"With H2O Driverless AI's MOJO technology, we are able to integrate strongly with our current infrastructure."

Maram Mishan
Actuarial Data Scientist, Airvantage

Use Cases

Risk Management

Overview of the Challenge

When a subscriber uses PAS, Airvantage creates a behavioural risk score. Prior to using H2O Driverless AI, PAS executed a series of static business rules, based on the subscriber’s history, to make airtime advance decisions. Because the risk is directly borne by Airvantage, as authorisers of the advance, the business rules’ risk score must be defensive to mitigate risk. The danger was not false positives – making advances with undue risk. The problem was false negatives – refusing credit inappropriately. Airvantage needed to know whether the current rule-engine-based risk model was overly cautious and was declining desirable customers. As a result, they used real world customer data to train a new model that re-evaluates rejections. The Airvantage data science team evaluated over 30 data science toolkits and platforms, and performed Proof-of-Concepts (PoCs) for four of them.