Insurance fraud detection increasingly depends on connecting claims, policyholder, provider, and transaction records rather than reviewing each event in isolation. Insurers use predictive models, graph analytics, and automated alerts to identify suspicious patterns while routing legitimate claims through normal workflows. Some providers sell dedicated fraud platforms; others build the underlying software and data integrations that make insurer-specific detection possible.
1. Itexus
Itexus develops custom financial and insurance software, including systems for fraud analysis. Its insurance development offering covers transaction and claims monitoring, AI and machine learning to flag suspicious activity, and tools for screening vendors or claims before further investigation. This is a development-led option for insurers that need technology fitted around their existing processes and data.
The relevant work also sits within a broader fintech engineering practice spanning digital platforms, integrations, data-intensive workflows, and modernization. Insurers planning a tailored detection application can combine fraud rules, analytical models, review queues, and reporting within the software they already operate.
Key Facts
- Best for: Custom-built claims and transaction fraud analysis software
- Core services: Insurance fraud detection, insurance software development, AI and machine learning, systems integration
- Specialization: Financial technology and tailored insurer workflows
- Headquarters: Dover, DE, USA
- Founded: 2013
- Team size: 150+ engineers
Contact Information
- Website: itexus.com
- Email: moc.suxeti@ofni
- LinkedIn: www.linkedin.com/company/itexus
- Facebook: www.facebook.com/itexus
- Twitter: x.com/ItexusSoft
- Instagram: www.instagram.com/itexus.soft
- Address: 8, The Green, STE road, Dover, DE 19901, United States
2. Quantexa
Quantexa offers a decision intelligence platform with insurance fraud solutions for application and underwriting fraud, claims, billing and provider fraud, internal fraud and complex investigations. The platform ingests internal data, enriches it with external sources such as corporate registries and industry claims data, and uses entity resolution and graph analytics to connect customers, claimants, providers, assets and organizations.
Fraud scorecards run on that connected data, and investigators work in tools that include Q Assist, a generative AI copilot. Quantexa supports real-time decisions during claims, underwriting and payment approval as well as batch analytics for investigations. It suits insurers whose fraud signals are scattered across many systems.
Key Facts
- Best for: Network-based fraud detection across the insurance lifecycle
- Core services: Entity resolution, graph analytics, fraud scoring, investigation tools
- Specialization: Claims, underwriting, provider and internal insurance fraud
- Headquarters: London, UK
- Founded: 2016
- Team size: 501–1,000 employees
Contact Information
- Website: quantexa.com
- Address: 10 York Road, London SE1 7ND, United Kingdom
- LinkedIn: www.linkedin.com/company/quantexa
3. Owl
Owl builds AI tools that read and analyze insurance claim files, with special investigations units listed among its use cases alongside claims, legal and underwriting teams. Its vision-language model processes legal and medical records, photos and handwritten notes, and links dispersed data about a claimant into one account, which helps investigators spot facts the file alone does not show.
The product set includes OwlVision for document reading, OwlSignal for eligibility insights, OwlAssist for asking questions about a claim file and OwlEnrich for adding outside context on claimants. Owl suits life, health and disability insurers reviewing complex, document-heavy claims.
Key Facts
- Best for: Life, health and disability insurers with document-heavy claims
- Core services: Claim document reading, eligibility insights, claimant data enrichment
- Specialization: AI claim file analysis for claims and SIU teams
- Headquarters: Vancouver, Canada
- Founded: 2018
- Team size: 51–200 employees
Contact Information
- Website: owl.co
4. Shift Technology
Shift Technology builds AI software for insurers, with fraud and risk solutions that analyze claims information and support investigators. Its technology surfaces suspicious patterns, helps prioritize cases, and brings information relevant to a claim into the review process. The focus is operational insurance fraud detection rather than general-purpose financial crime monitoring.
The platform combines automated screening with investigation support, including the context specialists need to distinguish unusual but legitimate claims from cases warranting deeper attention. It suits insurance teams seeking a dedicated product across fraud detection and claims investigation.
