High time to reduce medical claim abuses in the Middle East
Healthcare fraud is increasing day by day in the Middle East countries. Reports show that about 30% of healthcare companies’ expenditures are based on a fraudulent medical claim. Firstly, let us know about the definition of healthcare abuse in the Middle East. It is basically the usage of unethical practices which does not follow prescribed
Machine Learning: The game-changer in the Middle East
Machine Learning is the need of the hour as it helps in the development of computer programs, which can access a variety of data in a short time. These programs can learn from their own experiences with databases without any explicit programming. They use concepts of Artificial Intelligence and analyse data very fast and efficiently.
Machine Learning in health insurance without operational disruption. Myth or reality?
October 3, 2019. Key health insurance stakeholders met at 5th MENA Health Insurance Congress to discuss the current state of health insurance in the Middle East and its trends. Netcetera Middle East was an official Silver Sponsor and a Machine Learning expert.
Rule-based systems vs. Machine Learning
We can safely predict that in the next 5 years most of the rule-based insurance systems will be replaced by more dynamic Machine Learning based systems for claims handling and WAF detection.
Calculate your potential ROI with RiSIC Calculator
We are happy to announce the launch of RiSIC Calculator. Estimating your potential ROI has never been easier! RiSIC team worked hard to convert data into real world results. RiSIC Calculator has been developed in our labs to enable insurance providers with an easy to use, yet efficient tool to estimate the potential ROI
Artificial Intelligence partner at WTIC
WTIC 2019 is the definitive ultimate forum to meet leading minds from Takaful, InsurTech and Insurance Innovation, with representation from over 35 countries, ranging from key markets for Takaful such as the GCC and South East Asia to emerging markets such as Africa and Europe.
Machine Learning powered risk classification to detect waste, abuse and fraud in health insurance claims
Machine Learning and more generally Artificial Intelligence are again at the peak of expectations. The availability of large amounts of data and affordable computing power helps spur new research that contributes to the increased interest in this field. Between all the hype around super intelligent computers that may outsmart humanity, we are daily witnessing the emergence of new practical tools. These tools focus on a specific domain and address a limited problem.
Tinker, tailor, doctor, fraudster. Middle East Insurance Review, February 2019.
This is an inside story how our team of data scientists, machine learning experts and software developers decided to tackle the huge issue of healthcare insurance fraud, waste and abuse and potentially transform the healthcare industry in the UAE for good. If you are interested in how RiSIC was born, this article should be an interesting read for you.
Predictive Analytics is a breakthrough in the battle against waste abuse and fraud
Premiums in the United Arab Emirates currently amount to over $9 billion. WAF has become one of the main reasons for this uncontrolled and at times unjustified growth. Existing rule based systems for fraud detection are not in a position to identify fraudsters and prevent losses insurance companies are incurring. Insurers are seeking more adequate and efficient alternatives for fraud detection and loss prevention.