The financial services sector that has traditionally been conservative regarding the usage and adoption of advanced technology is rushing full speed to incorporate Big Data solutions.

71 percent of the industry is already using Big Data and analytics, up from 36 percent just two years ago, according to a survey by the University of Oxford. As information-based competition explodes in the digital economy, banking executives increasingly are asking what kinds of investments they should make in Big Data, a complex marriage of vast customer information, technology and analytical methods.

Banking CIO Outlook recently profiled the top 10 banking analytics solution providers for 2016. These Big Data pioneers include SQream Technologies, Tableau, MapR, Actian, EXL, Logi Analytics, Nice Actimize, Securnix and Trepp.

Banking on Data: With the right strategy in place, Big Data technologies can open up a world of predictive possibilities.

 

The embrace of Big Data makes good business sense. For one thing, large financial services firms arguably have the longest analytics experience of any vertical. Being a data-intensive industry in an era when banking is becoming increasingly commoditized provides a massive opportunity to stand out from the competition.

With every banking transaction a nugget of data, the financial services industry is sitting on vast stores of information. By using Big Data to collect and analyze information, banks can improve, or even reinvent, nearly every aspect of banking.

Mission: Customized Service

Specifically, the financial services sector is reacting to changing customer expectations. Personalized service from banks is the order of the day. A large proportion of the current Big Data projects in the financial services sector revolve around customers – driving sales, boosting retention, improving service and identifying needs. More than 70% of banking executives worldwide say customer centricity is important to them. In another study, some 55 percent of financial industry respondents surveyed pinpointed customer-centric projects as their top priority.

A Year to Remember: 2015 was groundbreaking for banking and financial markets firms, as they continue to learn how Big Data can help transform their processes and organizations. 

However, achieving greater customer centricity requires a deeper understanding of customer needs. Banks are only using a small portion of this data to generate insights that enhance the customer experience. Research indicates that less than half of the banks analyze customers’ external data, such as social media activities and online behavior.

Data is perceived by the financial industry as a goose that has many golden eggs to lay.

 

Who’s Using Big Data…And How

Mid-tier and small-tier firms were able to more rapidly adopt new data platforms that are helping them leapfrog the architectural complexities that their larger brethren must work against. This segment of the market therefore is expected to move more rapidly on growth, profitability and strategic projects that are aimed at more immediate revenue contribution, versus the more long-term, compliance and cost-dominated priorities that larger financial institutions are focused on.

All Eyes on Consumer Behavior: Data about individual’s financial-related activities is viewed as a particularly lucrative source of  actionable information.

Regardless of an organization’s size, the ground is shaking for all financial institutions that must contend with a deluge of multisource data. Today, banks and other financial firms are required to analyze longer ranges of data than they used to, frequently going back years.

SQream Technologies is one company that is successfully addressing these challenges by providing an analytics solution that is the most cost-effective on the market.

Financial entities dealing with rapidly scaling data can effectively ingest and store massive amounts of historical information, process it and analyze it in real-time with SQream DB.

With SQream the power of a full-rack database machine is condensed into a single standard 2u server. Requiring much less hardware than other Big Data solutions translates into a much more cost-effective approach for the finance industry , without compromising on performance.

SQream can be used as an analytical data warehouse or as an accelerator for an existing data warehouse to accelerate reports and analytics, without the need for OLAP. Organizations can connect any visualizer (JDBC, ODBC, .NET) to SQream, allowing them to implement all kinds of applications such as risk management, behavior analysis, cyber security, fraud detection, predictive analytics and more.

The result is improved productivity, reduced costs and creation of a long-term competitive advantage.

Conclusion

Big Data is enabling financial institutions to harness the massive amounts of data at their disposal to obtain real time, actionable insights into customers’ preferences and behavior patterns. The key questions a financial firm must ask is how it can both derive genuine insight from its data and change the way it interacts with customers, competitors and the market through fact-driven decision-making.

Financial institutions that develop clear and comprehensive answers will set the trend in customer service, improve profitability and respond more rapidly to the evolving competitive demands of the industry.