September 25, 2022

Fintech will revolutionise financial sector, advance inclusion – Shri. V.P.Nandakumar


Big Data Analytics and disruptive change

Big data analytics is “the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.” And big data, in this context, is defined as “extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.”

Unlike the manufacturing sector, the financial sector does not involve production or logistics processes. Instead, the sector is required to process a huge volume of real-time transactions backed up by a large volume of historical data to support decision-making. Big data’s potential value is significant here and it will likely set off a series of disruptive changes to the industry

Thanks to technology and modern web based apps, banks can now capitalise on their knowledge of customers’ credit behaviours and build customised and innovative financial products to cater to their requirements. Various traits of customers are being analysed using their social behaviours, spending habits, lifestyle and consumption patterns, and record of re-payment. A customer’s payment profile often reveals the individual’s pattern of behaviour regarding how much they have paid, what they have paid for, who was paid, the banks involved, transaction time and location, and so on. In fact, it says much more about the individual than any social media metric or record.

Consequently, where traditional credit card approval methods use simple indicators like income or employment status, banks can now use data analytics to screen new credit card applicants based on analysis of online information about their consumption habits and (online) purchasing behaviours. Simultaneously, the bank can place this information in the wider context of current economic information and local economic ecosystem to understand their customers’ offline behaviours. Based on the picture that emerges, the bank can decide whether or not to accept new customers into its credit system, having duly weighed the customer’s spending potential against the embedded risks.

In the area of risk management, where big data reworks the traditional risk management model with cloud computing, data analytics can significantly improve accuracy of risk estimation and at a much lower cost. Fraud, default in payments and bankruptcies are some common occurrences which weigh on banks’ balance sheet. Data analytics is being increasingly used tool to minimise such occurrences based on the customer credit history and payments pattern.

While minimising risk and addressing defaults is one aspect, big data analytics comes into its own when banks design their products and offerings based on the peculiar traits of the customer. This is the area of precision marketing, where big data analytics can sift through huge volumes of data, allowing financial institutions to collect and analyze relevant customer data, and thereby provide more individualized and tailored services.



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