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The Top 5 Trends for Big Data in Financial Services

September 23, 2017

The Top 5 Trends for Big Data in Financial Services

Big data is about answering big questions, and some of the biggest questions are being asked by financial services. Using big data in financial services makes it possible for financial institutions to do a better job of predicting financial trends and protecting investors from disaster. The possible insights offered by assimilating and analyzing oceans of data make it possible to identify financial trends sooner than ever before.

Gartner reports that 64 percent of financial service firms launched big data initiatives in 2013, and many more are anticipated by the end of 2014. The New York Stock Exchange generates over 1 TB of market and reference data daily. Consumers complete on average 10,000 credit card transactions every second. Mobile payments reached more than $235 billion in 2013. To track and analyze all that data was more than any financial institution were able to tackle before there was big data in financial services.

Banks are swimming in information but before now they had no means of assimilating and making sense of it all. Complex data processing systems support bank and trading activities and each transaction generates more data for analysis. Big data in financial services means that loan and credit data, market trends, actuarial models, and a host of additional data points can be analyzed to identify trends and improve decision-making. When applying big data in financial services, knowledge means money.

Here are five of the most prominent applications for big data in financial services:

1. Fraud Detection and Security

One of the best ways to fight cybercrime is with early detection. Banks are prime targets for cybercriminals and fraudsters, and any kind of public breach creates a lot of embarrassment, bad publicity, and unwanted scrutiny. In fact, Mandiant’s 2013 Threat report indicates that 63 percent of all data breaches are reported by third parties, and the median number of days before detection is 243. Clearly banks have a vested interest in any technology to identify and prevent a data breach or fraud.

Using big data in financial services promotes better detection and security. Big data makes it possible to capture real-time activity and immediately detect anomalies, detecting fraudulent behavior before any harm can be done. Analytics also provides a deeper understanding of potential vulnerabilities so financial institutions can tighten their security against future attacks.

2. Governance, Risk, and Compliance

Increased regulation is placing more pressure on financial institutions and is prompting use of new technologies such as big data to mitigate risk and improve governance. For example, to demonstrate good governance, risk, and compliance (GRC) you need a real-time picture into your entire financial operation. Using big data in financial services can bring together all the GRC data for analysis. This minimizes risk and promotes compliance in the event of an audit; all the performance data is on one place for easy access.

3. Better Customer Insight and Service

Financial services is an incredibly competitive marketplace so any information that will provide a competitive advantage is welcome. Consumers surrender a lot of valuable personal information to banks about their borrowing habits, spending habits, household income, transactions, etc. Using big data in financial services makes it possible to analyze that data to develop more appealing and personalized financial products.

Big data also can provide insight into individual financial needs. For example, an increase in spending on home improvement projects could trigger a personalized message offering a home improvement loan. Or opening a new joint account for a son or daughter might signal that it’s time to offer help with college education. Big data in financial services makes it possible to analyze structured and unstructured data and come up with insights about individual preferences and spending patterns to promote products that engender customer loyalty.

4. Optimizing Pricing

When the economy is tight being able to squeeze an extra basis point from a mortgage could mean millions for a bank’s bottom line. Being able to develop better risk profiles for lending and insurance premiums also can protect profits. Big data analytics make it possible to assimilate all kinds of competitive market rates and other relevant risk data to develop smarter pricing and lending strategies that lead to more revenues.

5. Better Operational Efficiency

As with every kind of business, using big data in financial services can improve operating efficiency. Big data can be used to conduct various operational analyses, from determining optimal staffing for a customer call center to determining which branch banks offer the most profit potential. Big data analytics makes it possible to break down data siloes and provide a comprehensive, holistic picture of operations.

These are just five of the trends for big data in financial services. Perhaps more than any other industry, the financial market has more data available and more potential to get more value from big data analytics.