Webinars

The Multi-Armed Bandits problem is a classic reinforcement learning problem that demonstrates the exploration–exploitation tradeoff dilemma. To correctly estimate the causal effect of price changes or different financial product bundle offers on consumers’ purchasing decisions, the standard methodology is to perform a randomized controlled trial (RCT). However, RCTs could be expensive, and financial institutions are often reluctant to conduct them for the required period of time.

The past year turned usage-based insurance policies from niche offering to a popular choice by consumers everywhere. As a reaction, many carriers rolled out their first telematics-based products, with the goal to scale up to meet the demand, face competitors and follow the changing moods of a highly volatile market.

Peter Reynolds from Earnix talks about how organizations can create a system that enables advanced analytical data handling at scale, smarter automation, and real-time price deployment.