The article, Big Data: How CitiBank Delivers Real Business Benefits With its Data-First Approach outlines how big data is taking the financial services industry by storm. Big data is used in many industries including insurance, healthcare and even sports, saving businesses money and providing insight to important trends. Recently, retail and investment banks have begun to collect large volumes of data to improve their analytical landscape. The reason why the banking industry has lagged others in implementing big data analytics is due to the sensitivity of the data, the need to protect the best interests of their clients and the need to make consequential decisions in real time. Nonetheless, the banking industry, led by CitiBank has engineered big data models that span across many different applications, positively affecting many of their business ventures.
Considering CitiBank employees nearly a quarter million people globally and have many different lines of business it would be impossible to implement a uniform analytics program for the entire firm. Therefore, CitiBank analyzes the expected benefits and opportunity cost to determine whether a certain business line would benefit from big data analytics. One specific operation of CitiBank that has benefited from this is their customer retention and acquisition. Citi analyzes data using machine learning algorithms to track spending habits of their customers. This provides valuable information that can be used to make actionable decisions regarding customer service, fraud detection, compliance and more. Citi also uses big data to spot anomalies, which allows them to help predict unusual or incorrect charges. Michael Simone, Managing Director of Data Platform Engineering claims that if these anomalies, or “errant data points” as he refers to them can be identified, Citi can save substantial amounts of capital. Citi has been a catalyst in the big data analytics space in banking but are continuously looking for more effective ways to implement analytics.
Simone states that of the many big data analytics ventures Citi has taken on, only a small fraction of them involve real time decision making. He claims that this is where the industry is focused in terms of innovating their use of data analytics. This part of the article resonated with me greatly as I will soon be graduating and pursuing a career in the financial services industry. Clearly, this is an ever-changing industry, so the importance to be able to see around the corner in terms of what types of innovation are taking place is crucial. It will be interesting to see how the gap between big data analytics and real time decision making closes in the coming years, and what impact it has on the industry. The goal of implementing big data analytics in banking was to help banks serve their customer’s needs more effectively, if analysts can do so in real time, the results could be profound.