Posted on 25th September, 2019 | Category: Business | 1 min read
In a data driven world, while we move on from Human-Off to Machine-On paradigm, it is imperative not to forget the role of interpretability of the results thrown by a machine. One of the biggest challengesthat Decision makers are facing today while implementing ML/AI algorithms remains accurate interpretation of the results that human can understand easily instead of the “black box” engine that Analysts projects them to be.
Machine Learning is nothing but the scientific study of algorithms and statistical models that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.Thus, often it becomes difficult for Analysts to explain the influence of model parameters on outcome to the business folks.As is the case with any business, without proper understanding of the impact of various parameters, business leaders will never let the models get implemented. More so in the field of Finance where stakes are very high.This limits the implementation of the complex models like Deep Learning that actually gives significant lift over traditional models.
In this series of articles, we will discuss how various ML/AI algorithms are being used in the lending spaceof a customer life cycle journey – Market, Sell, Bill, Deliver, Support and Retain. How Omni channel Customer Engagement enhances Customer Experience. How Analytics & Advance technological applications are used for Business Process Optimization leading to better ROI.
The application areas of ML/AI in Fintech that we are going to cover includes: