Health Insurance Fraud Detection Software
95%
Decrease in Outstanding
Referrals
50,000+
Settled Investigations
100m+
Fraudulent Claims Managed
10+
Ready Integrations
Comprehensive Healthcare Fraud Prevention
The growing scale of health insurance fraud detection challenges demands robust systems that can manage the complexity of modern schemes. Fraud in healthcare is diverse, spanning provider fraud, patient schemes, and prescription fraud, often involving layered tactics that make detection difficult. These schemes significantly increase healthcare costs and can harm patient care through delayed or denied treatments.
FraudOps provides healthcare fraud prevention software designed to address the unique threats faced by the healthcare sector. The platform empowers insurers with data-driven tools for medical insurance fraud investigation, ensuring that both common and sophisticated fraud schemes are identified early. With automated detection, real-time alerts, and advanced analytics, FraudOps offers comprehensive protection for healthcare providers, insurers, and patients alike.
Advanced analytics identify suspicious healthcare billing patterns before they cause major financial or operational losses.
Real-time alerts enable investigators to quickly address potentially fraudulent claims with speed and precision.
Integrated case management tools streamline medical insurance fraud investigation processes for greater efficiency and accuracy.
Automated detection algorithms improve health insurance fraud detection accuracy while reducing unnecessary investigations and false positives.
Specialised Medical Fraud Investigation Capabilities
FraudOps specialises in tackling the complexity of medical insurance fraud investigation by combining technology and industry expertise. Healthcare fraud often involves networks of providers, patients, and intermediaries working together, making early detection critical. Sophisticated schemes such as upcoding, phantom billing, and prescription diversion require advanced tools capable of recognising patterns across large datasets.
By leveraging AI-powered healthcare fraud prevention software, FraudOps delivers unmatched visibility into potential risks. Its centralised investigation platform allows insurers to track, analyse, and resolve suspicious cases while maintaining compliance with healthcare regulations. The system’s ability to adapt to evolving fraud tactics ensures that insurers stay ahead of emerging threats, protecting both financial resources and patient welfare.
Machine learning algorithms detect complex fraud patterns faster than traditional review processes, improving early intervention rates significantly.
Comprehensive reporting features strengthen compliance efforts while enhancing transparency across investigation workflows.
Centralised fraud intelligence supports collaboration between teams handling provider fraud, patient schemes, and prescription fraud cases.
Continuous system learning improves health insurance fraud detection by adapting to new methods used in medical insurance scams.
Streamline medical insurance fraud investigation for faster, accurate results.
Health Insurance Fraud Types
Fraud in the healthcare sector is increasingly sophisticated, involving multiple actors and varied schemes. Effective health insurance fraud detection requires clear categorization of risks, enabling targeted strategies to disrupt them. FraudOps structures intelligence into provider misconduct, patient deception, prescription abuse, device manipulation, and billing manipulation. This segmentation allows healthcare fraud prevention software to apply relevant detection models, automate alerts, and provide investigators with context for each flagged case.
Medical Provider Fraud
Medical provider fraud erodes trust and inflates costs. Schemes include billing for services not rendered, upcoding to claim higher reimbursements, and unnecessary treatment provision to justify inflated charges. Phantom billing invents patient encounters, while kickback and referral fraud incentivize inappropriate care. Health insurance fraud detection systems flag deviations from standard treatment patterns and compare provider claims against peer norms. Healthcare fraud prevention software validates service dates, patient visits, and diagnostic codes. In medical insurance fraud investigation, correlating chart notes, device usage logs, and appointment histories exposes misrepresentation, supports recovery efforts, and deters future misconduct through targeted monitoring and compliance audits.
Patient and Claimant Fraud
Patient and claimant fraud impacts insurers and legitimate members. Offenses include identity theft, impersonation, exaggerated symptom reporting, and treatment shopping across multiple providers. Insurance card sharing allows unauthorized individuals to access benefits, while fake injury claims exploit lenient verification processes. Health insurance fraud detection integrates biometric verification, geolocation analysis, and cross-claim history to confirm legitimacy. Healthcare fraud prevention software identifies overlapping treatments, duplicate prescriptions, and suspicious injury timelines. In medical insurance fraud investigation, case linking across states and networks helps reveal organized schemes, enabling rapid containment, evidence gathering, and collaboration with legal authorities to enforce accountability and recover losses.
