Projects / Top Talent Project

Optimizing the Accuracy & Explainability of Medical Insurance Claim (Fraud, Waste and Abuse) FWA Detection by Leveraging AI & Anomaly Detection

Project Kickoff: June 1

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This is a paid opportunity. In order to be eligible to apply for this project, you need to be part of the Omdena community and have finished at least one AI Innovation Challenge.

You can find our upcoming AI Innovation Challenges at

The problem

Health insurance fraud is a pervasive issue that significantly impacts the insurance sector globally, including in Saudi Arabia. Fraudulent activities can range from false claims, inflated bills, services not rendered, to more complex schemes involving multiple parties. The client, having established a successful claim management solution in Southeast Asia, faces new challenges as it expands into the Saudi Arabian market. Due to regional differences in healthcare practices, regulations, and types of fraud prevalent, the existing models, which are tailored for Southeast Asia, may not perform effectively in detecting fraud within the Saudi context. This discrepancy could lead to high false positives and missed fraudulent activities, reducing the trust and financial stability of the insurance providers.

Impact of the Problem:

  • Financial Losses: Insurance fraud leads to significant financial losses for insurance companies, which often trickle down to consumers through higher premiums. Inefficiencies in detecting fraud can strain the resources of insurance companies and increase operational costs.
  • Reduced Trust: Inability to effectively manage and detect fraud can erode consumer trust in insurance products. For the insurance sector, maintaining consumer trust is crucial for retaining clients and ensuring the sustainability of insurance offerings.
  • Regulatory and Legal Issues: Insurance companies are typically subject to strict regulatory requirements regarding fraud prevention. Failure to comply with these regulations due to ineffective fraud detection can result in legal penalties, further financial losses, and damage to the company’s reputation.
  • Operational Inefficiency: Without a robust fraud detection system, insurance companies may expend excessive resources investigating false leads or fail to identify actual fraud promptly. 
  • Lack of Explainability: AI models that lack explainability can be a significant barrier in regulated industries like insurance. Insurers need to convince the consumer and medical service providers with easy-to-understand reasons backed by medical knowledge.

This project aims to optimize the accuracy and explainability of AI models for detecting health insurance fraud in Saudi Arabia. By leveraging real datasets from the region and employing advanced AI and anomaly detection techniques, the project seeks to tailor the fraud detection models to effectively identify and explain fraud patterns specific to the Saudi Arabian market. This optimization will not only enhance the detection capabilities but also ensure that the AI system’s decisions are transparent and understandable, which is crucial for meeting regulatory standards and gaining the trust of stakeholders. 

The project goals

The primary goal of this project is to optimize the accuracy of selected types of claims and minimize the no. of false positives within the health insurance sector of Saudi Arabia. This initiative will leverage advanced AI and anomaly detection techniques to address specific challenges in fraud detection using real datasets provided by the client. The project will unfold over the following planned phases:

  • Data Analysis and Feature Engineering: The first phase involves in-depth data analysis and feature engineering tailored to recognize the unique patterns and anomalies prevalent in the Saudi Arabian health insurance sector, which will be also used to develop an Analytic Dashboard to engage users for a better understanding of the AI results. 
  • Model Development and Optimization: In this phase, around 50,000 actual claim data will be provided with standard set of denial reasons for model training and AI models will be developed and optimized specifically for detecting fraudulent activities. This includes selecting suitable algorithms, tuning parameters, and testing different approaches to achieve the best performance in fraud detection.
  • Model Evaluation and Performance Metrics Collection: This is crucial to prevent legitimate claims from being incorrectly flagged as fraudulent, which can create significant issues for hospitals and patients.
  • Explainability Enhancement: Concurrently, efforts will be made to enhance the explainability of the AI models. This involves implementing techniques that make the decision-making processes of the AI models transparent, allowing stakeholders to understand how and why certain predictions are made. 
  • Validation and Refinement: This step ensures that the models are accurate and reliable from a practical, real-world perspective. Feedback from domain experts will be used to refine the models further.
  • Deployment and Continuous Monitoring: Upon successful validation, the AI models will be deployed within the client’s existing systems to monitor transactions in real-time. This integration aims to provide continuous, actionable alerts on potential fraud, enhancing the client’s ability to respond swiftly and efficiently to fraudulent activities. Auto re-fining will be built based on the regular feedback from the users.

Thus, this project aims to deliver an advanced fraud detection system that enhances the accuracy, reliability, and transparency of health insurance claim processing in Saudi Arabia. By providing in-depth insights into claim patterns and potential anomalies, this initiative is expected to significantly reduce financial losses due to fraud, enhance operational efficiency through the Quality Assurance Portal for day to day Claim Management and Analytic Dashboard for senior management engagement, and boost the credibility of health insurance providers.

**More details will be shared with the designated team.

Why join? The uniqueness of Omdena Top Talent Projects

Top Talent opportunities come as a natural next step after participating in Omdena’s AI Innovation Challenges.

Everyone in the community is eligible to participate once they have shown the relevant skills based on the merit of involvement in past Omdena challenges and the community.

If you are looking for the next challenge after participating in one or more Omdena AI Innovation Challenges, then we believe you have made the right choice! With a healthy, pressured environment, you will be pushed to contribute, learn and grow even more.

Find more information on how an Omdena Top Talent Program works

First Omdena Project?

Join the Omdena community to make a real-world impact and develop your career

Build a global network and get mentoring support

Earn money through paid gigs and access many more opportunities

Eligibility to join an Omdena Top Talent project

Finished at least one AI Innovation Challenge

Received a recommendation from the Omdena Core Team Member/ Project Owner (PO) is a plus

Skill requirements

Good English

Machine Learning Engineer

Experience working with Machine Learning, and/or Data Analysis is a plus.

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