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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.  

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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

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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.

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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.

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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.

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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.

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The National: RiSIC spotted almost 37.000 bogus claims, saving insurers millions of dirhams

An analysis proves it: RiSIC could save millions in the healthcare industry. The risk classification system, based on artificial intelligence, can help identify waste, abuse and fraud in health insurance claims – currently tailored for GCC region. The results of the first claims analysis by our machine learning solution were presented at the National – a leading daily newspaper in the United Arab Emirates.

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