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

Business Challenge

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.

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.

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.

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

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.

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

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Solution Approach

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

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

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.

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

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

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

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Business Impact

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.

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

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

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.

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

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.

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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

50+

AI & ML
Engineers

40+

AI & ML
Projects for
reputed Clients

5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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