Big data is especially promising and differentiating for financial services companies. With no physical products to manufacture, data – the source of information – is one of arguably their most important assets. The business of banking and financial management is rife with transactions, conducting hundreds of millions daily, each adding another row to the industry’s immense and growing ocean of data. So the question for many of these firms remains how to harvest and leverage this information to gain a competitive advantage?


While the banking industry’s structured customer data is growing in size and scope, it is the world of unstructured data that is emerging as an even larger and more important source of customer insight. Investment bankers, financial advisors, relationship managers, loan officers and countless other front-office employees must have ready access to detailed product and customer information in order to make better and more informed decisions, while also supporting regulatory and compliance reporting requirements.


“Big data”– which admittedly means many things to many people – is no longer confined to the realm of technology. Today it is a business imperative and is providing solutions to long-standing business challenges for banking and financial markets companies around the world. Financial services firms are leveraging big data to transform their processes, their organizations and soon, the entire industry.”


One of the key values of the banking industry has been its ‘Customer-Focused’ mindset, but in the new era, the trend is moving to being ‘Customer-Centric’. This is because advances in technology and communication, combined with an explosive growth in data and information, have given rise to an even more empowered and aware global consumer. With this change in consumer dynamics; the banking industry has an opportunity to develop an improved customer engagement strategy.


The banks have a vast variety and amount of customer data due to an increasing number of transactions through various devices, but they are only using a very tiny proportion to generate insights and enhance the customer experience. Data science goes beyond traditional statistics to extract actionable


Historically banks collected huge amounts of data but were unable to derive meaningful insights in a timely manner, which prohibited them to predict and respond to the changing consumer needs and led to missed opportunities. Today the Banking Industry truly believes that big data analytics offer a significant competitive advantage, and even then, only 37% of the banks actually have any hands-on experience with any live big data processes or policies. Banks are no longer questioning the benefits of big data, but some are still holding back. Apparently, 63% of the banks and financial institutions are just exploring and experimenting with it. As per another study by Dell, only 1 in 5 companies use advanced analytics report or utilize high volume or high velocity data commonly associated with big data and majority of firms seem to have their hands full with their own internal, “small” data. One of the major reasons behind a slower adoption of big data in the banking industry is the organization structure and silos.


According to, Deutsche Bank: Big data plans held back by legacy systems, February 2013, Big Data plans at Deutsche bank were held back due to legacy infrastructure, which resulted in 90% overlap of data since petabytes of data was stored across 46 data warehouses and the bank constantly kept collecting data from the front end (trading data), the middle (operations data) and the back-end (finance data).

E.g. Data Inhibitors for Digital in Banking:

  1. Fragmented Book of Record Transaction systems
  • Lending systems along geographic and business lines
  • Trading systems along desk and geographic lines
  1. Fragmented enterprise systems
  • Multiple general ledgers
  • Multiple Enterprise Risk Systems
  • Multiple compliance systems by business line
  • AML for Retail, AML for Commercial Lending, AML for Capital Markets…
  1. Lack of real time data processing, transaction monitoring and historical analytics
  2. Typically, proprietary vendor and in-house built solutions that have been acquired over the years building up a significant technological debt.
  3. Unable to keep pace with the progress of technology
  4. Data Silos -Move to combine Fraud (AML, Credit Card Fraud & InfoSec) into one platform
  5. Issues with flexibility, cost and scalability

Key Functional Areas:

  • Capital Markets
  • Asset & Wealth Management
  • Cyber Security
  • Digital Banking
  • Risk & Regulatory Reporting

The Key benefits of big data and analytics to banking and financial industry can be:

  • Business Profitability
  • Customer attrition
  • Customer acquisition costs
  • Risk Management
  • Fraud Detection
  • Anti-money laundering
  • Effective Marketing
  • Managing Reputational risk
  • Broker and trade compliance
  • Reduce data warehouse costs
  • Adopting New sales strategies

As a result of adopting big data analytics, the banks can answer questions like:

  1. What’s really happening across the customer journey?
  2. Which campaign combinations accelerate revenue?
  3. Which offers drive customer loyalty?
  4. What credit card behavior signals potential fraud?
  5. How can we assess customer risk before extending credit?
  6. How can we create more targeted campaigns?
  7. How can we acquire new customer with a reduced cost strategy?
  8. How can we know in advance is a customer is about to leave us and be proactive to reduce customer attrition?
  9. What can we do to make our customers happy?


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