Increasing access to financial services data helps accelerate the monitoring and management of datasets and facilitates better business decision-making. However, financial services datasets are typically vast, ranging in terabytes of data, containing both structured and unstructured. It is a laborious task to comb through all the data and map them reasonably. Mapping the data is important to perform comprehensive analysis and take informed business decisions. Based on client engagements, we have observed that there is a lack of industry standards for definitions of key terms and 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 methodology DaME (Data Mapping Engine) that performs data mapping by training a data mapping engine and utilizing human-in-the-loop techniques. The results from the industrial application and evaluation of DaME on a financial services dataset are encouraging that it can help automate data mapping and improve system human-in-the-loop 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.