Prescription Drug Fraud
Prescription drug fraud combines abuse, diversion, and financial exploitation. Schemes include doctor shopping for multiple prescriptions, forging or altering prescriptions, exploiting pharmacy benefits, and diverting controlled substances to illicit markets. Drug resale schemes monetize excess or stolen medications. Health insurance fraud detection correlates prescribing patterns with patient histories, pharmacy fills, and diagnosis codes. Healthcare fraud prevention software monitors early refills, cross-pharmacy purchases, and inconsistent dosage levels. In medical insurance fraud investigation, integrating toxicology results, prescriber credentials, and dispensing records exposes misuse, supports law enforcement referrals, and strengthens pharmacy network oversight to prevent recurrent abuse and safeguard patient health.
Medical Device and Equipment Fraud
Medical device and equipment fraud exploits high-value claims and complex supply chains. Common schemes involve unnecessary device implantation, misrepresenting device quality, and billing for premium models when standard devices were provided. Maintenance and repair fraud and warranty claim abuse inflate costs. Health insurance fraud detection compares serial numbers, purchase orders, and patient records. Healthcare fraud prevention software traces supply chain data, invoices, and lot codes to verify legitimacy. In medical insurance fraud investigation, cross-referencing surgical logs with billing claims reveals discrepancies, deters counterfeit device use, and supports enforcement actions against suppliers or providers engaged in fraudulent practices.
Medical Provider Investigation
Fraud detection in healthcare requires precise investigation methods to identify irregular practices among providers. The Medical Provider Investigation process in health insurance fraud detection focuses on validating credentials, monitoring treatment patterns, identifying billing anomalies, and comparing provider performance. These methods ensure insurers can detect fraudulent claims before they cause financial damage. By using analytics and verification techniques, organisations can assess provider legitimacy, detect unusual billing behaviours, and flag suspicious activities for review. This approach protects both insurers and policyholders, while supporting compliance with regulatory requirements and maintaining the integrity of healthcare services across diverse provider networks.
Provider Credential Verification
Verifying provider credentials is essential in health insurance fraud detection to confirm qualifications, licenses, and affiliations. This process ensures that only authorised professionals deliver medical services covered by insurance policies. Investigators review licensing records, medical board certifications, and disciplinary histories to detect falsified or expired credentials. Automated systems can integrate with national registries, reducing manual verification time and minimising errors. Credential verification also helps identify fraudulent entities posing as legitimate providers, preventing improper claims from being processed. By establishing a trustworthy provider base, insurers reduce risks, enhance compliance, and maintain high standards of patient care across all service areas.
Treatment Pattern Analysis
Treatment pattern analysis identifies abnormal or excessive service patterns that may indicate fraud. In health insurance fraud detection, this process involves reviewing historical claims data to detect inconsistencies in diagnosis and treatment frequency. For example, a provider repeatedly performing high-cost procedures beyond standard medical guidelines may raise concerns. Analytical models compare treatment records with medical necessity benchmarks, helping detect upcoding or unnecessary procedures. This analysis also flags providers whose patient treatment timelines deviate from established norms. By using automated analytics and cross-referencing patient histories, insurers can uncover patterns that suggest deliberate fraud while also identifying potential training needs for providers.
Billing Anomaly Detection
Billing anomaly detection focuses on identifying irregularities in submitted claims that could signal fraud. Within health insurance fraud detection, this includes identifying duplicate charges, mismatched procedure codes, and inflated costs. Automated fraud detection systems flag unusual billing activity for review, while auditors investigate patterns inconsistent with legitimate medical practice. Data analytics tools compare billing frequency, cost averages, and claim volumes against industry benchmarks to detect anomalies. This process also identifies potential collusion between providers and claimants. By spotting billing anomalies early, insurers prevent overpayments, safeguard financial resources, and ensure healthcare services are billed accurately and ethically in all insurance transactions.
Peer Comparison Analytics
Peer comparison analytics evaluates provider behaviour by benchmarking against similar practitioners. In health insurance fraud detection, this method helps highlight outliers whose billing or treatment practices deviate significantly from peers. For instance, if a provider consistently bills for more procedures or higher-value treatments than others in the same speciality, it may indicate fraudulent activity. Data visualisation tools present comparison results in clear dashboards, enabling investigators to focus on the highest-risk cases. Peer comparison also supports compliance by documenting objective performance measurements. This approach combines statistical analysis with healthcare expertise to detect fraud while promoting fair and ethical medical practices.
