The Rise of Big Data Analytics in Investment Banking Market

The competitive landscape of a market explains the strategies incorporated by the key players. It may be for investment, marketing, or product development. Moreover, the rising cost pressures and evolving customer expectations direct the strategies for a company to adopt or else zone out in the competition.

In this current market scenario, the certified investment bankers and key players are enabling their investment banking industries toward a value-based pricing approach. Investment banking industries are leveraging the use of big data analytics to deliver better transparency and improve proficiency in their services.

Big data analytics can speed-up time-consuming tasks like –

  • valuing portfolios of assets, and
  • approving client mortgages

Further, it helps to reduce churn rates and improve pricing accuracy. In investment banking, volume and velocity of data are the driving factors to implement big data analytics. Some of the key players who are into big data analytics are enlisted below as references.

  • Goldman Sachs has taken the lead by investing $15 million in big data analytics. The automated technology enables Goldman’s front-office teams to answer millions of complex questions regarding global market scenarios.
  • Deutsche bank’s corporate and securities business have developed a new approach to customer data analysis. It appointed Sam Wisnia as head of FIC structuring and strategic analytics for the corporate banking and securities division.
  • JP Morgan Chase has developed Contract Intelligence (COiN), a machine-learning algorithm to analyze documentation and extract significant information.

Financial sectors are the most-intensive sector in the global economy and the use of big data cannot be overestimated. As a result, we find several players gradually moving toward technology and analytics usage.

Moving forward, let us see how exactly big data analytics helps the industry.

Big Data Analytics in Investment Banking and other Financial Sectors: Use Cases 

Big data ensures personalized and secure financial services for banking. It can be integrated into every decision-making process as dependent on actionable insights from clients’ data. The list of use cases is growing every day.

  • Financial services can collect customers’ social media profiles, understand sentiment analysis, and create a credit risk assessment
  • Establish automated, accurate, and personalized customer support service
  • Implement incentive optimization, attrition modeling, and salary optimization by human resources personnel
  • The financial industry is shifting toward customer-centric models through customer segmentation
  • Manage market, detect fraud, credit, and operational risks
  • Improve predictive power regarding risk models and develop risk management methods
  • Process automation in terms of compliance checks and data entry
  • Deliver innovative services and implement risk management strategies

The Final Note

Data is driving the business environment and is becoming critical to make concrete decisions. It allows for decisions involving actions to derive short- and long-term benefits. It helps to reduce costs incurred and maximize profits.

Big data analytics is revolutionizing the business world and industry including finance sectors. It enables the investment banking industry to manage and analyze huge volumes of structured and unstructured data as well. It drives improvements in customer experience, business, and maximize return on investments (ROI).

Investment banks can grasp the enormous potential by deploying advanced analytics in their day-to-day activities.

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