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Big Data use in Banking and Financial Services

In Big Data Analytics by dunritetech14 Comments

The amount of data generated and handled in the banking & financial sector is tremendous. But with Big Data analytics, the banking & financial firms not only simply store data but use it to generate business insights & add value. More than 28% of financial firms have already implemented Big Data and are obtaining a competitive advantage. According to the recent study by IDC, the worldwide revenue for Big Data analytics solutions is expected to reach $260 billion by 2022.

Here are the top 5 advantages of using Big Data in Banking and Financial Sector

  1. To Prevent Errors and Frauds:
    Big Data analytics enables you to analyze all the data at once to generate the insights and metrics needed to address the fraud and compliance-related challenges. Banking firms use Big data to identify & address irregular transactions before they occur. With fraud detection algorithms, customers who have poor credit scores can be easily identified and they can be denied loans thus minimizing the financial risk for the banking and financial firms.
  2. Personalized banking solutions to the customers:
    Big data analytics enables the banking and financial sector to narrow their understanding of customers’ needs, pinpoint problems that need attention and find the best way to fix the existing problems. It aids the banks in understanding customer behavior based on the inputs received from their investment, shopping patterns, etc. This further helps the banking sectors to find new ways to cater to their customers and deliver more value. According to Oracle, the ability to offer customized solutions to the users can bring you up to an 18% higher annual revenue.
  3. Enhanced Risk Management:
    While all firms regularly need to monitor and assess risk management, the need may be the highest for the banking and financial firms. Big data finds application and brings value in various areas like fraud and credit management, market and commercial loans, and operational risks. Big data technology can boost the predictive power of risk models, enhance the system response time, and offer extensive risk coverage.
  4. Business operation Optimization and Automation:
    Around 30% of all work in banks can be automated through technology thus enabling the banks to experience cost savings and reduce the risk of failure by eliminating the human factor from the critical processes. Big data analytics makes it so easy to combine, integrate and quickly analyze all of the data at once – regardless of source, type, size, or format – to generate the insights that are needed.
  5. Enhanced Employee Performance and Management:
    By using Big Data tools and techniques correctly, companies can track, monitor, and analyze the performance metrics of their employees. This will help them in identifying the strong as well as the weak or unhappy performers in the company. Accordingly, companies can reward the top performers and take initiatives to improve the condition of weak performers as well.

Banks need to adopt data-driven approaches if they want to stay relevant and competitive. Looking to explore in this area but struggling to find appropriate Big Data applications in the banking sector for your business? Contact us today.

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