Machine learning (ML) provides institutions with predictive analytics that leverage large amounts of both historical and continually-generated data to obtain actionable insights. These insights, in turn, allow institutions to design and deploy interventions to increase revenue, lower costs, and compete in a dynamic marketplace by providing more customer-centric products and services.
However, few understand or have visibility into the inner workings of machine learning models so many are left wondering how good the insights actually are. For skeptics and experts alike, BFA has created the Truthalizer, a verification process that uses an out-of-sample dataset to evaluate the efficacy of a particular machine learning model as well as evaluate the intervention it triggers. As such, the Truthalizer can help leaders understand the quality of insights and how much to rely on them. Read more here.