Real-Time vs Batch Deepfake Detection: What's Right for Your Claims Process?

When integrating AI-powered deepfake detection into your claims workflow, one of the first architectural decisions is timing: do you analyze media in real time (as claims are submitted) or in batch (periodically, after claims have entered the system)?

The answer isn’t one or the other. It’s both — but with different roles.

Real-Time Detection: Analysis at Intake

How It Works

Media files are submitted with a claim → the detection API is called immediately → results are returned before the claim reaches an adjuster.

Claimant submits photo → Detection API (asynchronous) → Result attached to claim → Adjuster sees claim + forensic context

Typical latency: images often return in seconds; video is asynchronous and typically takes minutes, depending on file size and analysis depth.

Advantages

Catches fraud before payout. The highest-value benefit. Manipulated evidence is flagged before any payment decision is made. This prevents the scenario where fraud is detected after the claim is already paid — at which point recovery is expensive and uncertain.

Provides adjuster context. When the adjuster opens a claim, forensic analysis results are already attached. If the photos are clean, the adjuster processes normally — zero friction. If manipulation is detected, the adjuster has forensic intelligence from the start: heatmaps, confidence levels, specific findings.

Reduces investigation cost. Catching fraud at intake is cheaper than catching it during investigation. Early detection avoids the costs of adjuster time, reserve allocation, and SIU investigation on claims that should never have progressed.

Enables automated routing. Claims can be automatically routed based on detection results: clean claims to standard processing, flagged claims to senior adjusters or directly to SIU. This optimizes human resources without manual triage.

Limitations

Latency constraints. Real-time analysis must complete fast enough not to delay the claims process. For images, this is typically achievable (seconds). For long videos or large document packages, real-time analysis may take minutes — acceptable for most workflows but potentially problematic for instant-decision processes.

Depth trade-offs. Faster analysis may mean shallower analysis. Real-time detection typically runs the standard detection pipeline; the deepest forensic analysis (which may take longer) is reserved for flagged claims that warrant additional scrutiny.

Infrastructure requirements. Real-time detection requires always-on API availability with low latency. During catastrophe events, when claims volumes spike dramatically, intake integrations need queuing and retry logic, and capacity should be agreed with your vendor in advance.

Best For

• FNOL (First Notice of Loss) screening — the highest-value integration point
• Mobile app submissions — photos taken and uploaded in real time
• Digital-first claims — web portal and app-based claims where speed matters
• High-volume, lower-value claims — auto glass, minor collision, routine property — where manual review time is limited

Batch Detection: Periodic Analysis

How It Works

Claims are ingested and stored normally → at scheduled intervals (hourly, daily, weekly), accumulated media files are submitted to the detection API in bulk → results are written back to claim records and alerts generated for flagged claims.

Claims accumulate → Scheduled batch job → Bulk analysis → Results written to claims → Alerts for flagged items

Current request limits: deetech accepts up to 20 files per batch request on non-Enterprise plans and up to 100 on Enterprise, for images, audio, and documents. Video is submitted through the asynchronous video endpoint rather than the batch API. Larger portfolios are orchestrated as multiple bounded requests under agreed capacity; they are not submitted as one thousands-of-files batch.

Advantages

Deeper analysis. Without real-time latency constraints, batch processing can apply more thorough analysis: additional detection layers, higher-resolution frequency domain analysis, more extensive cross-referencing against historical claims.

Historical re-analysis. When a new fraud pattern is identified — or when detection models are updated with new capabilities — batch processing can re-analyze historical claims that were processed before the new capability existed. This catches fraud that was undetectable at the time of original submission.

Cross-claim pattern review. Batch processing enables review across claims, not just within a single claim: surfacing the same or similar photos across different claims and patterns in metadata across submissions for an investigator to assess. A similarity match is a lead for review, not a fraud finding.

Resource efficiency. Batch jobs can run during off-peak hours, using infrastructure that would otherwise be idle. This reduces cost compared to maintaining always-on real-time capacity for peak loads.

Simpler integration. Batch processing requires less sophisticated integration than real-time — a scheduled job that reads from a file store or database, rather than event-driven API calls triggered by claim events.

Limitations

Delayed detection. Fraud is identified hours or days after submission — potentially after the claim has already been assessed or even paid. Recovery from a paid fraudulent claim is significantly more expensive than preventing payment in the first place.

No adjuster context at review. If the adjuster reviews the claim before batch analysis completes, they make their assessment without forensic intelligence. The detection results arrive after the fact.

Alert fatigue risk. Batch analysis of historical claims may generate a large volume of alerts simultaneously, overwhelming SIU capacity. Results need to be prioritized and released in manageable volumes.

