Increase your denial rate


save costs and reduce payment of fraudulent claims


automatically categorize your risk scoring into low, medium and high risk claims


process high-risk claims faster


reduce the manual labor

Automate your claims processing


increase the efficiency in claims processing


increase the accuracy in claims processing


reduce labor costs invested into processing claims

Increase your claim recovery rate


evaluate and compare the effectiveness of your TPAs


better provider and network risk management

Real time control

RiSIC is a real-time machine learning engine that analyses claims before decisions are made and flags up unexpected outcomes for human intervention – helping insurance providers clamp down on waste, fraud and abuse as they occur.

No investments needed

Not only does RiSIC connect with existing systems seamlessly without needing disruptive integration, but you also only pay when RiSIC starts saving you money. This means that you can start gaining control of your costs without up-front expenditure, and without risk.

Continuous improvement

Unlike static rules-based engines, RiSIC learns and improves all the time. Each doctor’s prognosis and claims processor decision makes the system more accurate – making it more efficient at streamlining your costs.

Beneficial for all stakeholders

RiSIC does not just help insurance providers. By stamping out waste, abuse and fraud, it helps lower insurance premiums to the benefit of patients and employers. It also helps hospitals and healthcare providers identify wasteful procedures to deliver more effective healthcare. On a population level, it supports the health system by contributing to the management of the medical inflation.

Trained for GCC medical case appraisal

We built RiSIC leveraging our decade long experience in analyzing healthcare claims and in collaboration with GCC based healthcare insurance company. Training the RiSIC engine with market specific claims data and decisions makes it incredibly well tuned and capable of detecting localized behavior.