The Challenges of Big Data in the Banking Industry

The Banking and Financial Services industry generates a huge volume of data summing up to over 2.5 quintillion bytes of data. Each activity of this industry generates a digital footprint backed by data that opens endless opportunities for the banking industry to capitalize on that data.

Technology has made the banks towards harnessing the data for making intelligent decisions. In recent years, Big Data analytics is gaining popularity with banks digitizing the operational processes they needed to ensure different means, which are feasible to analyze technologies like Hadoop, RDBMS for their business gains.
The benefits of Big Data in banking –

According to the study of IDC, the global revenue for Big Data and business analytics solutions is expected to reach $260 billion by the end of 2022. Working with banks can now use a customer’s transactional information to continually track their real time thereby providing the exact type of resources required or needed at any given moment.This real time evaluation helps in boosting the overall profitability and performance of the banking industry pushing them into the trajectory of the growth cycle. The other benefits of Big Data for the banking industry include-

  • Big Data provides a full view of banking business
Customer experience/ behavior plays a crucial role in internal process efficiency. Big Data analysis provides a full view of customer experience such as like claims analysis that helps in making informed, data driven decision.

  • Big Data for personalized marketing
Big Data is now being used for personalized marketing, targeting customers based on their spending. Analysis of customer behavior on social media by using sentiment analysis can help banks in creating a credit risk assessment thereby offering customized products to the customers.

  • Big Data analytics helps banking sector in enhancing their cybersecurity and in reducing risks
Systems enabled with Big Data can detect fraud by using intelligent algorithms. Big Data can be applied to bring immense value to the bank in the avenues of effective credit management, fraud management, operational risk assessment and integrated risk management.
Challenges of Big Data implementation in Banking and Financial Sector

The Banking and Financial Services industry can obtain better grasps of its needs by aligning with technologies like Big Data thereby providing improved services in a timely manner with optimized operational costs. However, some challenges or road blocks needs to be addressed and these challenges include-

  • Outdated systems that struggle to keep up
The banking sector is slow to innovate when compared with other sectors. Still,a majority of the world’s leading banks rely on IBM mainframes for their operations. When compared to the customer centric and agile startups traditional banking institutions are far behind.When it comes to big data, the situation is even worse with most legacy/outdated systems not able to cope up with the growing workload. Huge amounts of data when collected and stored using outdated infrastructure can put the stability of the entire system at risk.

  • Bigger is the data higher is the risk
Banking providers with huge data in their legacy systems need to take into account the safety of data at all times. Studies have shown that only 38% of banking organizations globally are ready to handle the risk associated with the safety of the data they have in their systems. Cybersecurity remains a burning issue for the banking and financial sector.

  • Lower levels of data maturity
Data maturity is essentially the extent to which banks and other financial institutions are utilizing their data to get the most out of it. The more highly data is esteemed and the more sophisticated the techniques to analyze the data decides the data maturity of the organization.

Studies have shown that most of the banks/financial institutions did not rank themselves very high regarding data maturity. Therefore, banking institutions should understand the importance of progressing quickly to a higher level of data maturity. An increased data maturity improves predictive analytics capability that in turn positively influences an organization’s ability to plan.