AI-supported fraud detection and plausibility checks
Automated fraud detection for document-based processes
AI-supported fraud detection is an automated control procedure for the early identification of manipulations, anomalies, and fraud patterns in document-based processes. It combines rule-based plausibility checks with self-learning AI models and enables companies to perform continuous, audit-proof risk analysis in real time.
What is AI-supported fraud detection?
AI-supported fraud detection is the automated analysis of structured and unstructured data to identify fraud patterns, manipulations, and irregularities in document-based processes. Data such as:
- Documents
- Images
- Metadata
- Process information
- historical comparative values
are continuously evaluated. Unlike manual checks, the analysis is complete, objective, traceable, and in real time.
How does AI-based fraud detection work?
The solution combines several levels of analysis:
- Rule-based plausibility checks: Technically defined check rules, threshold values, and dependencies between data points.
- AI-supported anomaly detection: Machine learning models identify complex patterns and statistical anomalies.
- Image and metadata analysis: Detection of manipulations, EXIF evaluation, time and location validation.
- Reference and benchmark comparisons: Comparison with historical data, market values, and internal standards.
- Continuous learning: The models improve through confirmed cases of fraud and normal cases.
The result: dynamic, self-optimizing fraud detection. This allows you to automate plausibility checks, identify anomalies in real time, and create an audit-proof control instance that is scalable and can be integrated into your existing system landscape.
In complex or particularly sensitive cases, our experienced specialists from traditional fraud investigation provide support. This results in an integrated approach combining technology and human expertise.
Typical fraud patterns in automotive, financial, and insurance processes
AI-supported fraud investigation detects, among other things:
- Manipulated reports or invoices
- Multiple submissions of identical documents
- Inconsistencies between document content and metadata
- Unusual claims or assessment histories
- Implausible time or location details
- Deviations from reference values
These anomalies often go undetected during manual checks.
Areas of application for AI-supported fraud detection
- Damage reports
- Vehicle valuations
- Lease returns
- Contract and application review
- Invoice and receipt verification
- Bonus and incentive settlements
- All types of document-based review processes
- Technology-supported analysis with operational supplementation
AI and traditional fraud investigation combined in a meaningful way
Automated plausibility checks and machine learning models detect anomalies quickly, objectively, and scalably. Today, a large proportion of document-based fraud cases can be solved entirely on the basis of data. Clear manipulations, inconsistencies, multiple submissions, or implausible metadata can be identified and evaluated automatically without additional manual effort.
AI really comes into its own in standardized, high-volume processes:
- Full checks instead of random sampling
- Immediate risk assessment
- Automatic prioritization
- Scalable decision-making logic
At the same time, there are cases that go beyond purely data-based patterns. Complex issues, organized fraud systems, or legally contentious cases require:
- Experience in fact assessment
- Investigative expertise
- Industry-specific know-how
- Operational research
That is why we take a hybrid approach: automated review for data-based cases and targeted expert support for complex situations.
Our expertise in fraud investigation
For many years, EXCON has been supporting companies in the professional investigation of fraud cases. Our services include, among other things:
- Fact-finding in suspected cases
- On-site research and investigations
- Background checks
- Document and plausibility analyses
- Support in recourse and recovery processes
In the area of insurance fraud, complex situations often arise in which data-based analysis and operational experience can be combined in a meaningful way.
The most common use cases include:
Suspected auto insurance fraud
- Staged vehicle theft
- Manipulated accident damage
- Previous damage reported as current damage
- Implausible damage scenarios
AI recognizes, among other things:
- Unusual damage histories
- Inconsistencies in time, location, and metadata
- Deviations from reference values
- Multiple reports
In complex situations, our specialists take over the in-depth examination of the facts.
Occupational disability while continuing to work
A classic risk area in personal insurance. Automated analysis can:
- Detect discrepancies between reported limitations and documented activity
- Identify temporal inconsistencies
- Compare document patterns
In disputed cases, our experienced investigators provide support with further examination.
Suspicious fire damage or burglary reports
Typical indicators in this area include:
- Implausible times of damage
- Conspicuous damage histories
- Inconsistent witness statements
- Conspicuous economic situations
AI-based plausibility checks identify patterns and anomalies at an early stage. Operational investigations secure evidence and facts as needed.
Multiple billing of repair or medical costs
This is where data-based fraud detection shows its particular strength through targeted:
- Duplicate checks
- Invoice similarity analysis
- Pattern recognition for billing amounts
- Comparison with historical reference values
Many of these cases can be solved completely automatically.
Manipulated damage documentation for household or property insurance
For example:
- Retrospectively altered images
- Manipulated EXIF data
- Excessive damage values
- Recurring document structures
AI reliably detects technical and content anomalies. In complex or escalating cases, a supplementary operational review is carried out.
Why the hybrid approach is particularly useful in insurance fraud
A significant proportion of insurance fraud cases can now be analyzed on the basis of data and evaluated automatically. At the same time, there are situations in which:
- Legal assessment is necessary
- External investigations are required
- High claims amounts are involved
- Organized fraud systems are in place
This is where human fraud investigation complements AI-supported analysis. The result is a tiered review process that ranges from automated full reviews and risk scoring to targeted expert intervention when necessary.
Advantages for companies at a glance
| Operational | Financial | Governance and compliance |
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Seamless integration into your process landscape
The solution from our technology partner MotionsCloud has a modular structure and can be integrated into:
- Existing document management systems
- Claims or contract platforms
- ERP or workflow systems
- Compliance and audit environments
All review steps, scores, and decisions are fully documented.
Secure data processing for AI-supported fraud detection
AI-supported fraud detection is operated on a platform developed in-house by our technology partner MotionsCloud, whose development, operation, and data storage take place entirely within Europe. Sensitive document, contract, and claims data is not transferred to cloud providers in the US or China. Processing is carried out in accordance with the GDPR and in compliance with the EU AI Regulation.
In collaboration with our technology partner MotionsCloud, we integrate AI-supported image, document, and anomaly analyses into an audit-proof architecture with clear access concepts, encryption, and fully traceable decision logic.
Test AI-supported fraud detection: Request a demo now
Automated fraud detection does not replace investigators, but it complements their processes in a targeted and effective manner. Arrange a no-obligation demo with our experts now.
Frequently asked questions about AI-supported fraud detection
Does AI replace human fraud investigators?
No. AI prioritizes and analyzes data sets efficiently. The final assessment of complex cases is still carried out by our specialists in EXCON fraud investigation.
Is fraud detection audit-proof?
Yes. All audit decisions are documented and are auditable and traceable.
Can the solution be integrated into existing systems?
Yes. The architecture is modular and API-enabled.
How quickly does AI detect anomalies?
In real time or immediately after documents are received, not just during periodic audits.
