AI-Powered File Validation for Insurance
Validate claim and underwriting photos, documents, audio, and video before they influence payout, escalation, underwriting, fraud, or automated decisions.
Why Insurance Needs More Than Deepfake Detection
Not every questionable claim contains a deepfake. And not every underwriting risk involves fraud.
Insurance organizations increasingly depend on digital photos, videos, estimates, invoices, and supporting documents to make claims and underwriting decisions. Generative AI makes it easier to fabricate or manipulate this evidence. But genuine files can also be misleading, inconsistent with policy or claim information, or reveal important risk conditions that weren’t disclosed.
DeepScan helps insurers evaluate submitted files across three critical dimensions:
Is the Evidence Authentic?
Identify signs of AI generation, digital manipulation, suspicious metadata, and previously reused photos or documents.
Does the Information Make Sense?
Validate estimates, invoices, incident details, dates, vehicles, property information, and other content against associated claims and business records.
Does It Meet Business Requirements?
Apply insurer-defined rules for acceptable costs, required documentation, submission deadlines, visible hazards, and other claims or underwriting conditions.
Because reliable insurance decisions depend on more than detecting fake content. They depend on validating the information behind every decision.
One Platform. Two Critical Insurance Workflows.
DeepScan helps insurers strengthen claims validation and underwriting risk assessment using AI-powered analysis of submitted files.
This service provides automated validation and forensic expertise to flag potential fraud, errors and omissions for further review.
APIs enable easy integration with existing applications and large scale analysis, able to provide 10s of millions of validations per month in near real-time.
Claims: Validate Evidence Before It Influences Settlement
Submitted claims evidence can be manipulated, reused, inconsistent, incomplete, or unsupported, even when it appears legitimate.
DeepScan helps claims and SIU teams identify:
AI-generated or digitally altered damage photographs.
Reused images or media from unrelated incidents.
Manipulated repair estimates, invoices, and receipts.
Numerical errors, inflated costs, or missing documentation.
Inconsistencies between submitted evidence and claim details.
Exceptions to insurer-specific claims rules and thresholds.
By evaluating suspicious submissions earlier, DeepScan helps insurers prioritize investigations and support more confident claims decisions.
Underwriting: Identify Risks Beyond Image Fraud
DeepScan analyzes submitted property and vehicle photos to identify visible conditions and compare them with application or policy information. Using configurable business rules, insurers can identify potential risk indicators such as:
Swimming pools and related property features.
Dogs and other animals.
Trampolines and recreational equipment.
Visible roof deterioration or damage.
Discrepancies between photographs and disclosed information and all DeepScan built-in manipulation and AI detection
Underwriting teams can define which objects, conditions, and exceptions matter to their risk appetite and review procedures. DeepScan helps turn submitted photographs into actionable underwriting intelligence, even when the images themselves are completely authentic.
How DeepScan Validates Insurance Files
Submitted Evidence
Photos · Videos · Documents · Audio
Forensic Authenticity
Duplicate & Reuse Checks
Content & Case Cross-Checks
Custom Business Rules
Actionable Validation Results
Proceed · Flag · Request More Information · Escalate
The Business Benefits of AI-Powered Insurance File Validation
Reduce unnecessary review, identify risks earlier, and strengthen claims and underwriting decisions.
Reduce Manual Review
Automatically screen incoming photos, videos, and documents so claims and underwriting teams can focus on suspicious submissions and exceptions.
Reduce Potential Claims Leakage
Identify potentially manipulated evidence, inflated costs, duplicate submissions, and inconsistent information before they influence settlement decisions.
Strengthen Underwriting Risk Assessment
Detect visible hazards and property conditions, compare findings with application information, and apply carrier-specific underwriting rules.
Improve Decision Confidence
Give adjusters, SIU investigators, and underwriters more complete information through forensic findings, contextual validation, and business-rule results.
Support Scalable Automation
Integrate validation into digital intake, claims management, and underwriting workflows without requiring every submission to receive the same level of manual scrutiny.
Calculate your cost savings and ROI
Try our interactive online calculator to discover your potential operational and fraud savings, along with associated ROIs of using Attestiv.
> 10M
Monthly validations
Enterprise APIs
Custom validation rules
Built for the Entire Insurance Workflow
Automated validation through AI analysis
DeepScan can help automate validation during first notice of loss (FNOL), prioritize suspicious submissions for SIU review, and support decisions across the claims lifecycle.
Attestiv’s clear reports, responsive analysts, submitted file validation support, and identification of highly realistic forgeries have enhanced our ability to prevent improper payments while strengthening our claims review process.
It’s important for us to have as much knowledge about an incident as we can while making the process more seamless for our clients. Attestiv is an exciting addition for us in all facets of the claims process.
Insurance FAQ
Attestiv DeepScan™ uses AI-powered forensic analysis, metadata inspection, and file integrity checks to identify potential manipulation in insurance claims evidence.
The platform can detect indicators of AI-generated or digitally altered damage photos, modified repair estimates, suspicious invoices, fabricated documents, and reused media.
DeepScan goes beyond visual analysis by examining file characteristics, document contents, and contextual inconsistencies. Suspicious findings can be flagged for additional review before they influence claims decisions.
Yes. DeepScan can cross-check information extracted from submitted photos, videos, and documents against associated claims, policies, applications, and other available business records.
For example, it can identify discrepancies involving vehicle information, incident dates, damage descriptions, repair costs, property characteristics, and other relevant details.
Insurers can also configure validation rules to identify missing documentation, inconsistent information, or exceptions to established claims and underwriting requirements.
Yes. DeepScan can analyze authentic photographs for visible property conditions and potential underwriting risks, even when there is no evidence of digital manipulation.
Examples include swimming pools, dogs, trampolines, visible roof damage, and other property features or conditions relevant to underwriting.
Insurers can customize which objects, conditions, and risk indicators are evaluated and compare findings against application or policy information.
This allows DeepScan to support underwriting risk assessment in addition to fraud detection.
Yes. DeepScan supports configurable business rules that allow insurers to tailor validation workflows to their specific requirements.
For claims, rules can identify unusual repair costs, missing documentation, inconsistent dates, unexpected amounts, or discrepancies between submitted evidence and claim information.
For underwriting, rules can flag visible hazards, property characteristics, incomplete disclosures, or conditions requiring additional inspection.
These configurable checks help insurers apply consistent validation criteria while directing exceptions to appropriate reviewers.
Yes. DeepScan can compare image contents and available metadata against associated claims, applications, and case records.
For example, it can identify potential discrepancies involving vehicles, property characteristics, visible damage, reported incident details, dates, locations, and other relevant information.
Organizations can also apply custom business rules to flag inconsistencies or exceptions that require further review.
Yes. DeepScan provides enterprise APIs that allow insurers to integrate AI-powered file validation directly into existing claims management, underwriting, digital intake, and fraud investigation workflows.
Organizations can automate analysis of submitted photos, videos, audio, and documents, receive validation findings, and use those results to support downstream decisions or additional review.
DeepScan also provides a web-based dashboard for interactive analysis and investigations.
Attestiv uses encrypted communications and access controls to help protect submitted images and customer information.
Data handling and retention requirements can be reviewed based on the organization’s intended workflow and deployment configuration, with zero data retention configurations available.
Organizations with specific privacy, security, or compliance requirements can evaluate these considerations during integration planning.