Recent improvements in computing power, decreasing costs of storage and the availability of suitable infrastructure has renewed the focus on artificial intelligence. We are on the cusp of an inflection point as far as artificial intelligence and its applications are concerned. A lot of focus has been on the ability of the new, emerging technologies to use the available data and create “use cases”. But, for the technologies to fulfill their potential, the availability of rich and high quality data is essential.

A recent Gartner research estimates that poor data quality is responsible for an average loss of $15million per year in all organizations. As far as a financial institution is concerned, data quality does have a major impact on its daily activities. In big financial institutions, especially the sell-side firms, there has been an increasing focus on data quality to build mature data management capabilities. With the changing nature of business, the advent of Fintech and stricter regulatory requirements, these existing approaches to data quality management may not be sufficient as they can be reactive in nature. 

This point-of-view explains how machine learning can help in improving data quality management that can build operationally more efficient financial institutions.

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