Big Data in Banking, all that You Should Know

Banking and the Financial Services Industry is a domain where the volume of data generated and handled is enormous. Each and every activity of this industry generates a digital footprint backed by data. As the number of electronic records grows, financial services are actively using big data analytics to derive business insights, store data, and improve scalability.

Technology has made the Banks to work in tandem to harness the data for intelligent decisions. This has prompted many BFSI organizations to disrupt their analytics landscapes and gather valuable insights from immense volumes of data assets stored in their legacy systems.

Harnessing Big Data in Banking

Following the Great Recession of 2008 which drastically affected global banks, big data analytics has otherwise enjoyed decade old popularity in the financial sector. When banks began to digitize their operational processes, they needed to ensure different means which were feasible to analyse technologies like Hadoop and RDBMS (relational database management systems) for their business gains.

These business gains have been made possible with the existing data analytics practices that have simplified the monitoring and evaluation of the vast amounts of customer data which include personal and security information. With great trust on technology to handle the growing customer volumes and more transactions, the overall service level offered by the organizations has also enhanced.

Working with Big Data, banks can now use a customer's transactional information to continually track his/her behaviour in real-time, providing the exact type of resources needed at any given moment. This real-time evaluation boosts the overall performance and profitability of the banking industry thrusting it to further into a growth cycle.

Banking is an industry which generates data on each step, and industry experts believe that the amount of data generated each second will grow 700% by 2020. The financial and banking data will be one of the cornerstones of this Big Data flood, and being able to process this data goldmine means gaining a competitive edge over the rest of the financial institutions.

The Four Pillars of Big Data

The big data flows can be described with 3 V's. That includes variety, volume and velocity. Here is how these relate to the banks:

•  Varietyis the different data types processed. Banks have to deal with huge numbers of various types of data day in and day out. From transaction details to credit scores and risk assessment reports, the banks have troves of customer data.

•  Volumeis the space that the data will take to store. Giant financial institutions like the JPMorgan Chase., China Construction Bank Corporation, and BNP Paribas, etc. generate terabytes of data daily.

•  Velocityis the speed of adding new data to the database. With the volumes that the banks of today work on, handling 1000+tranactions is not a hypothetical figure.

These 3 V's are useless if a business does not have the 4'Th one which corresponds to Value. Value for the banks corresponds to applying the results of big data analysis real time and to make business decisions.

The banks can make strategies based on these pointers:

•  Customer segmentation based on their profiles

•  Cross-selling and Up-selling based on the customers' segmentation

•  Improvement of customer service delivery on based on their feedbacks

•  Discovering the spending patterns and making customised offerings

•  Risk assessment, compliance & reporting that aid to fraud management & prevention

•  Identifying the main channels where the customer transacts like credit/debit card payments and ATM withdrawals.

Banks have several used cases to showcase the different ways where the data have been harnessed and used for intelligent analysis. This data opens up new and exciting opportunities for customer service by improving TAT, and customised service offerings.

Improving Customer Experience

With so many financial institutions in the market, it gets tough for the customer to decide which bank to transact with. Customer experience, in this case, becomes a deciding factor. Big data analysis presents with the customised analysis for each customer, thus improving their services and offerings.

Personalised Marketing

Big Data is used for personalized marketing, targeting customers on the basis of their individual spends.  Analysis of the customer behaviour on social media through sentiment analysis helps banks create credit risk assessment and offer customised products to the customer.

Optimized Operations

Big data can be applied to bring immense value to the bank in the avenues of effective credit management, fraud management, operational risks assessment, and integrated risk management. Systems that enable with Big Data can detect fraud signals further analyse them real-time using machine learning, to accurately predict illegitimate users and/or transactions, thus raising a caution flag.

Conclusion

The BFSI industry will obtain a better grasp of its needs, by aligning with the latest technologies like Big Data and the other global trends both internally into their operations and with customers. This will help the BFSI industry to provide improved services in a timely manner with optimized operational costs. Though the implementation of Big Data on a large scale has just started to evolve in the BFSI industry, the sooner organizations adopt Big Data practices, the quicker they will be able to unlock the benefits this technology brings to their business.