Patient Fraud Detection
Detecting fraudulent activity from patients or claimants is crucial in health insurance fraud prevention. By using advanced tools and data-driven methods, insurers can identify suspicious patterns and protect resources. FraudOps applies sophisticated analytics to uncover inconsistencies, prevent false claims, and ensure accurate payouts. These capabilities help insurers manage risk and improve overall claim handling efficiency.
Identity Verification
Health insurance fraud detection often begins with strong identity verification. FraudOps uses document authentication, biometric checks, and database cross-referencing to confirm claimant identities. This process helps detect impersonation, stolen identities, or falsified details before fraudulent claims are processed. The system integrates with national and regional identity databases to improve accuracy and speed. Automated alerts notify investigators of mismatches or irregularities, enabling timely intervention. Consistent identity checks prevent duplicate claims and ensure legitimate claimants receive timely benefits. By implementing robust identity verification, insurers strengthen fraud prevention efforts and maintain trust with genuine policyholders, reducing the risk of costly fraudulent payouts.
Treatment History Analysis
Accurate patient fraud detection involves a thorough review of medical treatment history. FraudOps consolidates treatment records from multiple providers to identify unusual or excessive procedures linked to a claimant. This analysis helps detect staged injuries, falsified illnesses, or claims for non-existent treatments. Cross-checking treatment timelines ensures that medical procedures align with genuine healthcare needs. Automated pattern recognition highlights inconsistencies between reported injuries and prescribed treatments. Investigators can quickly access detailed timelines and supporting documents, reducing manual review time. By verifying treatment history early in the claims process, insurers can stop fraudulent cases before payouts occur, safeguarding resources and operational integrity.
Multiple Provider Patterns
Health insurance fraud detection is strengthened by monitoring multiple provider patterns. FraudOps identifies cases where a claimant seeks similar treatments from different providers without medical justification. This behaviour often signals intentional overutilisation or organised fraud schemes. The platform cross-references provider records, appointment dates, and treatment types to highlight potential abuse. Alerts are triggered when overlapping claims or excessive visits occur. By analysing geographic and temporal data, insurers can detect organised networks of fraudulent activity. This approach prevents duplicate claim payouts, reduces unnecessary healthcare costs, and enables targeted investigations, ensuring resources are directed towards genuine cases requiring valid medical support.
Prescription Shopping Detection
Effective patient fraud detection includes spotting prescription shopping activity. FraudOps tracks prescription histories to identify claimants obtaining the same or similar medications from multiple providers or pharmacies. This behaviour often indicates controlled substance abuse or resale. The system flags overlapping prescriptions, excessive refill requests, and suspicious pharmacy patterns. Integration with national prescription monitoring programs enhances detection accuracy. Real-time alerts allow investigators to intervene before reimbursement or distribution. This approach helps protect patient safety, prevent misuse of medications, and reduce fraudulent claims related to prescription abuse. By addressing prescription shopping early, insurers safeguard both financial resources and community health.
Experience smarter health insurance fraud detection with FraudOps today.
Prescription Drug Fraud Prevention
Prescription drug fraud can escalate healthcare costs and compromise patient safety. Healthcare fraud prevention software equips insurers with tools to monitor, flag, and address suspicious prescription activity. By detecting patterns such as excessive refills, doctor shopping, or pharmacy collusion, the system supports quick intervention. This ensures resources are allocated appropriately and safeguards are in place against misuse. FraudOps streamlines detection with automated data analysis and cross-referencing capabilities, enabling insurers to take proactive measures. With robust monitoring, fraudulent schemes involving prescriptions can be intercepted before they cause significant financial or health-related harm.
Prescription Monitoring
Prescription monitoring is central to effective health insurance fraud detection. FraudOps consolidates prescribing, dispensing, and claim activity to reveal irregular refill frequencies, dosage escalations, or overlapping therapies. Alerts prioritize cases with repeated early refills, cross prescriber shopping, or clinic hopping patterns. Medical insurance fraud investigation teams review linked patient histories, prescriber licenses, and diagnosis codes to confirm necessity. The platform ties pharmacy records to encounter notes and benefit rules, improving evidence quality and triage. Continuous monitoring reduces leakage, accelerates resolution, and protects member safety while ensuring medications align with clinical need. Dashboards prioritize risk and guide proportional investigative effort efficiently.
Drug Interaction Analysis
Drug interaction analysis strengthens medical insurance fraud investigation by validating clinical plausibility. FraudOps correlates prescriptions with diagnoses, allergies, and lab results, highlighting dangerous or unsupported combinations. Outliers include high risk interactions, duplicate therapies, or contraindicated drugs that appear frequently in abusive patterns. Investigators compare prescriber specialties and treatment rationale to medical guidelines. Health insurance fraud detection benefits from rules that flag missing diagnostics or incompatible regimens. Findings are packaged with timelines, references, and clinician notes, enabling clear disposition. This approach protects patients, reduces claim waste, and supports corrective education with measurable outcomes. Dashboards highlight priorities and streamline reviewer follow up.
