Infernotions Technologies Ltd

By: Infernotions  09-12-2011
Keywords: analytics, Warning System

Our experience in servicing clients over the last decade has led us to standardized analytical product offerings that are delivered over the Internet as services ‘on demand’. With these offerings our clients integrate cutting edge analytics into their business processes with minimal disruption to operations and with negligible system integration costs.

ClaimsGator is a claims abuse mitigation offering from Infernotions Technologies Ltd. It uses proprietary analytical technology to diagnose patterns of abuse in insurance claims. The results are reported via a hosted web solution. The hosted solution also includes a customizable case management solution. Property and casualty insurers use ClaimsGator to monitor the provider network and to identify instances of fraud in insurance claims. Manufacturers use ClaimsGator as an early warning system to diagnose patterns of abuse and product failure in warranty claim. ClaimsGator protects our clients profits.

BlackBox is a solution (patent pending) for helping manufacturers estimate the risk of product failure due to abuse and for assigning responsibility for product abuse to a person or a process. The solution comprises a tag for recording the magnitude and the timestamp of abuse delivered to a product unit. An analytical engine uses these data to deliver insights on the risks and the responsibility for the abuse. Clients use BlackBox to reduce their warranty risk exposure and to close the revenue leakage in their logistics process.

Keywords: analytics, Warning System

Other products and services from Infernotions


Business intelligence and embedded analytics for guaranteed return on investment

Our consulting approach is analytically rigorous and we use a mix of statistical and quantitative analysis, explanatory diagnostics and predictive models in our work. A multi-year analytical customer relationship management process transformation resulting in year over year improvement in average revenues per customer.