Data Storage

How Big Data in Banking Can Disrupt the Financing Sector

Data has become crucial in helping businesses align their goals with modern technological advancements. Big data in banking has impacted the value-based pricing models that enable banks to mitigate financial fraud. It also helps the financing sector reduce customer churn rates and increase customer satisfaction. It has become crucial for businesses to acknowledge the competition and familiarize new technologies to perform efficiently. In one of their articles, the Investment Banking Council of America shares how big data in banking can reinvigorate the finance industry.

Weak Points of the Modern Banking System

The banking industry has several concerns they must address in order to satisfy customers’ needs and generate profits. Some of those concerns are:

  1. Inability to monitor and evaluate a large amount of unstructured information
  2. Lack of coordination that will incorporate sales into customer satisfaction
  3. Categorically assessing customer behavior
  4. Introduction of offerings that do not contribute much to the business or customers
  5. Inability to develop long-term relationships with customers

Fields Big Data in Banking Can Reinforce

Here is a list of different aspects of banking that big data can optimize:

  1. Continuous data growth to assess information at a better response rate
  2. Real-time analysis of data and management of prominent data structure
  3. Reducing the risk of fraud and enhancing the security framework
  4. Incorporating customer insights and marketing data analytics to improve the overall performance of the firm

Case Study: JP Morgan

JP Morgan is a fine example of how you can incorporate big data in banking. Here is how they enhanced their business with the help of big data:

  1. Introduced data processors and managers
  2. Provided customers insights and were aware of the market trends

Case Study: Deutsche Bank

Deutsche bank uses big data that helps the firm in various ways:

  1. Allows them to trade and mitigate business risks
  2. Improves customer intimacy through automated personalized recommendation algorithms
  3. Lessens the possibility of fraud
  4. Manages unstructured data

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