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Application of big data analytics in the banking and finance services industry

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The banking sector has been one of the major beneficiaries of big data analytics from the very beginning. Be it marketing analytics, collection analytics or risk management big data plays an indispensable role. We will try and figure out what makes big data analytics so important for the banking and finance services industry (BFSI) and how, as students of the discipline you can benefit from this.

Data Analytics Utilization

Why is big data important for banks?

The banks around the world deal with an increasing amount of data thanks to digitization. Not only is there an incredible amount of transaction data to deal with but there are KYCs and details of potential customers and borrowers. Efficient and sustainable storage options are available in today’s world hence storing this huge amount of data would not cause much of a problem for the financial institutes. But storing the data is definitely not the only thing they are interested in.

Analytics for optimizing marketing strategy

The BFSI invests a good deal in marketing and the leaders surely want their strategies to yield good results. Any marketing strategy comes with a better chance of failing than succeeding. Data analytics can tilt the balance in favour of the banks. Like every other industry this industry too can use customer information to optimize the marketing strategies. Sentiment analysis can play a vital role in this. Understanding the market, understanding how it impacts the lives of the customers, all can play a role in designing a working marketing strategy. All of this can be done with the help of analytics.

Analytics to aid collection and recovery

When it comes to debt recovery big data analytics can play a crucial role in determining whether to go for an auction or make a distress sell. Analytical models are being used to decide whom to contact and when, in order to smoothen the process of collection.

Risk management

Every financial institute runs with a risk of losing money. The primary reasons why a bank can lose money are fraudulent transaction and loan defaults. It is always better for banks to prevent such occurrences than to detect at an early stage. Analyzing the customer’s data can help the bank detect a potentially fraudulent applicant. It can help the institution avoid situations like a default. Even if you put this aside there is always a risk of being hacked. A sturdy analytics framework can detect any discrepancy in the processes way faster, thus decreasing the risk.

BFSI and the analytics industry

The disciplines of big data analytics and banking have shared a keen bond from the very beginning. The banking sector is the largest investor in service based analytics. So, we can easily contend that this sector creates a lot of data analytics jobs for aspirants with good skills and experience. The market is wide open for new talent. The Malaysian economy has started depending heavily on analytics based solutions for various problems; learning big data analytics in Malaysia is a good idea at this point. 

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