Pharmacy Network Monitoring
Pharmacy network monitoring within healthcare fraud prevention software reveals coordinated schemes. FraudOps maps dispensing behaviors across locations to detect location hopping, cash anomalies, and repetitive fills by linked identities. The system flags pharmacies with unusual claim acceptance rates, late night activity clusters, or high volumes of controlled substances. Investigators review supply invoices, fulfillment logs, and inventory movements to validate legitimacy. Combined with plan rules and licensing data, these insights expose collusion, rogue operations, and billing manipulation. The outcome is faster interdiction, fair provider management, and sustained reduction in pharmacy related losses. Investigators receive playbooks, visual maps, and configurable escalation pathways.
Controlled Substance Tracking
Controlled substance tracking focuses on diversion risks and public safety. FraudOps integrates prescription drug monitoring program data, claims, and dispensing logs to track quantities, prescribers, and refill timing. Healthcare fraud prevention software alerts when thresholds are exceeded or when multiple pharmacies and clinicians appear within compressed periods. Health insurance fraud detection teams validate medical necessity against pain agreements and treatment plans. Evidence packs include pharmacy communications, pill counts, and toxicology results to support actions. The process reduces abuse, deters organized rings, and protects members. Cross border patterns and courier records are examined for diversion risk. Repeat prescribers trigger enhanced review.
Medical Billing Investigation
Medical billing errors and intentional fraud can result in significant financial losses for healthcare providers and insurers. Effective health insurance fraud detection requires detailed verification of coding, procedure legitimacy, and billing patterns. By investigating suspicious claims promptly, insurers can minimise risk, improve accuracy, and strengthen overall compliance. FraudOps offers the tools to conduct efficient and thorough medical billing reviews.
Coding Accuracy Verification
Accurate coding is essential for effective health insurance fraud detection. FraudOps enables investigators to review and validate billing codes against official medical classification systems, ensuring claims match provided services. This process identifies mismatches, incorrect codes, and intentional misrepresentation, which may indicate fraudulent activity. Through automated checks, the system reduces manual errors and accelerates the investigation process. Detailed audit trails help maintain transparency, while advanced analytics highlight patterns requiring deeper review. By ensuring code accuracy, insurers protect financial resources, maintain compliance, and improve trust in claims processing, ultimately supporting a more reliable and efficient medical billing environment for all stakeholders involved.
Procedure Validation
Validating claimed medical procedures is a critical step in medical insurance fraud investigation. FraudOps cross-references submitted claims with patient records, treatment histories, and medical necessity guidelines to confirm that procedures billed were actually performed and justified. This process detects phantom procedures, unnecessary treatments, or misrepresented services. Automated validation speeds up the review while maintaining thoroughness, reducing the chances of undetected fraud. Investigators can flag high-risk claims for further scrutiny, supported by comprehensive reporting tools. By integrating data from multiple healthcare sources, FraudOps strengthens accuracy, safeguards payer resources, and ensures patients receive care aligned with legitimate medical needs.
Duplicate Claim Detection
Duplicate claims can lead to inflated payouts and unnecessary losses. With advanced health insurance fraud detection, FraudOps automatically identifies and flags claims that match previously submitted records. The system analyses billing dates, procedure codes, and patient identifiers to detect repetition, whether intentional or accidental. Investigators can review flagged claims quickly, minimising payment delays and ensuring only valid submissions are processed. This automation reduces administrative workloads while improving financial accuracy. By maintaining strict oversight of claim duplication, insurers can prevent overpayment, enhance operational efficiency, and reinforce trust between providers, patients, and payers, ultimately creating a more secure healthcare billing process.
Upcoding and Unbundling Detection
Upcoding and unbundling are common tactics targeted in healthcare fraud prevention software. FraudOps uses intelligent algorithms to detect when providers bill for more expensive procedures than performed or split bundled services to increase reimbursement. By comparing claim details against standard coding guidelines, the system flags inconsistencies for investigation. Automated alerts allow faster detection and intervention, preventing revenue leakage. Investigators can view historical patterns, provider profiles, and claim histories for comprehensive fraud assessment. This capability not only deters fraudulent billing practices but also ensures fair reimbursement, protecting payer funds while promoting accurate, ethical medical billing across healthcare networks.
