Balkrishna Singhania

The availability of more data than ever before together with sophisticated modeling techniques and high computational power to process data has changed the landscape for analytics driven business decision making. For instance, AIML techniques are helping companies understand customer behaviour more closely than ever before resulting in very precise customer segmentation and recommendation models. However the full potential of analytic models in business is far from realized (Gartner 2017, VentureBeat 2019 ). There is an interplay between various business components before a model can lead to business action. In successful frameworks good quality data and robust analytic models converge with seamless deployment, clarity of usage at each stage and strategic alignment with the end business user. This is often not the case. In this talk we highlight the difficulties that arise in the model ecosystem and share a use case where we have solved some of these problems to take our model from development to live implementation in the production environment for automated underwriting at HDFC Life.