Integrated Data Mapping Engine (DaME) for Financial Services

Abstract

Enterprise organizations have vast datasets that need comprehensive analysis on a frequent basis, in order to manage data and take business decisions based on it. However, we observe that there can be a lack of industry standards for definitions of key terms. Additionally, there is a lack of governance for maintaining business processes. This typically leads to disconnected siloed datasets generated from disintegrated systems. To address these challenges, we developed a novel, integrated methodology DaME (Data Mapping Engine) that performs data mapping using ensemble of NLP techniques.The results from the industrial application and evaluation of DaME on a financial services dataset are encouraging that it can help reduce manual effort by automating data mapping and reusing the learning. The accuracy from our dataset in the application is much higher at 69% compared to the existing state-of-the-art with an accuracy of 34%. It has also helped improve the productivity of the industry practitioners, by saving them 14,000 hours of time spent manually mapping vast data stores over a period of ten months.