AI & ML Model for Insurance Industry to identify anomalies & detect fraudulent claims

Business Challenge

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Fraud detection and prevention: Insurance companies must be able to detect and prevent fraudulent claims, which can be a significant challenge, especially with the use of sophisticated methods to conceal fraud.

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Claim assessment and processing: Assessing and processing claims can be a time-consuming and labor-intensive process, and may require a significant investment in technology and personnel.

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Compliance with regulations: Insurance companies must comply with a wide range of regulations related to claims handling and processing, which can be complex and challenging to navigate.

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Managing customer expectations: Insurance companies must manage customer expectations and provide clear and timely communication about the claims process and the status of claims.

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Data management: Insurers must be able to collect and manage large amounts of data related to claims, including information about policyholders, claims history, and loss data.

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Litigation and disputes: Insurance companies may face challenges in dealing with disputes and litigation related to claims.

Solution Approach

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As technology advances, the banking sector must also adapt to improve its ability to detect cybercrime and spot anomalies.

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Automated fraud detection can improve the accuracy of determining fraud risk, reduce false positives, and lower overhead costs.

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Our proposed solution, OptiSol helped a finance company develop a machine learning-based fraud claims detection solution, which improved accuracy by 90% compared to manual processes.

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The solution is able to identify claims that stand out as anomalies and the variables that cause them.

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Data is analyzed to identify and understand abnormal data points and the variables that may cause anomalies.

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The isolation Forest and Support Vector Machine (SVM) model were used to identify anomalous claims.

Business Impact

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Improved accuracy: AI-based systems can analyze large amounts of data and identify patterns that may indicate fraud, which can improve the accuracy of fraud detection and reduce the number of false positives.

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Increased efficiency: Automated fraud detection can reduce the need for manual review and investigation of claims, which can save time and improve efficiency.

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Increased customer satisfaction: By detecting fraud quickly and efficiently, insurance companies can improve customer satisfaction by resolving claims faster and with greater accuracy.

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Better data-driven insights: AI-based systems can analyze data to identify patterns and trends that can be used to improve fraud detection, risk management, and strategic decision-making.

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Identifying potential fraud: Fraud detection systems can identify potential fraud and alert the company before actual losses occur.

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Identifying patterns in fraudulent activities: AI-based systems can identify patterns in fraudulent activities and use that information to improve fraud detection and prevention, and to inform investigations.

Technology Stack

Trusted and Proven Engagement Model

  • A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
  • Our NDA-driven process is established to keep clients’ data and IP safe and secure.
  • The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
  • This helps clarify the goals, and limitations, and deliver quality products & services.
  • Our engagement model defines the project size, project development plan, duration, concept, POC etc.
  • Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).
  • The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.
  • On signing the SOW, an official project kick-off meeting shall be initiated.
  • Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

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50+

AI & ML
Engineers

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40+

AI & ML
Projects for
reputed Clients

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5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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