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Insurance Fraud Detection Automation

Use Cases, Features, Costs, ROI

ScienceSoft brings 12 years of experience in insurance software development to help insurance companies establish robust automation of their fraud detection processes.

Insurance Fraud Detection Automation - ScienceSoft
Insurance Fraud Detection Automation - ScienceSoft

Insurance Fraud Detection Automation: Key Aspects

Insurance fraud detection automation helps streamline insurance data validation processes and promptly spot customer fraud and malicious employee activities. Advanced insurance fraud detection systems can be powered by artificial intelligence (AI) to enable precise, analytics-based recognition and proactive prevention of suspicious transactions, identity theft, document forgery, and non-compliant workflows.

  • Implementation time: 9–15+ months for a custom fraud detection automation system.
  • Necessary integrations: customer interaction channels, claim management software, underwriting software, a policy administration system, accounting software.
  • Development costs: $200K–$800K+, depending on software complexity.
  • Average payback period: <7 months.
  • ROI: 200%–1,000%.

How to Automate Fraud Detection Across Six Major Risk Areas

Identity fraud detection

Get automated customer identity verification to eliminate manual routines across client data processing, ensure compliance with KYC/AML regulations, and prevent unauthorized access to insurance services.

Underwriting fraud detection

Monitor the way your underwriters estimate risks and approve insurance applications. Be notified immediately if underwriting does not comply with internal and legal guidelines.

Policy data fraud detection

Establish intelligent validation of policy data to make sure there are no non-existent beneficiaries, the value of the insured assets is not inflated, and the risk information is accurate and complete.

Policy manipulation fraud detection

Get instant alerts on the attempts to create, change, or share insurance policies by unauthorized parties to block bad actors fast and prevent document fraud and sensitive data leakage.

Claim fraud detection

Leverage AI-powered validation of claim data and automated detection of fraudulent cases to get up to 2x faster and 99%+ accurate claim decisioning, optimize settlement efforts, and prevent undue and inflated payouts.

Sourcing fraud detection

Obtain 100% visibility of your damage handling partner selection activities and automatically identify suspicious agreements and payment transactions that failed to pass the pre-set approval flow.

Reinsurance fraud detection

Monitor reinsurance transactions and agent behavior to quickly spot an unusually large number of reinsurance agreements or policy replacements that can signal potential fee churning.

Accounting fraud detection

Employ automation to instantly detect and prevent malicious manipulations across business-critical financial data and ensure consistent and compliant financial reporting.

Common Insurance Fraud Schemes Software Helps Recognize

Customer identity theft

Document forgery

Misrepresenting risk data

Car insurance fronting

Premium diversion

Inventing losses

Claim misclassification

Claim exaggeration

Double billing

Fee churning

Key Features of an Automated Insurance Fraud Detection System

ScienceSoft designs and builds fraud detection solutions with functionality bound to each company’s specific needs. Below, our consultants outline the features commonly requested by our clients from the insurance industry:

Insurance compliance rule management

  • Customizable internal compliance rules for customer onboarding, underwriting, policy issuance and updating, claim resolution, document creation and processing, insurance transaction recordkeeping, service supplier sourcing, etc.
  • Configurable requirements for individuals and businesses to qualify for insurance services, e.g., submitting salary statements to prove income, utility bills, rental agreements, or mortgage statements to prove residency, asset passports to prove asset ownership, environmental impact assessments reports for businesses to prove ethical practices, etc.
  • Automated conversion of personalized coverage terms fixed in insurance policies into claim validation and resolution rules.
  • Setting up rules to achieve and maintain multi-jurisdictional compliance with KYC/AML and OFAC requirements, IFRS17, CCPA, CPRA, GLBA, SAMA requirements (for the KSA), MCEV principles and GDPR (for the EU), NYDFS (for New York), HIPAA (for health insurance), other relevant legal regulations and data protection standards.
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Automated validation of customer and business data

