The telecommunications industry loses in excess of $10 billion annually because of bad debt. Risk analytics is a requisite to close up or at least block off the drain of bad debt. Risk analytics predicts early fraudulent and non-fraudulent bad debt, accounts that are least likely to pay, and predict long-term risk. The current methodology for risk management is the statistical regression-based analysis and model approach. However, this approach is not strong enough for the necessary data mining that would uncover undetected risk-predictive relationships. This knowledge can be used for predictive modeling with the standard statistical regression models, and advanced techniques, like artificial neural networks methods. The purpose of this article is to apply the data mining muscle, and the alongst predictive power of the new machine learning method – the GenIQ Model© – for risk analytics.