Best For

• Historical claims review — re-analyzing past claims when new detection capabilities are available
• Portfolio-level fraud assessment — understanding your overall fraud exposure
• Cross-claim pattern review — surfacing recycled images and clusters of similar submissions for investigators
• Catastrophe event backlog — processing the surge of claims that accumulated during a CAT event
• Model validation — testing new detection models against historical claims before deploying in real time

The Hybrid Approach: Both, With Different Roles

The optimal architecture uses real-time and batch detection for complementary purposes:

Real-Time Layer

• Runs on every new claim at the point of media submission
• Applies the standard detection pipeline (pixel forensics, frequency analysis, metadata verification)
• Returns results in seconds to minutes
• Provides adjuster context before claim review
• Triggers automatic routing for flagged claims
• Priority: speed and coverage

Batch Layer

• Runs periodically (daily or weekly) on accumulated claims
• Applies deeper analysis with additional detection layers
• Performs cross-claim review (image similarity and metadata patterns)
• Re-analyzes historical claims when models are updated
• Generates portfolio-level reports for management and compliance
• Priority: depth and pattern detection

Architecture

Claims Intake
    │
    ├─── Real-Time Detection ──→ Adjuster view + routing
    │    (every claim, seconds)
    │
    └─── Claim stored in system
              │
              └─── Batch Detection ──→ Deep analysis + cross-claim patterns
                   (scheduled, deeper)        │
                                              └─→ SIU alerts for newly identified fraud
                                              └─→ Portfolio reports
                                              └─→ Historical re-analysis results

Decision Matrix

Factor	Real-Time Priority	Batch Priority
Claims volume > 50,000/year	✅ Essential	✅ Essential
Claims volume < 10,000/year	✅ Recommended	Optional
Digital-first submission	✅ Essential	✅ Complementary
Paper/manual submission	Optional	✅ Primary
CAT event exposure	✅ Essential (burst capacity)	✅ Essential (backlog processing)
Historical fraud concern	N/A	✅ Essential
SIU capacity limited	✅ (reduces SIU load)	⚠️ (may increase SIU load)
Budget constrained	✅ Start here	Add when budget allows

Implementation Priorities

If You Can Only Start With One

Start with real-time. Stopping a fraudulent payout at intake is worth more than identifying it after the fact. Every fraudulent claim caught before payment is the full claim value saved. Every fraudulent claim caught after payment requires a recovery process that may recoup only a fraction of the loss.

Adding Batch Later

Once real-time detection is operational:

1. Week 1-2: Implement daily batch analysis on claims from the past 90 days
2. Week 3-4: Add image-similarity review across your stored claim media
3. Month 2: Extend historical analysis to past 12 months
4. Ongoing: Re-run batch analysis whenever detection models are updated

Scaling for CAT Events

Catastrophe events require special handling:

• Real-time capacity must absorb bursts of several times normal volume — agree the capacity and queuing behavior with your vendor before the season starts
• Batch processing should be accelerated during CAT periods (run multiple times daily instead of once)
• Detection thresholds may need temporary adjustment — CAT events generate unusual claims patterns that could increase false positives if thresholds aren’t calibrated

The Coalition Against Insurance Fraud estimates that fraud occurs in about 10% of P&C losses. After catastrophes, high claim volumes and pressure to pay quickly create openings for opportunistic fraud — which is when screening at intake matters most.

Measuring Effectiveness

Real-Time Metrics

Illustrative targets — assumptions to agree during a pilot, not measured deetech outcomes.

Metric	Target
Coverage (% of claims analyzed at intake)	> 99%
Analysis latency (images)	< 30 seconds
Analysis latency (video)	Minutes, asynchronous
False-positive rate	Agreed during your pilot
System availability	Agreed with your vendor

Batch Metrics

Metric	Target
Processing throughput	All accumulated claims within scheduled window
Cross-claim matches identified	Tracked monthly
Historical fraud identified	Tracked per re-analysis cycle
Pattern detection alerts (actionable)	Conversion rate to confirmed fraud

Combined Metrics

Metric	Target
Modeled losses avoided (real-time + batch)	Tracked quarterly
SIU conversion rate on detection alerts	Increase over baseline

Related Reading

• API Integration Guide
• Claims Management
• Enterprise Detection

---

deetech™‘s detection API is asynchronous: submit media, then poll for status or receive a signed webhook. Images often return in seconds; video, large documents, and deeper workflows can take minutes. Larger portfolios use bounded batch requests under agreed capacity. Request a demo to discuss the right architecture for your claims volume.

Sources cited in this article:

• Coalition Against Insurance Fraud — Fraud Statistics