The majority of banking organisations continue to rely on legacy infrastructure. As a consequence, they are unable to deal with the constant influx of data, which is required for running big data solutions.
Fremont, CA: Every minute, banks and financial institutions generate massive amounts of data. Banking involves millions of transactions, so the industry is data-intensive by definition.
All of this complex information falls under the umbrella of big data, which is defined as "large, diverse, and complex sets of data growing at an exponential rate." This information has enormous value for banks and financial institutions seeking a better understanding of their product performance, customer base, and industry trends.
With recent technological advancements, a large percentage of customers have begun to use digital banking. Customers can now easily communicate with businesses, research products and services, purchase items, provide feedback, and perform banking tasks thanks to the exponentially growing number of tablets, smartphones, and other electronic devices.
Let us look at the challenges involved in implementing big data in banking:
Challenges in Collating Data
Banking institutions provide a wide variety of services. As a result, banking data is frequently fragmented and stored in various departments. As a result, if a bank needs to create a customer profile based on his investment, it will be challenging because the customer's deposits, loans, and insurances are spread across departments. Compiling all of this information can be time-consuming.
Customer Privacy Concerns
At a high level, the data utilized by big data systems remains anonymous, even though bank can monitor the behaviour of individual customers if they wish. While this information aids in the detection of fraudulent activity, it can also pose a security risk if it falls into the wrong hands. Such security concerns can stymie big data implementation on a large scale.
Legacy Infrastructure Requires Upgradation
The majority of banking organisations continue to rely on legacy infrastructure. As a consequence, they are unable to deal with the constant influx of data, which is required for running big data solutions. Banks that want to integrate big data into their systems must overhaul their existing infrastructure, which is not an easy task