Key Facts
- Best for: Insurance fraud screening and investigation workflows
- Core services: AI-based fraud detection, risk analysis, investigative assistance
- Specialization: Insurance claims and fraud operations
- Headquarters: Paris, France
- Team size: 501–1,000 employees
Contact Information
- Website: www.shift-technology.com
- Email: moc.ygolonhcet-tfihs@gnitekram
- LinkedIn: www.linkedin.com/company/shift-technology
- Address: 14 Rue Gerty Archimède, 75012 Paris, France
5. FRISS
FRISS supplies fraud, risk, and compliance software for property and casualty insurers. The platform supports fraud checks during underwriting and claims handling and gives investigation teams tools for reviewing alerts and examining relevant evidence. Its products are built around insurance workflows rather than requiring fraud analysts to adapt a generic detection suite.
A key consideration is where insurers want risk assessment to happen. FRISS covers both policy acceptance and the claims lifecycle, allowing suspicious behavior to be addressed before a policy is issued or a payment proceeds. This makes it a practical product option for insurers seeking a consistent view of fraud risk.
Key Facts
- Best for: P&C underwriting and claims fraud screening
- Core services: Fraud detection, risk assessment, investigation support
- Specialization: Insurance-focused risk and compliance technology
- Headquarters: Utrecht, Netherlands
- Founded: 2006
- Team size: 201–500 employees
Contact Information
- Website: www.friss.com
- LinkedIn: www.linkedin.com/company/friss-software
- Address: 4700 Duke Drive, Suite 170B, Mason, OH 45040, United States of America
6. CLARA Analytics
CLARA Analytics uses artificial intelligence to improve casualty insurance claims decisions. Its CLARA Fraud product examines the relationships among claimants, medical providers, and attorneys, using claim activity and billing information to identify potentially suspicious behavior. The system generates fraud-risk indicators and information for special investigation unit referrals.
The product also supports ongoing monitoring as cases evolve, rather than treating an initial review as the final screening event. Its focus on workers' compensation and casualty claims is useful for claims organizations working with complex provider and litigation networks.
Key Facts
- Best for: Workers' compensation and casualty claims investigations
- Core services: CLARA Fraud, fraud scoring, SIU referral support
- Headquarters: Santa Clara, CA, USA
- Founded: 2017
- Team size: 51–200 employees
Contact Information
- Website: claraanalytics.com
- Twitter: x.com/claraanalytics
- LinkedIn: www.linkedin.com/company/clara-analytics
- Address: 440 North Wolfe Road, Sunnyvale, CA, 94085, USA
7. SAS
SAS offers analytics and fraud decisioning technology used across regulated sectors, including insurance. Its insurance-relevant solutions address suspicious claims, network patterns, and investigation workflows through statistical analysis, machine learning, business rules, and case management. The technology supports more than a single fraud score: it helps organizations turn multiple risk signals into decisions.
SAS is suited to larger insurance operations that need fraud detection governed alongside wider analytics practices. Its approach gives fraud teams tools for model management, investigation prioritization, and integration with substantial enterprise data environments.
Key Facts
- Best for: Enterprise fraud analytics and case management
- Core services: Fraud decisioning, machine learning, analytics, investigative workflows
- Industries: Insurance and other regulated financial services
- Headquarters: Cary, NC, USA
- Founded: 1976
- Team size: 10,001+ employees
Contact Information
- Website: www.sas.com
- Email: moc.sas@troppus
- Facebook: www.facebook.com/SASsoftware
- Twitter: x.com/SASsoftware
- LinkedIn: www.linkedin.com/company/sas
- Address: 100 SAS Campus Drive Cary, NC 27513-2414, USA
- Phone: +1-800-727-0025
8. Tata Consultancy Services (TCS)
Tata Consultancy Services combines insurance technology, business analytics, and enterprise implementation. Its Customer Intelligence & Insights offering includes insurance use cases for claims segmentation, fraud and risk management, and data-driven decision-making. TCS has also developed dedicated analytics for detecting suspicious claims in public benefit and insurance-related programs.
Its scale and integration capabilities matter when detection needs to operate across policy, claims, identity, and historical transaction data. The company can support insurers seeking to develop connected data environments and apply analytical models within broader transformation programs.
