Going Hand-in-Hand: Big Data and Banking
Going Hand-in-Hand: Big Data and Banking
A lot of developers work in the FinTech industry, so we thought it would be interesting to take a high-level look at how big data is therein being applied.
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Big Data and Banking
Banks are digitally transforming themselves at a fast pace with advanced branchless technology and contemporary services. The latest buzzword in the FinTech industry is chatbots, which have been adopted by almost all leading banks to make their customer service readily available to clients round the clock. So now, what is next? Big Data? But banks across the world are already using data analytics to upscale their business. However, tech experts believe that banks are still to realize the full potential of Big Data. While the BFSI sector creates an enormous amount of data every second, is it able to mine this voluminous amount of information?
Maybe it is time, say some. Big Data, which is defined by the volume of data, the variety of data, and the velocity of processing the data, presents big opportunities for financial institutions. Many of these have even transformed themselves with the help of data mining. While banks have been slow in the adoption of this technology due to the confidential nature of their data, the trend is seeing a positive change. Let’s take a look at some advantages of deploying Big Data techniques in banking.
American worldwide management consulting firm McKinsey Company says that marketing productivity can be boosted by 15-20 percent if companies use data and Big Data to make better marketing decisions. From ‘Product is King,’ BFSI strategies now focus on ‘Customer is King’ and it has become important to focus on what they need and expect from a bank and financial institutions. To understand this, just a few customer snapshots won’t make the deal, a data hub needs to be created with all the information about the customer and their interactions with the brand like personal data, transaction history, browsing history, service, and so on.
These customer insights generated by data-based analytics can empower the BFSI sector to segment customers and target them with appropriate material.
Banks and other monetary institutions can and are already using Big Data analytics to distinguish between fraudulent activities and genuine business transactions. Investigation and machine learning can both help determine standard movement in view of a client's history and differentiate it from unordinary conduct. The investigation can also suggest remedial activities, for example, blocking crooked transactions, deriving from actions taken in the past. It will not only stop misrepresentation before it happens but will also improve profitability.
What your employees feel about working for your company has a lot to do with what your end customers will experience. A higher level of satisfaction among employees will also extend to your customers and will push business growth. Big Data can help companies look at real-time data and not just annual reviews which are usually based on human memory. With the correct tools in place, companies can measure everything from individual performance, teamwork, inter-departmental interaction, and the overall company culture. When the data is related to customer metrics, it can also enable employees to spend less time on manual processes and more time on higher-level tasks.
Once the sorting is done and useless data can be justifiably thrown out, the remaining crucial data can help banks grow by leaps and bounds. Besides helping banks deliver better services to their customers, both internal and external, Big Data is also helping them improve on their active and passive security systems.
Big Data is already playing a role in the banking sector with many banks and financial institutions capturing customer related data for sentiment analysis, starting from social media websites to various market research channels.
Transactional analysis is being used to fathom spending patterns of customers, assess consumer behavior based on channel usage and consumption patterns and segment consumers depending upon the aforementioned attributes, and identify potential customers for selling financial products.
Most of these findings can be applied easily into fiscal systems of banks aiding them to reinforce data security and avoid any type of attack. A combination of many such transactional and sentimental gauges can help banks arrive at a holistic decision-making approach and thereby implement erudite machinery, a need of the hour for the banking sector.
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