Advanced Analytics and AI
Advanced analytics and AI deliver stronger fraud prevention capabilities by analysing patterns, predicting risks, and identifying irregularities in claims. These tools help insurers act faster, reduce false positives, and improve investigation accuracy. By integrating AI with healthcare data, organisations can achieve a higher level of fraud detection while maintaining compliance. The combination of predictive modelling, pattern recognition, and anomaly detection ensures that fraudulent activities are spotted early and addressed effectively, supporting both operational efficiency and regulatory requirements. This section covers the advanced features that enable a proactive approach to fraud detection in the healthcare and insurance sector.
Predictive Fraud Modelling
Predictive fraud modelling applies advanced statistical techniques and machine learning to identify high-risk claims before they are processed. In health insurance fraud detection, predictive models analyse historical claims data, patient records, and provider behaviour patterns to flag suspicious activities. This reduces investigation delays and allows for early intervention. The models are continuously trained with new data, improving accuracy over time. They help investigators focus on cases most likely to be fraudulent, optimising resource allocation. By combining predictive analytics with traditional investigative methods, insurers strengthen their fraud prevention strategies and improve the overall reliability of their claim assessment processes.
Pattern Recognition Algorithms
Pattern recognition algorithms help detect recurring behaviours and trends that indicate fraudulent activity. Within medical insurance fraud investigation, these algorithms compare large volumes of claims data to uncover hidden connections between providers, patients, and procedures. They can detect subtle patterns that human reviewers may overlook, such as repeated billing anomalies or excessive service usage. These tools are particularly effective for uncovering organised fraud rings and coordinated schemes. Integration with healthcare fraud prevention software ensures that detection is automated, accurate, and scalable. By identifying suspicious behaviours early, insurers can stop fraudulent claims from being processed and safeguard healthcare funds.
Anomaly Detection Systems
Anomaly detection systems identify outliers in claims data that may signal fraudulent behaviour. In health insurance fraud detection, these systems analyse multiple data points, including claim amounts, treatment types, and provider histories. When a deviation from normal patterns is found, the system flags it for review. This allows investigators to quickly assess and respond to unusual activity. Unlike rule-based systems, anomaly detection uses adaptive algorithms, meaning it can recognise new fraud methods as they emerge. When integrated with healthcare fraud prevention software, it provides a proactive defence layer, reducing financial losses and enhancing overall fraud investigation effectiveness.
Risk Scoring Engines
Risk scoring engines assign numerical values to claims based on the likelihood of fraud, allowing investigators to prioritise cases effectively. In medical insurance fraud investigation, these engines combine predictive analytics, pattern recognition, and anomaly detection to produce highly accurate scores. The system evaluates factors such as claim complexity, provider history, and billing irregularities. High-risk claims are escalated for immediate review, while low-risk claims can be processed more quickly. Integration with healthcare fraud prevention software ensures real-time scoring and continuous refinement of risk criteria. This approach streamlines workflows, reduces processing delays, and helps insurers address fraud threats with greater precision.
Regulatory Compliance
Maintaining regulatory compliance is essential in health insurance fraud detection to ensure investigations meet legal and ethical standards. This section addresses how the platform safeguards sensitive healthcare data, maintains privacy, and ensures adherence to evolving regulations. Each sub-feature strengthens medical insurance fraud investigation by ensuring processes are transparent, auditable, and compliant with strict industry guidelines.
HIPAA Compliance
The system supports strict HIPAA compliance by protecting patient data during medical insurance fraud investigation. Access controls ensure only authorized users handle sensitive records, while encryption safeguards data during storage and transmission. Regular security updates and policy checks help detect and address vulnerabilities quickly. The platform also provides user activity logs, enabling organisations to prove compliance during audits. With HIPAA-aligned workflows, healthcare fraud prevention software can operate confidently within legal boundaries, reducing risk while maintaining patient trust.
Data Privacy Protection
Data privacy protection is a critical element of healthcare fraud prevention software. The platform enforces robust encryption standards, anonymises sensitive identifiers when possible, and restricts access based on user roles. Continuous monitoring helps detect suspicious data access attempts. Privacy safeguards ensure that health insurance fraud detection activities do not compromise patient confidentiality. By embedding privacy principles into every stage of the investigation process, the platform promotes regulatory adherence and secures trust between patients, insurers, and investigative teams.
