Use of ‘Data Analytics’ in the Banking and Financial Industry – A Scrutiny

The platform of Big Data and Analytics enables organizations to tackle various complex problems that were previously not easy to solve. This platform is useful in helping the enterprises across all industries. According to a research conducted by Capgemini and EFMA, only about 40% customers believe that the banks understand their needs and preferences adequately. This happens mostly because, though the banks have a large volumes of customer data but they are using only a small portion of that data to cause cognizance that enhances the customer experience.

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Data Analytics can benefit an organization in the following ways:        

  • It enhances the systematic evaluation of potential risks
  • Able to detect prospective delinquency that couldn’t be detected before
  • It offers better comparison of the data for improved fraud risk decision making
  • The misconduct could be detected at the early stages
  • Assists in planning our audits or investigative fieldwork
  • A large amount of data can be evaluated in a shorter span of time
  • It is cost effective
  • The non-structured data formats can be analyzed simultaneously with the structured formats.

The Real time insights of the applied analytics solutions for the banking industry:

  • Manages risk exposure and capital in real time and ensures compliance with Basel III and via SAP for the banking industry with the applied analytics solutions.
  • To leverage deeper customer, channel, and also some insights from social media to enable the creation of finer customer segments and targeted products and also boost both customer satisfaction and profitability.
  • Segmentation, selling and retention of customers using authentic models of prediction and the Big Data
  • The products and services could be improved more with integrated social media and transition data
  • Effectively manage fraud and risk by linking the accesses and control to corporate goals

Recommendations to extract more value from Big Data, tailored to the Bank

Step1. Commitment towards more customer-centric outcomes: The Bank should focus big data initiatives with customer analytics by being able to appropriately understand customer needs and anticipate future behaviors and enable better service to the customers. The analytics helps in fueling the insights from big data that are becoming essentially important to create the level of depth in the relationship with the customers.

Step2. Prepare a business centric blueprint of the Big data strategy:  This blueprint would define what the organization aims to achieve with big data to help ensure realistic acquisition and use of resources and create sustainable business value. The development of the blueprint helps engage the business executives early in the development process.

Step3. Start with the existing data to achieve proximate results: Looking within the organization for new insights, towards the existing data storage, skills and infrastructure and extending existing capabilities to address more complex sources and data types. The big data analytics can help accelerate the speed to benefit and enable organizations to take advantage of the information stored in the existing repositories while the infrastructural implementations are underway.

Step4. Building Analytical Capabilities based on the Business Priorities: The bank should derive the organization’s development towards the big data capabilities, especially when the banks have to face the regulatory compliance and tight margins in today’s time. Such capabilities can reduce costs and increase revenues, which can help in offsetting necessary investments.

Step5. Based on the measurable outcomes, the bank should create a business case: The active involvement from one or more business executives is important to develop a viable and comprehensive big data strategy. Along with that, a strong IT collaboration is equally important.

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