Use of Big Data Analytics in Financial services

Posted in Operations & IT Articles, Total Reads: 760 , Published on 13 September 2015

Big data is currently the most popular word in technology world. Big data helps companies to get the important information from the data that is readily available to them. Currently data is doubling every year. Big data helps in handling the unstructured data very efficiently. This unstructured data mainly comes from internet. Now coming to the financial services sector, there is a huge amount of data that needs to be managed efficiently.

image:renjith krishnan,

The Big data technology will help in analysing the data and develop some business insights from the data.  The entire data ecosystem consisting of Big data and Analytics and 3 v’s of big data are given below:

The 3v’s of Big data have a huge influence in the financial service sectors.

1 Velocity : Firms need to complete trades at a faster rate to have a competitive advantage over competitors.

2 Variety : There are huge types of unstructured data from social media , blogs and semi – structured data such as trade messages are present  in banks that needs to be handled regularly

3 Volume: The financial institutions need to store huge amount of structured as well as unstructured data to make investment and business decisions. This huge data cannot be handled by normal software tools.

There are number of open sources platforms such as Apache Hadoop is available for  data processing. The final service sectors should invest in fibre based architecture that will handle Hadoop and other memory devices to handle data processing

Why financial services should use Big data Analytics

Risk management and better customer services are some of the main reasons of using Big Data   technology. Financial institutions handle a lot of amount of data. This technology helps to prevent data hacking and fraud detection .Thus there is a need that data should be handled properly. There are so many regulatory uncertainties in the financial firms that are forcing them to think about the data management and technology. There is constant threat of bad economic conditions and an urge to increase the revenue and reduce costs. Big data Analytics will provide solutions to these kind of problems. In addition to this, the firms can easily acquire and manage customers using analytics services. Customer needs can be easily satisfied by using this technology.

Advantages of using Big data analytics in financial services sector

Insurance: Insurance companies are susceptible to a lot of risk. This technology provides the underwriter a great boost by storing huge amount of data. The sensors and geographic data provides a large amount of information about home car etc. This tool helps in the following cases :

1. Fraud claim : Helps to recognize false claim by customers

2. Premiums: helps to know about the drivers of the car and can accordingly charge premium from them for providing insurance to their cars.

Commercial Banks: Banks contain a lot of data about customers. They are prone to a lot of credit risk of loan takers from bank. Now days the main concerns of the banks are increase in Non- performing assets and bad debts. Thus banks require a lot of verifications required in banks which help the banks to make more profits and reduce NPA. Big data application will help banks in the following cases

1 KYC: Helps to understand the background of customers. It will help in detecting real time fraud cases

2 Technological advancement: Now days technology is the core part of everything. All consumers prefer a user friendly domain.

3 Social Media : Helps in tracking social media to check the behaviour of customers towards the products

4 Acquiring and retention: Analytics helps in analysing the customer data and hence increases the acquiring and retention levels of customers.

5 Staffing : Helps in analysing unstructured data like voice to anticipate workloads and staffing required in customer care services of the banks

6 Online services: Online services can be provided to retail customers by the banks.

Investment Banking:

Trading: Provides common data stores to speed post-trade settlement and confirmation.

Hedge funds: Big data technology helps in providing in-memory analytics to optimize price discovery and investment mantras for a portfolio of trades and swaps.

Brokerage firms: Helps to distribute market data of different securities to brokers and investors. As a result they can make intelligent investment decisions on basis of the findings from data.

Asset Management :

1 Helps in detecting money laundering cases.

2 Helps in sentiment analysis of customers and thus helps in sale forecasting and brand strategy management.

3 Helps in calculating real time capital at the time of transactions

Challenges faced in using Big data analytics in financial services

There are different types of data from different sources that are incorporated into an analytical platform. As a result of this there is a chance of time lags to impact data currency and thus consistency of data is a very big problem. Big Data is implemented with new tools and is being looked in different ways. There is a shortage of people with the skills to bring together the data, and analyse it and publish the results or conclusions. Data integration and Big data syndication are huge problems in Big data technology.


The financial sector will be hugely benefited by the Big data Analytics tools. Though many companies are sceptical to invest the initial high amount of money to implement the Big data analytics services. In the long run these services will bring huge benefit to them. The cost benefit analysis shows better benefits compared to the cost incurred by the company in the long run. Big data technology will help banks to provide better customer preference. This technology will help the banks to handle a lot of data and gather many information from them. As a result of using the technology the profit and revenue of the institutions will increase. Technology helps to make banking simpler and customer friendly.

The article has been authored by Arijit Chowdhury, GIM, Goa



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