Audit Trail Management
Audit trail management enhances health insurance fraud detection by tracking every investigative action. The system records logins, data changes, and case updates, ensuring full transparency. Investigators can review chronological activity logs to verify compliance with regulations. These detailed records also aid in defending against legal disputes by providing evidence of proper handling. Built-in search and filtering features allow quick retrieval of specific activities. This level of accountability ensures that medical insurance fraud investigation processes remain compliant, traceable, and defensible in regulatory audit
Regulatory Reporting
Regulatory reporting tools within healthcare fraud prevention software simplify compliance with oversight agencies. Automated report generation ensures accuracy and timely submissions, reducing manual workload. The system formats reports to meet specific regulatory requirements, supporting various jurisdictions. Integration with case data ensures all reported information is consistent and verifiable. This helps healthcare providers and insurers demonstrate health insurance fraud detection compliance while focusing on active investigations. Reliable reporting functions strengthen credibility with regulators and enhance operational efficiency in fraud prevention.
Integration with Healthcare Systems
Seamless integration between healthcare fraud prevention software and core medical systems enhances detection accuracy and investigative speed. By connecting directly to claims processing platforms, patient records, and billing systems, FraudOps ensures that investigators have immediate access to relevant data without delays. This connectivity reduces manual searches, streamlines workflows, and helps maintain compliance by pulling data directly from secure, verified sources. Integration also supports real-time alerts, ensuring that potential fraudulent activities are flagged before they escalate into significant financial or reputational risks for healthcare providers and insurers.
EHR and EMR Connectivity
Health insurance fraud detection is more effective when linked directly with Electronic Health Records (EHR) and Electronic Medical Records (EMR) systems. FraudOps synchronizes with these platforms to pull structured and unstructured medical data in real time. This allows investigators to validate treatment records against claims, identify discrepancies, and detect patterns that could indicate fraudulent activity. Automatic syncing eliminates the need for duplicate data entry, reduces human error, and ensures that every investigation is based on the most current and accurate patient information available.
Claims Processing System Linkage
For effective medical insurance fraud investigation, integration with claims processing systems is crucial. FraudOps connects directly to these systems to monitor claim submissions, approvals, and payments as they happen. This linkage enables immediate flagging of suspicious claims, such as unusually high charges, duplicate billing, or mismatched patient details. Direct access to claims data allows investigators to begin reviews without waiting for manual reports, speeding up case resolution and preventing further losses from ongoing fraudulent activity within the healthcare payment cycle.
Pharmacy System Integration
Pharmacy data plays a critical role in healthcare fraud prevention software. FraudOps integrates with pharmacy systems to track prescription histories, dispensing records, and refill patterns. This allows investigators to identify red flags such as excessive prescriptions, forged authorizations, or dispensing to non-patients. By combining pharmacy records with clinical and claims data, FraudOps provides a complete picture of potential fraud schemes, enabling quicker identification and stronger evidence gathering. Integration also supports compliance with prescription monitoring programs and controlled substance regulations.
Laboratory and Diagnostic System Linkage
Linking medical insurance fraud investigation tools with laboratory and diagnostic systems enables real-time validation of test results against claims. FraudOps can flag cases where billed tests were never performed, results were altered, or charges exceeded standard pricing. This integration also helps detect unnecessary or fabricated testing patterns used to inflate billing. Having direct, automated access to diagnostic data strengthens the integrity of investigations, ensures accurate claim evaluations, and reduces the time needed to confirm fraudulent activity.
Integration and Customisationn
FraudOps is built to integrate seamlessly with enterprise systems and adapt to diverse investigation needs. It enhances your fraud analytics platform by aligning tools, users, and processes into a centralised workflow. These capabilities support efficient operations while improving investigation quality management across all environments.
System Integration
Connect easily with claims, policy, CRM, and identity systems through secure and reliable APIs.nn
Custom Workflows
Adapt workflows to match internal SLAs, team structures, and jurisdiction-specific procedures.nn
Flexible Dashboards
Tailor dashboards for investigators, reviewers, or executives with role-specific data views.nn
Team-Level Configuration
Support multi-region setups and user roles with granular access and case visibility settings.nn
Reporting Compatibility
Export data in multiple formats and connect with BI tools for flexible analysis and reportingn
Scalable
Deployment
Add features, users, and functions as operations grow without disrupting ongoing investigations.
Healthcare Fraud Case Studies
Real-world examples show how FraudOps drives effective health insurance fraud detection and protects patients. Explore case studies demonstrating successful medical insurance fraud investigation outcomes and proactive prevention strategies.