  • Real-time and batch capture and processing of insurance customer data from the available sources.
  • OCR- and AI-powered processing of unstructured customer data like printed, handwritten, digital documents and digital images.
  • Intelligent matching of customer data to the internal requirements and data available in third-party sources (credit rating bureaus, medical information bureaus, etc.).
  • Automatically cross-referencing multiple data sources for customer identity verification.
  • Rule-based AML/CFT and OFAC verification for new clients.
  • AI-supported contextual analytics to spot suspicious discrepancies in customer data like inconsistent income-to-spend levels or controversial asset ownership data.
  • Validation of customers’ digital signatures and biometric verification (e.g., facial recognition, fingerprint scanning) using intelligent image analysis technologies.
  • AI-based validation of claim data and documents submitted by policyholders against the policy terms and data provided by service suppliers, financial institutions, public services organizations, etc.
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Automated insurance fraud detection

  • Real-time detection and flagging of forged customer documents (IDs, loss claims, service receipts, income, health, and asset ownership documents) and customer behavior anomalies (unusual claim patterns, misclassified or excessive claims, etc.).
  • AI-supported cross-referencing of data on customer transactions and insurance events with historical data and external insurance fraud databases.
  • Monitoring employee activities across various insurance areas and analyzing their compliance with the pre-set workflow standards.
  • AI-enabled verification of loss estimate accuracy.
  • Real-time spotting of employee fraud and non-compliance: improper underwriting practices, claim case misinterpretation and loss overestimation, suspicious payment transactions, attempts to change sensitive data, and more.
  • Instant notifications to the insurance fraud investigators on fraudulent customer behavior and malicious employee activities.
  • Setting custom penalties for malicious actors in alignment with company policies and legal guidelines.
  • Rule-based penalty enforcement.
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Insurance fraud analytics and reporting

  • AI-powered prediction of insurance fraud based on the analysis of historical fraud scenarios.
  • Rule- or AI-based suggestions on the proper course of actions based on severity and type of fraud.
  • Calculating and tracking insurance fraud metrics: the number of fraud cases by period, department, customer segment, investigation rate, true and false positive rates, impact rate, etc.
  • Scheduled and ad hoc generation of fraud reports, including suspicious activity reports (SARs) and suspicious transaction reports (STRs).
  • Rule-based submission of approved fraud reports to regulatory entities (e.g., NICB and NAIC in the US) and business partners (e.g., reinsurers).
  • Fraud data encryption at rest and in transit, including asymmetric encryption for blockchain insurance fraud solutions.
  • Role-based access to fraud-related data.
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Get Robust Fraud Detection Automation

ScienceSoft is ready to build a reliable automated fraud detection solution to help your insurance firm boost process efficiency and minimize fraud-associated losses.

Essential Integrations for an Automated Insurance Fraud Detection System

Integrating insurance fraud detection software with the relevant systems helps eliminate manual aggregation and processing of insurance data and accelerate the identification of fraud cases. ScienceSoft recommends establishing the following integrations:

Customer interaction channels

To instantly spot suspicious customer behavior, faked documents and claims.

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  • Insurance portal
  • Email services
  • Messaging services
  • Business phone systems
  • Third-party insurance platforms


Corporate systems

What is not possible with configuration, we achieve with custom coding.

Custom module development

For data-driven validation of the information submitted by customers.

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  • Credit rating platforms
  • Banking systems
  • Medical information bureaus
  • Social security administration
  • Police administration
  • Department of motor vehicles
  • GIS, weather, and telematics platforms
  • Tracking systems of commercial customers
  • Social media


Essential Integrations for an Automated Insurance Fraud Detection System

How to Drive High Payback from Insurance Fraud Detection Automation

Below, ScienceSoft’s consultants list the important factors that help unlock 1,000%+ ROI for automated insurance fraud detection.

Accurate formalization of compliance rules

Converting internal and legal compliance requirements into clear automation rules gives software a consistent basis for detecting non-compliant servicing workflows and illegitimate customer transactions.

