Financial Services is a heavily regulated industry and organizational complexity that is driven by business segments, product lines, customer segments, a multitude of channels and transaction volumes. The role of data onto the financial services institutes has grown exponentially in recent years and is advancing rapidly. Traditional data solutions were built based on the demands of earlier days using technologies available at that point in time. However, the ever-growing amount of data and the insights that can now be extracted from it have rendered these solutions obsolete. A modern technology and advanced analytical solutions can only handle current demands and achieve business goals. Todays, Machine Learning (ML) gains traction in digital businesses and embraces it as a tool for creating operational efficiencies. The ML algorithm can analyze thousands of data sources simultaneously, something that human traders cannot possibly achieve. They help human traders squeeze a slim advantage over the market average. In addition, it has given the vast volumes of trading operations that small advantage often translate into significant profits. Robust architecture designs is one of the common traits of a successful enterprise financial ecosystem. This article discusses the use cases, benefits and pitfalls and the requirements of ML architecture to financial services institutes. This proposed ML architecture provides a fully functional technical picture for developing a cohesive business solution.

Advanced Analytics, Machine Learning, Machine Learning Model, Machine Learning Architecture, Financial Service Institutes, Digital Business

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