The data analytics revolution that began around the turn of this century, has found a strong resonance in the banking sector, an obvious area for the data analytics field to flourish considering the massive amounts of valuable data they have been collecting since decades. Analysing this data has now unlocked several secrets about movement of money, besides preventing thefts and disasters and understanding consumer behaviour. Banks are now reaping the benefits of data analytics by interpreting decades of concrete information into meaningful trends, data points, market triggers and consumer behaviour cases.

In an era where banking has become more of a commodity, data analytics has provided a great opportunity for every bank to try and stand out from the competition. Each banking transaction is a data capsule and the entire industry is sitting on an exhaustive vault of such information. Using data analytics for collecting and analysing big data, banks have improved and reinvented almost all aspects of banking operations. Data science, today, has enabled optimised transaction processing, hyper-targeted marketing, personalised wealth management advisory services, highly advanced and predictive risk management processes and more. The potential, in fact, is endless.

Data analysis tools and technologies have been especially effective for banks in combating risks and frauds. In the recent years all major banks have taken big steps to integrate their systems and connect diverse data sources into large digital warehouses that use data analytics tools to instantly forge insights from all the different channels of a bank's operations. This has ushered in a revolution in risk management, courtesy the far deeper and broader visibility into customer relationships and behaviour. Banks can now target fraud prevention and risk mitigation by leveraging the insights they get from analysing the data at their disposal.

Banks are also increasingly using both internal and external data analytics. Voice, video, social media, geospatial, and all other forms of unstructured data are today playing a key role to know customers better and predict their future behaviours. They are looking at both structured and unstructured data for developing banking products. Customers, today, are more attracted and retained with personalised products. Hence, their lifetime value to the bank goes up as well.

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Besides, big data management and data analytics are playing an increasingly crucial role in the recovery of bad debts. Delinquency status of an account is typically targeted for recovery functions. Needless to say, better knowledge about customer circumstances would improve targeting and have an instant impact on the recovery rates, and at the same time reduce costs and bad debts

White conventional data sources like transaction history, identity verification, and credit bureau information continues to be important for risk management and mitigation, hackers have become more innovative in devising newer methods for defeating the customer-identity based security system. At the same time, large scale data thefts have become so common that embezzlers have a seemingly infinite supply of compromised data that includes credit card number, usernames and passwords. This enables them to bypass simple authentication processes.

The reality has driven banks and financial institutions to instil complementary data analytics solutions. These include device intelligence, and malware and anomaly detection as extra layers of the risk evaluation process. These tools are fundamentally different from the credit-based or standard identity system because they consider all account credentials-particularly identity data-as compromised. These add-on data analytics components, as a result, are used in evaluating the risk based on the device's attributes, a customer's typical credit and purchasing behaviour, or signatures indicating intrusion or presence of malware.

Harnessing the power of data analytics can undeniably enhance a bank's performance. However, it is a more strategic question, rather than a technological one. It is important how a bank gets genuine insight from its databank and modifies its interaction with customers, peers, and the market at large through fact-based decision making. Organisations that master this would emerge as trendsetters in customer service, improved profitability, and prompt response, thereby catering to the competitive demands of the banking as well as the financial industry at large.