ML-powered analytics

ML algorithms help recognize known and emerging insurance fraud schemes, including deepfakes and AI-generated forged documents. ML models continuously learn from new data to improve fraud detection accuracy.

Integration with multiple data sources

Connecting fraud detection software to corporate systems and third-party data sources, including non-standard ones like IoT devices and social media, helps quickly obtain comprehensive data on customer and employee behaviors.

ScienceSoft’s Featured Success Story

Development of Custom Loan Management Software for a US-based Microfinance Company

ML Algorithms to Identify Dental Insurance Fraud with 95% Accuracy

ScienceSoft helped a dental insurance startup deliver an innovative fraud detection software product. Our data scientists created medical image recognition algorithms for:

  • ML-powered capture and analysis of claim-supporting dental X-ray images.
  • Automated detection of oral health problems.
  • Matching the insights to the data provided in claims.
  • Instant detection of mismatched data and duplicated images.
  • Real-time reporting of fraudulent claims.

How Much It Costs to Establish Insurance Fraud Detection Automation

Implementing insurance fraud detection automation for a midsize company may cost around $200,000–$800,000+, depending on the scope and complexity of the automation solution and the number of integrations.

Below, we provide the approximate cost estimations based on ScienceSoft's experience in fraud detection software projects:


Building a custom insurance fraud detection solution of average complexity that:

  • Spots customer fraud across 1–3 insurance areas: onboarding, payments, claims, etc.
  • Integrates with 1–5 corporate and external systems.
  • Enables batch and real-time data processing.
  • Enables diagnostic and predictive insurance analytics using statistical and non-neural-network ML models.


Developing a comprehensive custom insurance fraud detection system that:

  • Monitors fraud and non-compliance across multiple insurance areas.
  • Integrates with 5+ back-office and third-party systems.
  • Features real-time big data analytics for instant fraud detection.
  • Provides advanced root cause analysis and forecasting using deep learning models.
  • Offers prescriptive fraud handling.

Key Financial Outcomes of Insurance Fraud Detection Automation

  • $1M–$5M+

    reduction in annual fraud detection and investigation costs due to automation.

  • Up to a 90%

    decrease in fraud-associated losses due to timely prevention of undue claim payouts and malicious employee transactions.

  • 5%+

    increase in business profitability due to minimized fraud-related losses and operational expenses.

When to Opt for Custom Insurance Fraud Detection Software

ScienceSoft’s experts recommend building a custom insurance fraud detection automation system in the following cases:

  • You need to automate complex or unique fraud detection workflows, for example:
    • Verifying insurance workflow compliance with internal policies and local regulations.
    • Capturing and analyzing claim-relevant data in various formats, including voice recordings, handwritten text, video, IoT device readings.
    • Validating loss estimates for specific insurance types, such as professional liability insurance or natural disaster insurance.
  • You want to employ advanced technologies, such as ML for prescriptive insurance fraud prevention or smart contracts for automated fraud reporting.
  • You want smooth and cost-effective integration of your insurance fraud solution with all the required software, including legacy insurance tools and third-party systems.
  • You need a flexible solution that can be easily evolved with new capabilities to support sustainable business growth and quickly adapt to regulatory compliance changes.

Insurance Fraud Detection Automation with ScienceSoft

Fraud detection automation consulting

We design the optimal feature set, architecture, and toolkit for your fraud detection system and provide advice on the use of advanced techs. You also receive a detailed project plan with cost and time estimates for smooth implementation.

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Implementation of fraud detection automation

We handle the end-to-end development of a fraud detection automation solution or modernize your existing fraud monitoring tool. You benefit from prompt implementation and high software quality.

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About ScienceSoft

ScienceSoft is an IT consulting and software development vendor headquartered in McKinney, Texas, US. Being ISO 13485 certified, we design and develop medical software according to the requirements of the FDA and the Council of the European Union and ensure software compliance with HIPAA and HITECH regulations.