates may spike from hundreds to tens of thousands of claims per hour. The pipeline must scale elastically.
Feature extraction
Each claim generates a feature vector encompassing submission metadata features (timing, channel, device, location), image analysis features (deepfake detection scores, perceptual hashes, model fingerprint vectors), document analysis features (template identification, metadata extraction, content analysis), financial features (amount distribution, payment destination characteristics), and policyholder features (policy age, history, contact detail recency).
Feature extraction must operate in near-real-time — ideally within minutes of submission. This enables live monitoring of developing fraud patterns rather than retrospective analysis.
Correlation engine
The core of surge analysis is the correlation engine — computing pairwise and higher-order correlations across the claim population. This is computationally intensive. Comparing 100,000 claims pairwise generates 5 billion pairs. Efficient implementation requires approximate nearest-neighbor algorithms (locality-sensitive hashing, FAISS) for image and document similarity, streaming graph construction for network analysis, spatial indexing (R-tree, geohash) for geographic correlation, and time-series analysis infrastructure for temporal pattern detection.
Alert generation
Correlation findings must translate into actionable intelligence. The output should include individual claim risk scores incorporating all correlation signals, cluster reports identifying groups of related suspicious claims with visualisation of connecting evidence, geographic heat maps showing the relationship between claim density and verified disaster impact, and temporal analysis dashboards showing submission patterns and anomaly flags.
These outputs integrate into the claims management workflow, routing flagged claims to the Special Investigations Unit with supporting evidence pre-assembled.
Calibration: Avoiding False Positives During Disasters
The greatest risk in surge analysis is false positive escalation. During a catastrophe event, legitimate claims genuinely cluster — geographically, temporally, and in damage characteristics. Over-aggressive correlation thresholds will flag legitimate claims, delaying payouts to genuinely affected policyholders.
Baseline calibration
Detection thresholds must be calibrated against the expected characteristics of legitimate surge claims. This requires historical catastrophe data for the same geographic region, disaster-type-specific correlation baselines (floods produce different patterns than cyclones), and insurer-specific claim population characteristics.
Progressive escalation
Rather than binary flag/pass decisions, use progressive escalation. A claim matching one anomaly signal receives enhanced monitoring but continues through normal processing. A claim matching two or three signals receives automated enhanced review. A claim matching four or more signals is escalated to SIU with supporting evidence.
This approach ensures that unusual but legitimate claims — a policyholder who recently changed banks, or who submitted a claim late due to evacuation — aren’t automatically treated as fraudulent.
Feedback integration
Adjuster and investigator outcomes must feed back into the correlation engine. Claims escalated but confirmed legitimate adjust the model’s sensitivity. Claims paid but later identified as fraudulent identify missed signals. This feedback loop is continuous, improving accuracy with each event.
Preparing for the Next Event
Surge analysis capability cannot be built during a disaster. It must be deployed, tested, and calibrated before the event.
Pre-season deployment: In Australia, catastrophe season peaks from November to April (cyclone, flood, bushfire). Detection infrastructure should be deployed and tested by October.
Simulation testing: Use historical catastrophe claim data to simulate surge events and validate detection performance. Inject synthetic fraudulent claims into the simulation to test sensitivity and specificity.
Integration testing: Ensure end-to-end integration with claims management systems under surge load conditions. A detection system that works at normal volumes but fails at 10x throughput provides no protection when it’s most needed.
Runbook development: Document escalation procedures, SIU coordination protocols, and regulatory reporting requirements specific to catastrophe fraud. During the event, there’s no time to design processes from scratch.
Conclusion
Coordinated deepfake fraud during catastrophe events is a systemic threat — not because individual fraudulent claims are undetectable, but because the volume and urgency of legitimate claims creates cover for fraud at scale.
The defense is equally systemic: batch analysis that identifies coordination patterns invisible in individual claim review. Temporal, geographic, visual, metadata, financial, and network signals, correlated across the entire claim population, reveal the structure of coordinated campaigns.
This analysis must be automated, scalable, and pre-deployed. When the next disaster strikes, the detection infrastructure must already be running. The claims will not wait, and neither will the fraudsters.
To learn how deetech helps insurers detect deepfake fraud with purpose-built AI detection, visit our solutions page or request a demo.