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Big Data in Financial Services: What You May Not Know

June 22, 2017

Big Data in Financial Services: What You May Not Know

Big data has evolved well beyond the "buzzword” phase, and solution providers have been working with customers to deliver actionable intelligence, especially in financial services and banking. It seems to have been made for financial services. Big data can help address issues such as improving customer satisfaction, reducing risk and addressing regulatory compliance. And banks and financial services companies are developing increasingly sophisticated big data use cases in an effort to stay ahead of the market and their competition.

According to industry analysts, the top four U.S. banks spend between $7 billion and $10 billion each year on technology, much of that on big data infrastructure and analytics. According to Aberdeen Group, those financial institutions that apply big data analytics have better sales with higher lead generation, more lead conversion and greater customer retention. To date, banks and other financial institutions have primarily applied big data analytics in four areas:

  1.          Customer intelligence
  2.          Customer satisfaction and retention
  3.          Verification and security
  4.          Regulatory reporting

Financial services companies are looking for new strategies that embrace analytics 3.0. Beyond simple analytics (1.0) and big data analytics (2.0), analytics 3.0 is changing banking. Banks are gathering more information from website interactions, call centers and in-bank tellers to learn more about customers, and using analytics integrates that data to identify strategic inflection points in the customer journey. For example, banks are leveraging mobile banking data to track customer behavior to offer value-added services on the fly. Bank of America has developed BankAmeriDeals, which offer immediate cash back via customers’ mobile phones based on previous transactions.

However, big data analytics can do so much more for financial services companies. Here are seven of the big data trends and technologies having an impact in financial services:

1. Machine learning

Big data analytics is ideally suited for security and fraud detection. Advanced analytics will use more machine data to improve models to detect fraud. Analytics will accelerate toward more real-time analysis and alerts and will even enable a preprogrammed response to prevent fraud without human intervention.

2. Leveraging the Internet of Things (IoT)

Other industries such as retail, manufacturing and telecommunications are already applying IoT data as part of big data analysis. The pioneers have started to demonstrate both the value and the pitfalls of IoT, and banks are learning by example. Applying IoT metrics to real-time, multichannel marketing will make it easier to reach customers at key decision points. IoT will enable banks to embed new services in equipment such as ATMs and even mobile devices.

3. Portfolio management

Software vendors are building big data analytics into financial advisory applications, providing better market intelligence without actually calling it out as big data. Applications built on big data platforms will have to prove themselves, and when they do, the financial advisors using those applications will be able to improve portfolio performance.

4. Integrated compliance

 Data governance and compliance are being deeply integrated into big data platforms. More Hadoop solutions that are able to access legacy data stores for regulatory compliance are evolving. New data lakes that serve as central data repositories for compliance are also emerging.

5. Proactive compliance

Technology is becoming increasingly important in addressing compliance, and financial services companies are finding new ways to harness technology for proactive compliance. Big data is facilitating decisions about underwriting loans, authorizing foreclosures and other matters, ensuring compliance in advance, not after the fact.

6. The “smart” data lake

Data lakes have emerged as vast repositories of raw data, but cataloging and adding meaning to that data is an ongoing challenge. Smart data lake tools are taking advantage of semantic technologies to make sense of massive data stores. The biggest challenge is performance at scale, and vendors are working to develop massively parallel, in-memory graph databases that support semantics. Smart data lakes also give end users access to data cataloging and self-service analytics.

7. More big data “killer apps”

More banks are expected to start running proofs of concept with front-end interfaces for big data platforms. This is the next step in the creation of complete solutions that optimize the front end and the back end in tandem. This will open new opportunities for system integrators as well.

While banks can conceptualize these new applications for analytics, they still need help with execution. Experienced integrators and solution providers that can show financial institutions how to harness Analytics 3.0 will help financial services customers get a jump on their competition and make themselves invaluable in the process. The financial services community is seeing more value in big data every day, but they need solution providers’ big data expertise to help them profit from that value.