Key Facts
- Best for: Large insurance data and fraud analytics programs
- Core services: Insurance IT services, predictive analytics, AI integration
- Specialization: Enterprise-scale financial services technology
- Headquarters: Mumbai, India
- Founded: 1968
- Team size: 10,001+ employees
Contact Information
- Website: www.tcs.com
- Email: moc.sct@gnitekram.aidni
- Instagram: www.instagram.com/tcsglobal
- LinkedIn: www.linkedin.com/company/tata-consultancy-services
- Twitter: x.com/TCS
- Facebook: www.facebook.com/TataConsultancyServices
- Address: 9th Floor, Nirmal Building, Nariman Point, Mumbai 400 021, India
- Phone: +91 22 6778 9595
9. OSKI Solutions
OSKI Solutions delivers custom software, AI development, and cloud engineering. Its AI practice covers machine learning pipelines, prediction, classification, and the integration of AI-driven services into working applications. The company also develops data systems and business platforms that handle complex information flows.
In insurance fraud detection projects, those capabilities connect to the engineering work behind data ingestion, anomaly models, automated review steps, and investigator-facing tools. Its offering is relevant to organizations commissioning tailored software and keeping control of their own fraud detection workflow.
Key Facts
- Best for: Bespoke AI-enabled applications and cloud platforms
- Core services: AI development, custom software, data engineering, cloud development
- Specialization: Integration of predictive models into business workflows
- Headquarters: Tallinn, Estonia
- Team size: 51–200 employees
Contact Information
- Website: oski.site
- Email: etis.ikso@tcatnoc
- LinkedIn: www.linkedin.com/company/oski-solutions
10. Verisk
Verisk provides data, analytics, and technology for the insurance industry. Its claims information and fraud solutions help insurers examine prior loss information, identify potentially duplicated or questionable activity, and improve investigative decisions. Broad industry datasets are central to this approach because a suspicious pattern may not be visible within one insurer's records alone.
The company's claims-related products serve organizations that want more context during claim handling and special investigations. The emphasis is on shared intelligence, screening, and analytical support for insurer operations rather than standalone custom software engineering.
Key Facts
- Best for: Industry claims intelligence and fraud investigation support
- Core services: Insurance data, claims analytics, fraud detection tools
- Specialization: Property and casualty insurance information
- Headquarters: Jersey City, NJ, USA
- Founded: 1971
- Team size: 5,001–10,000 employees
Contact Information
- Website: www.verisk.com
- Facebook: www.facebook.com/VeriskAnalytics
- Twitter: x.com/verisk
- LinkedIn: www.linkedin.com/company/verisk-analytics
- Address: 545 Washington Boulevard, Jersey City, NJ 07310-1686
- Phone: 800 48977489
11. DataWalk
DataWalk develops graph-based analytics and investigation software for organizations dealing with connected fraud. Its insurance fraud offering links information from claims platforms, customer records, external datasets, and other sources so investigators can examine relationships and detect organized schemes. The platform also supports configurable scores and analytical methods.
Graph analysis is valuable when separate claims share addresses, participants, providers, vehicles, or other identifiers. DataWalk focuses on making these relationships visible and searchable, allowing teams to explore suspicious networks and adapt their investigation logic as new patterns emerge.
Key Facts
- Best for: Organized fraud and connected-claims investigation
- Core services: Graph analytics, fraud intelligence, entity relationships, investigations
- Specialization: Complex and network-based fraud detection
- Headquarters: Redwood City, CA, USA
- Team size: 51–200 employees
Contact Information
- Website: datawalk.com
- Email: moc.klawatad@ofni
- Facebook: www.facebook.com/datawalkplatform
- Twitter: x.com/DataWalk
- LinkedIn: www.linkedin.com/company/datawalk
- Address: 2000 Broadway Street, Suite 232, Redwood City, CA 94063
- Phone: 800.475.8139
12. Sprout.ai
Sprout.ai develops insurance-focused AI for claims processing and fraud detection. Its platform uses information from claims and supporting documents to identify anomalies and unusual patterns, flagging potential fraud, waste, or abuse for closer examination. These insights help claims teams prioritize suspicious cases while automating routine work.
The technology brings fraud screening into the broader claims workflow, alongside data extraction, decision support, and automated processing. Sprout.ai works with insurers, service providers, and managing general agents across health, motor, property, and commercial insurance. Its emphasis on insurance-trained models and explainable decisions is relevant to teams seeking integrated claims intelligence.
