Non-health insurance fraud costs billions in losses annually in the US alone, according to FBI estimates. Carrier balance sheets do not absorb that cost; it transfers to policyholders through premium increases, adding to combined annual household costs. This highlights the gap between traditional detection methods and the schemes they are asked to identify.
Why traditional detection methods cannot keep up
Rules-based fraud detection operates on heuristics. It has predefined fraud indicators, where a breach of thresholds triggers investigation, and the model is recalibrated periodically. The structural limitation is that these systems can only assess parameters already identified as relevant. The context-specific patterns that emerge within particular geographies, customer segments, or claim types are invisible to a static rule set that cannot learn from them.
Fraud incidence compounds this further. Across typical insurance claims populations, confirmed fraudulent claims account for a small percentage. The severe class imbalance causes heuristic models to fail in both directions: they generate false positives that consume investigation capacity, and they miss novel fraud patterns that fall outside their configured parameters. Recalibrating these systems to keep pace with evolving fraud behaviour requires sustained manual expertise that most claims operations cannot maintain.
Machine learning in insurance fraud detection
Insurance fraud analytics uses machine learning to address the class imbalance challenge. Rather than operating from fixed rules, these models learn from the statistical characteristics of confirmed fraud cases and apply that learning to new claims at scale. Feature engineering is the critical technical determinant of model performance. By combining raw data elements, such as the relationship between claim date and policy alteration date, witness count, vehicle age, and police notification status, into signals, it distinguishes fraud patterns from genuine claims.
Research across multiple insurance datasets consistently shows that ensemble classifiers, which aggregate the outputs of multiple models rather than relying on a single one, deliver the strongest performance. Adjusted random forest and modified random undersampling approaches outperform single-classifier methods on both precision and recall. Real-time fraud scoring flags suspicious claims at intake and routes them for specialist review, while legitimate claims proceed through automated workflows.
The synthetic media threat
The emergence of generative AI as an accessible tool has changed the character of insurance claims fraud analytics significantly. Research suggests that insurance claims may now contain altered images, fabricated documentation, or synthetic medical reports. A large number of insurance executives have identified fraud detection as a leading priority for generative AI investment, in response to this threat.
Three forms of synthetic media fraud are most prevalent.
- Deepfakes: AI generates convincing audio or video impersonations such as fabricated witness statements or event footage.
- Shallow fakes: Simpler manipulation techniques, like image cropping, splicing, or reuse of photographs from prior claims, alter evidence that adjusters are unlikely to cross-reference manually.
- Fully synthetic media: Generative AI tools produce fabricated images, voices, and identities that older verification processes were not designed to identify.
Precision detection and the data imperative
Multimodal forensic analysis is the technical backbone for synthetic media detection. Multimodal detection with AI models evaluates image, video, audio, and text simultaneously, flagging inconsistencies in compression artefacts, lighting, sound patterns, and metadata of each claim. Content provenance verification, which validates where and how submitted media was captured, creates a tamper-resistant chain of custody that forensic analysis alone cannot produce.
Behavioural analytics
Structured claims data, device patterns, IP addresses, timestamps, and vendor histories are monitored for anomalies that, when reviewed alongside media forensics, expose coordinated fraud rings and synthetic identity schemes that individual claim review would miss.
Data quality
Data quality determines the efficacy of these functions. Research across insurance fraud datasets confirms that predictive accuracy depends more on data quality than on algorithm choice. A well-engineered feature set applied to clean, complete data consistently outperforms sophisticated models applied to incomplete or inconsistently structured inputs. This dependency applies to machine learning models and generative AI detection tools equally.
Challenges and ethical considerations
AI-powered fraud detection introduces governance obligations alongside its operational benefits. Detection models trained on historical data can encode existing biases into scoring systems, producing elevated false positive rates for specific customer segments. Ensuring that models are tested for fairness and that their outputs are explainable to regulators and customers when claims decisions are challenged is essential.
Data privacy obligations vary significantly across jurisdictions. Systems processing biometric verification, health records, or behavioural tracking data must comply with GDPR, CCPA, PIPEDA, and other applicable regional standards. Insurance carriers building detection infrastructure across multiple markets must embed regulatory compliance into the architecture from the outset.
How can Infosys BPM help with insurance claims fraud detection?
Through its fraud analytics and modelling capabilities, Infosys BPM helps insurance organisations build and operate fraud detection infrastructure spanning machine learning model development, synthetic media verification, and the governance controls that sustain regulatory confidence across jurisdictions and lines of business.
Frequently asked questions
The value comes from catching more genuine fraud while reducing wasted investigation effort. Non-health insurance fraud exceeds 40 billion dollars a year in the US, costs that pass to policyholders as higher premiums. By improving detection precision and automating legitimate claims, AI reduces leakage, lowers investigation cost per claim, and protects both loss ratios and customer trust.
Fraud detection is an ongoing contest, not a solved problem. As generative AI makes synthetic media cheaper, fraudsters adapt, so detection models must be retrained continuously on new confirmed cases. This is why machine learning outperforms static rules: it learns evolving patterns rather than relying on fixed indicators, though it still requires sustained monitoring and model governance.
No. AI handles scale and triage, scoring claims at intake and routing suspicious ones for review, while legitimate claims proceed automatically. Investigators focus on complex, high-value, and contested cases where judgement matters. AI also surfaces coordinated fraud rings that manual review would miss, so the model shifts investigator time toward higher-impact work rather than removing it.
By learning the statistical characteristics of confirmed fraud rather than matching fixed rules, machine learning can flag claims that deviate from genuine behaviour even when the exact scheme is new. Behavioural analytics on device patterns, IP addresses, and vendor histories expose anomalies across claims, catching emerging synthetic identity and fraud-ring tactics that static rule sets would miss.
Explainability must be built into the model, not added later. When a claim decision is challenged, insurers need to show which factors drove the fraud score and defend it to regulators and policyholders. This requires models tested for fairness, transparent feature logic, and documented reasoning, since opaque scoring creates both regulatory and reputational risk.
Clean, complete, well-structured claims data matters more than the algorithm. Insurers need historical confirmed-fraud cases to train on, plus structured claim attributes, media evidence, device and vendor data, and consistent labelling. A well-engineered feature set on reliable data outperforms sophisticated models on incomplete inputs, so data quality is the first investment, not the model itself.
