Tracing Opportunities for Data Analytics in Banking Industry in Today's World

Financial inclusion refers to the introduction of financial services for the poor, vulnerable, and marginalized sections of the population. Since the dawn of this century, it has been one of the foremost priorities for developed and developing nations across the world. While financial inclusion is a noble policy, it increases the burden on the banks substantially in terms of managing their operations. In order to ease this burden, many technical experts and economists have suggested the introduction of data analytics in banking.?

Why the need for data analytics??

Banks deal with different types of customers every day. Their needs and demands differ, and so do their credit ratings. In such a case, banks cannot afford to have the same approach toward all and have to treat each customer on a case-by-case basis. Data analysis in banking aids in customer segmentation, with the help of which banks get a clear idea of what their customers want from them. This helps with better targeting and delivery of financial services as banks can tailor their approach towards each individual customer.??

Another important area where advanced analytics in banking becomes crucial is fraud detection and risk management. Big data, due to its inherent nature, can analyze huge volumes of information and search for patterns that are characteristic of frauds and scams. Thus, predictive analytics in the banking industry help to keep track of the transactions a customer is making, detect anomalies, and predict whether the customer is engaging in fraud or not. On similar lines, advanced analytics in banking can also help in assessing the credit capabilities of a customer and warn the bank of the potential risks involved in offering a credit facility to the customer.?

Latest developments in data analytics in the banking industry?

Just like many other industries, artificial intelligence, and machine learning are expected to play a huge role in advanced analytics in financial services. These technologies are expected to increase the efficiency and accuracy of the predictive analysis, thus helping the banks manage their funds more effectively. Additionally, the integration of natural language processing is expected to aid banks in designing applications that will increase the customization of banking services for customers. Moreover, advanced analytics and the adoption of big data are anticipated to enable banks to respond to changes in the market and make real-time decisions in a shorter span of time. Global data analytics in the banking market is, thus, expected to grow significantly in the coming period on account of the increasing adoption of advanced technologies.?

Strategic acquisitions and partnerships?

The future growth opportunities for data analytics in the banking market are huge. In order to capitalize on them, leading players in the industry are forging strategic alliances in the form of collaborations and acquisitions. Not just private banks, but certain central banks, too, have adopted data analytics for better management of their national economy. For instance, the Reserve Bank of India, India’s central bank, has partnered with McKinsey and Accenture, to introduce artificial intelligence and machine learning for bettering the supervisory role of RBI. The trustworthiness and popularity of advanced analytics in banking naturally increase when central banks of such huge economies opt for these technologies for their operations.?

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