Key Facts
- Best for: Insurers seeking AI-driven claims fraud screening
- Core services: Fraud, waste and abuse detection, claims automation, AI decision support
- Specialization: Insurance-trained AI and claims workflows
- Headquarters: London, UK
- Founded: 2018
- Team size: 11–50 employees
Contact Information
- Website: sprout.ai
- LinkedIn: www.linkedin.com/company/sproutai
- Address: 1 Mark Square, London, EC2A 4EG, United Kingdom
13. A-listware
A-listware supplies software development teams, technical specialists, and IT engineering services. Its work includes data analytics, AI and machine learning capabilities, custom application development, testing, and infrastructure support. These disciplines support insurers that need to extend the engineering capacity behind fraud detection systems.
Instead of deploying a ready-made claims fraud product, the firm provides development resources for building and maintaining business software. This is especially relevant where a carrier already has fraud rules or a model strategy and needs engineering capacity to integrate data, build investigator screens, and maintain operational systems.
Key Facts
- Best for: Dedicated engineering capacity for fraud analytics platforms
- Core services: Software development, AI and ML engineering, team augmentation, QA
- Specialization: Outsourced product and enterprise software teams
- Headquarters: London, UK
Contact Information
- Website: a-listware.com
- Email: moc.erawtsil-a@ofni
- Facebook: www.facebook.com/alistware
- LinkedIn: www.linkedin.com/company/a-listware
- Address: St. Leonards-On-Sea, TN37 7TA, UK
- Phone: +44 (0)142 439 01 40
14. Gradient AI
Gradient AI supplies insurance-focused predictive analytics for underwriting and claims. Its platform draws on insurance data to help organizations assess risk, identify costly or unusual claim patterns, and improve claims management decisions. It serves health, property and casualty, and workers' compensation markets.
Fraud detection is part of the broader value of these risk signals: suspicious activity can be highlighted when patterns depart from expected outcomes. For insurers, the company is particularly relevant when fraud-related review is closely connected to claims severity analysis, early intervention, and wider portfolio risk management.
Key Facts
- Best for: Predictive insurance risk and claims analytics
- Core services: AI-based underwriting insights, claims analysis, risk signals
- Headquarters: Boston, MA, USA
- Founded: 2018
- Team size: 51–200 employees
Contact Information
- Website: www.gradientai.com
- Email: moc.iatneidarg@ofni
- LinkedIn: www.linkedin.com/company/gradientai
- Address: 1 Marina Park Drive, Suite 305, Boston, MA 02210
- Phone: 1-888-958-5846
Comparison Table
| Company | Best for | Headquarters | Founded | Team size |
| Itexus | Custom-built claims and transaction fraud analysis software | Dover, DE, USA | 2013 | 150+ engineers |
| Quantexa | Network-based fraud detection across the insurance lifecycle | London, UK | 2016 | 501–1,000 employees |
| Owl | Life, health and disability insurers with document-heavy claims | Vancouver, Canada | 2018 | 51–200 employees |
| Shift Technology | Insurance fraud screening and investigation workflows | Paris, France | — | 501–1,000 employees |
| FRISS | P&C underwriting and claims fraud screening | Utrecht, Netherlands | 2006 | 201–500 employees |
| CLARA Analytics | Workers' compensation and casualty claims investigations | Santa Clara, CA, USA | 2017 | 51–200 employees |
| SAS | Enterprise fraud analytics and case management | Cary, NC, USA | 1976 | 10,001+ employees |
| Tata Consultancy Services (TCS) | Large insurance data and fraud analytics programs | Mumbai, India | 1968 | 10,001+ employees |
| OSKI Solutions | Bespoke AI-enabled applications and cloud platforms | Tallinn, Estonia | — | 51–200 employees |
| Verisk | Industry claims intelligence and fraud investigation support | Jersey City, NJ, USA | 1971 | 5,001–10,000 employees |
| DataWalk | Organized fraud and connected-claims investigation | Redwood City, CA, USA | — | 51–200 employees |
| Sprout.ai | Insurers seeking AI-driven claims fraud screening | London, UK | 2018 | 11–50 employees |
| A-listware | Dedicated engineering capacity for fraud analytics platforms | London, UK | — | — |
| Gradient AI | Predictive insurance risk and claims analytics | Boston, MA, USA | 2018 | 51–200 employees |
How to Choose an Insurance Fraud Detection Provider
Insurers usually pick between a ready-made fraud platform and a partner that builds detection around their own claims systems and data. Either way, the provider has to work with your lines of business, your investigators and your regulators. These checks help you judge whether a solution will catch more fraud without slowing honest claims.
