“The world is one big data problem.”
– by Andrew McAfee, co-director of the MIT Initiative

Financial inclusion is on the rise globally. The 2017 Global Findex database shows that 1.2 billion adults have obtained an account since 2011, including 515 million since 2014. Between 2014 and 2017, the share of adults who have an account with a financial institution or through a mobile money service rose globally from 62% to 69%.
Now consider the amount of data we routinely give away to banks and other organizations. What are the banks going to do with all that data?
There are many ways in which Big Data Analytics are changing Banking. Here are the six most conspicuous big data applications:

Performance Optimization

The very name – Big Data – can be overwhelming. The truth is, however, that the aim of Big Data technologies is to take big volumes of data and use them to simplify tasks.

  • It enables banks to optimize and streamline backend processes through technologies like Machine Learning and Artificial Intelligence.
  • Operations like algorithmic trading and commercial loan agreement interpretation can be made as easy as never before.
  • Scanning through documents available in the cloud instead of sifting through mounds of papers increases accuracy and efficiency.
  • Data-based automation can be improved through Machine Learning to reduce turnaround time in reviewing documents to just a few seconds.
  • Not only do record-keeping and retrieving become more efficient, but man-hours are also reduced substantially, performance is boosted significantly and operational costs witness a remarkable reduction.

Customer Segmentation

Big Data helps immensely in analyzing customer data, segmenting it for banking business and making personalized offers & services based on this analysis.

  • Analytics help banks to transform from being product-centric to customer-centric, which is what the customers demand today. Understanding customer behavior and spending patterns help to segment them and supports the transformation.

When banks divide their customers into segments, based on several variables like demographics, transactions, interactions with the bank, external customer data and customer asset value, it enables them to become more customer-oriented.

Effective Personalized Offers & Feedback Analysis

As Big Data analytics are used to go above and beyond customer segmentation, they can give banks an edge above their competitors with personalized marketing.

  • Rather than rolling out general offers that may or may not be leveraged by all customers, banks can introduce personalized offers to customers who could use them the best.
  • Banks can also leverage Big Data for effective customer feedback analysis since segmentation will be already in place, and based on feedback, even better analysis can be done.

Fraud Detection

Frauds are bigger than ever given the introduction of the digital channels.

  • Big data applications can aid in telling authentic business transactions from fraudulent ones by analyzing customer transaction history, their spending behavior, saving sources, income sources, etc. Unusual activities can often be an indication of fraud.

Notification and subsequent blocking of such irregular transactions can be a step in preventing fraud before it can happen

Risk Management

Apart from the regulatory risk assessment through stress testing, banks can also not only manage but exponentially reduce risk by way of assessing customer profiles.

  • Back-testing historical data can help manage the risk of algorithm trading. In case a risk threshold is exceeded Big Data can enable real-time risk altering to help banks do optimize risk management.

Compliance to Regulations

Since the 2008 financial crisis, the scenario regarding compliance with regulations for Banking and Financial Services is heavy-duty. Monitoring and reporting of every transaction are mandatory.

  • With the assistance of Big Data, this monitoring and reporting of data can be used for surveillance and recognition of trading patterns.
  • Artificial Intelligence and advanced Big Data Analytics are already helping in anti-money laundering function, leading to better risk detection and prevention.
  • In fact, a report from McKinsey & Company, dating back to 2015, revealed that around a dozen European banks were already leveraging Machine Learning based on Big Data Analytics for better function.

With the rapidly changing face of Banking and Financial Services industries, it is important to ride the wave. The power of Big Data can not only enable banks to gain actionable insights but also help them move from a static to a dynamic plan.

Big Data Has Changed Banking For Good!

As the Banking sector struggles to deal with the overwhelming data load, we’ve come forward with solutions that help banks, insurance companies and other financial institutions make good use of these huge data loads through Big Data technologies.

As a specialized Big Data developer and service provider, we have served clients in the United States of America and across the world. Helped financial institutions make the best use of their data, especially when it comes to data security and compliance. We cater to our clients’ needs with customized solutions such as:

  • Performance Optimization
  • Customer Segmentation
  • Fraud Detection
  • Offer Personalization
  • Feedback Analysis
  • Compliance to Regulations

With over a decade of experience in Big Data, Scala and ETL Development covering various sectors of Banking and Financial Services Industry, our proficiency revolved around data technologies. We have been working closely with some of the top 5 US banks helping them enhance their data management capabilities.

For instance, we helped a top 5 US bank with extracting valuable knowledge from huge – both in terms of structure and size – market news files that could not be processed by regular RDBMS systems. Big Data technologies and specifically Spark was used to process huge data files and store them in HBase. While the project started as a proof of concept, after going life it has been in full productive use.

As there is a lot of talk in the market but a lot less action, we enlist below a selection of specific work items we have delivered to our customers:

  • Laid down the foundation of Big Data Architecture and chose technologies to be used.
  • Delivered on Big Data technologies like Apache Spark, Hadoop, HBase, Hive, Impala on Cloudera. Big Data Eco-System was designed to end with back-end batch processing using Scala in Spark, storage using HDFS, database using HBase and Impala, and scheduling using Control M.
  • Worked on the average file size of around 100 GB (often parsed 3 times a day) while maintaining history. The news database had hundreds of Terabytes.
  • Modeled the Hadoop database HBase per the feed file and reporting requirements.
  • Developed efficient transformational and storage mechanisms to process massive data files in minutes.
  • Developed end-to-end pipeline from fetching relevant data files, storing and processing them using Apache Spark and HDFS.
  • Created a querying methodology over HBase to enable reporting.

Author:- Mr.Gagandeep Singh
Dated:- 19/02/2020