Hadoop is present in all the vertical industries today for leveraging big data analytics so that organizations can gain competitive advantage. With petabytes of data produced from transactions amassed on regular basis, several banking and financial institutions have already shifted to Hadoop. The ones who have not are making hadoop adoption in the enterprise as a priority in 2015 as they do not want to risk huge market share loss. Here are some essential and intriguing big data analytics use cases financial services must incorporate to minimize risk and stay ahead of competitors in the banking and finance industry.
The financial services industry trends have experienced an unprecedented churn in the past decade. The boundaries of financial services are constantly being pushed by introducing newer, more innovative technologies for analyzing financial big data and ensuring secure, cashless transactions such as internet banking, card based transactions and mobile based transfers.
On the other hand, the governments of developing countries are leaving no stone unturned to include more people to the banking and financial system by introducing financial inclusion schemes.
The financial sector, owing to the sensitivity of financial big data, needs to coordinate with numerous other sectors (stock exchanges, tax authorities, central banks, securities controlling authorities, revenue department, etc.). It has to ensure it fulfills the regulatory requirements of the government authorities around the world and at the same time, it has to constantly think about introducing easier, secure and faster ways to ease the transaction processing for the customers.
Financial institutions need to analyze this financial big data and it requires automation of the highest order. The emergence of Big Data in finance has necessitated the development of software capable enough of handling it in real time.
Just to give an idea on the size of data we are talking about, the Financial and Securities organizations are dealing with financial big data of about 3.8 petabytes per firm (3.8 million GB) and the banking institutions are not too far behind, juggling about 1.9 petabytes of data on an average.
The need for Big Data in Financial Services
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The financial data that sits in the vast databases of banking and financial institutions can be turned into a goldmine of information, provided a suitable tool is used to analyze data. All the departments of a financial institution, i.e. marketing, sales, operations, product, technology, etc. need to do the critical number crunching, in real time to analyze the finance and banking industry trends to come up with predictive analysis.
For example, the marketing team, if provided with the demographics of the sales trend of the past few years, can use that information to devise a suitable plan for positioning its next product and customize it on the basis of geography, age group and regional preferences. Similarly, if the technology team can get an insight into the pace at which the database is expanding, it would be better prepared for scaling up the infrastructure and would avoid last moment surprises which can be disastrous for the finance company's reputation.
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The following section outlines the critical applications of Big data in finance and underlines why financial institutions cannot afford to lag behind in the Big Data game:
Customization and Recommendation
There has been a definite paradigm shift from personal banking to online banking which has provided another simpler, easier to access interface for the customers and business houses to interact with financial institutions. There is a universal agreement that the personal touch has been lost and that is where big data and Hadoop in finance play a vital role.
The interactions of the customers can be tracked in real time and the analysts are able to offer them products based on the customers' interests, risk appetite and capital commitment.
Anti Money Laundering Practices
The emphasis on tracking the source of money is greater than ever before. Finance sector organizations are required to set up a framework which helps detect the sources of black money and keep it out of the system. Also, there is a need to maintain and constantly update a universal list of defaulters to keep down the non performing assets of the banks worldwide.
This warrants an automated process for big data in finance to manage millions of customer names and their respective transactions in real time to flag the suspicious transactions.
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Portfolio Analysis and Asset Valuations
The BFSI sector conceptualizes pitches, sells and maintains a number of investment instruments that require complex calculations depending upon the market value – which changes every second.
There is a need to analyze and update the portfolios of each and every customer and present him the data on demand. In addition, customers’ historical record needs to be analyzed and maintained after consolidating them from a variety of data sources.
Leveraging the Power of Hadoop in Data Finance
For obvious reasons, the financial sector is expected the most to maximize the opportunities and returns while keeping a check on the investments and associated risk.
An insurance company does its homework on the historical data on claims before it arrives at a figure for premium payments. Investment companies know that some companies in their portfolio are bound to lose value yet they offer attractive returns on the overall portfolio. A bank delves deep into the data finance and evaluates the probability of holding a particular sum as deposits before offering a free account.
Hadoop is an open source software which allows financial institutions to analyze financial big data and have deeper insight into their risks and opportunities. A number of banks have turned to Hadoop in finance for data analysis solutions to integrate and analyze data from a variety of internal divisions such as mortgage, loans, consumer and retail banking, individual and corporate lending and treasury banking. Despite the presence of a number of software solutions that enable big data analysis across domains, Hadoop in finance has fast emerged as the preferred choice for financial big data as Hadoop financial analysis has the following advantages:
Segregation of data and computation to save network bandwidth and faster calculations
- Financial big data requires performing thousands and millions of calculations in a matter of seconds. Traditional solutions import data over the server which causes unnecessary bottlenecks over the network and interferes with other processes.
- Hadoop in finance can be leveraged to perform calculations locally and use the network only for transmitting the results which saves precious network space for more important processes.
Tasks performed in isolation
Using Hadoop in finance, tasks can be broken down into smaller fragments and performed independently. This enhances the ability to handle failure, as one failed node can be restarted instead of restarting the entire process tree. This results in considerable savings of time, effort and infrastructure usage.
A financial organization can use Hadoop at any scale it desires to. Using Hadoop in finance gives it the option of easy scalability based on its needs. It has got a linear programming model which requires the analyst to write MapReduce tasks.
Hadoop offers a flat scalability curve, which means that if you are using a Hadoop program on ten nodes and want to scale it up from there, very little, if any, task is required to do so. The growing magnitude of demand is easily compensated by performing little rework on the applications.
How the big Financial Players use Hadoop
A software tool, no matter how good its features might be, is only as good as its real life applications. Hadoop is one such tool that is being used by the world leaders in finance. This section highlights how the world's leading financial institutions are using Hadoop in finance.
Morgan Stanley, one of the world's largest financial services organizations manages over 350 billion in assets and it used the Hadoop framework as a small departmental experiment a couple of years ago. That experiment gathered momentum and now that small Hadoop cluster has expanded into a heavy reliance on the powers of Hadoop for industry critical investment projects. Gary Bhattacharjee, Morgan Stanley's executive director of enterprise information, said at the Fountainhead conference on Hadoop in Finance in New York, “The differentiator that Hadoop brings is that now you can do the same things on a much larger scale and get better results. It allows you to manage petabytes of data which is unheard of in the traditional database world.”
Another finance industry bigwig, Bank of America, being one of the largest banks of the United States of America, has been equally proactive in tapping into the power of Hadoop in finance to manage voluminous On – Line Transaction Processing (OLTP) data. Abhishek Mehta, Managing Director for Big Data and Analytics at Bank of America has been fairly vocal about using Big Data in finance and equates its emergence to a second Industrial Revolution. He says, “Hadoop will be equally disruptive, not just for existing systems, but it will enable you to do things you couldn't do before. It's good to be occupying the front seat with it and being the leader in thinking about it.”
Fault tolerance is one of the major advantages of using Hadoop in finance. At a fundamental level, financial big data is transferred via individual nodes and while it is being transmitted, it is also copied to other nodes in the cluster. It ensures that in an otherwise unlikely scenario of failure, a copy of the data is always ready to be used. In addition, the distributed No NameNode architecture gives insurance against both single and multiple failures.
To sum it up, Hadoop and Big Data in financial services has been acknowledged by worlds leading financial institutions as the way for the future and if you are not already on it, join the bus and leverage the power of the big elephant before it is too late!
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