Fit with your lines of business. Fraud patterns differ between motor, property, health, workers' compensation and life. Ask which lines the models or rules were built for and request examples of scenarios they detect, such as staged accidents, inflated repair estimates or provider billing schemes. A tool tuned mainly for one line may need significant work before it is useful in another.
Referral quality, not alert volume. Ask how the provider measures hit rate: the share of referred claims that investigators confirm as fraudulent or adjust. High alert volumes with low hit rates overload your special investigations unit and delay legitimate claims. Request a pilot on your historical claims with known outcomes so you can compare results against your current rules.
Explainable scores for investigators. Every flagged claim should come with readable reasons, such as linked parties, unusual timing or billing anomalies, that an adjuster can verify. Explanations matter when a claim is denied, disputed or reviewed by a regulator. Be wary of a single risk score with no supporting evidence, because investigators will either ignore it or follow it blindly.
Data integration and network analysis. Check which sources the solution can use: claims and policy systems, adjuster notes, medical bills, repair shop data, industry databases and third-party identity data. Ask whether it links entities across claims to reveal organized rings. Integration with your core platform usually decides the timeline, so ask for real integration examples with systems like yours.
Feedback loop from case outcomes. Models degrade as fraudsters adapt. Confirm that investigation outcomes flow back into the system automatically, that models are retrained or rules reviewed on a defined schedule, and that you can see performance drift over time. Ask who owns tuning after go-live: your team, the vendor, or both, and what that costs.
Compliance and fair treatment. Ask how the provider handles personal and health data, where it is stored, and how access is logged. Confirm the solution supports your obligations on automated decision-making and avoids proxies for protected characteristics. Detection should route claims to human review rather than deny them automatically, which keeps you on safer ground with regulators and customers.
Conclusion
Effective insurance fraud detection is as much a data and workflow problem as a modeling problem. Relevant signals may be scattered across claim files, policy records, medical bills, linked identities, and external sources. A strong implementation brings that information together, prioritizes cases with meaningful evidence, and gives investigators a clear way to review and document decisions. Insurers should also consider how models are monitored, how false alerts affect customer experience, and whether detection rules can change when fraud patterns shift. The most sustainable approach combines reliable data, measured automation, and accountable human review.
Frequently Asked Questions
Should insurers buy a fraud detection platform or build a custom solution?
Buying is usually faster when your lines of business and claims system match what the platform already supports. Building makes sense when your data, products or workflows are unusual, or when you want to own the models. Many insurers combine both: a platform for core scoring plus custom integrations, data pipelines and investigator tools around it.
How long does it take to implement insurance fraud detection?
A pilot on historical claims can often run within a few weeks once data is available. A production rollout connected to the claims system typically takes several months, depending on data quality, the number of integrations and how many lines of business are included. Data preparation and integration usually take longer than model setup.
What data is needed for effective fraud detection?
At minimum you need claims records, policy and policyholder data, payment history and confirmed fraud outcomes from past investigations. Detection improves with adjuster notes, provider and repair shop data, third-party identity information and industry claim databases. Labeled outcomes are especially valuable, because without them it is hard to measure whether the system is catching real fraud.
How much does insurance fraud detection software cost?
Platform pricing is typically a subscription based on claim volume, premium written or the number of lines covered, plus implementation fees. Custom development is priced by scope and team size. The main cost drivers are the number of data sources to integrate, real-time versus batch scoring, case management needs and ongoing model maintenance.
Can AI fraud detection deny claims automatically?
It should not, in most cases. Fraud detection is best used to prioritize claims for human review, while straightforward low-risk claims move faster. Automatic denial based on a model score creates legal, regulatory and reputational risk, especially where rules on automated decisions apply. Keep a documented human decision and the evidence behind it for every adverse outcome.