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Big Data Analytics Investments in Financial Services

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January 11, 2017
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How Big Data Analytics Is Changing Financial Services as We Know It

The Influence of Analytics in Financial Services, Prompted by Digitalization

The quickest path to meeting ambitious growth goals for the banking industry is digitalization, and financial IT leaders are well aware. For healthy growth, the industry must continue developing digital systems for every aspect of business operations, from customer portals and apps to digital marketing and data analytics initiatives.

Mass digitalization has greatly accelerated the use of big data analytics in financial services. Nearly unfathomable amounts of data are being generated around-the-clock, originated from online portals, mobile apps, and IoT devices. Forward-thinking leaders know that this data can be mined and translated into meaningful information that will steer decision making and provide real-time insights into consumer behavioral patterns. As digital adoption grows, there will be an even higher need for analytics and business intelligence (BI) practices. In the next five years, this will probably become a major pressure of the financial CIO.

According to ComputerWeekly, the opportunity that analytics brings is the ability to identify the potential of data and develop business strategy based on factual insight. Most financial organizations recognize that data is now a corporate asset, and are responding by hiring chief data officers and specialized analytics roles, and making sizable investments in analytics initiatives overall.

Data analytics alone does provide a significant amount of useful insights, but it has also resulted in the inception of a number of advanced technologies that use the data to carry out tangible activity, such as automation, artificial intelligence (AI), and robotics.

Data on the Front-end and Back-end

Digitalization initiatives are happening on the front and back-end of financial organizations, and considerable budget allocations are being made to implement synchronous data analytics activities. By and large, businesses have been focusing on digitalizing the back-end first to ensure systems are collecting and storing the right data, from accounting systems to CRMs. Front-end applications, such as customer-facing portals and mobile apps, are also priority.

Financial organizations are reaching a critical point during which tying all systems together to collect and analyze the data they generate is a business-shaping initiative; executives have high expectations of the CIO to operationalize and materialize information. The ultimate goal is to deliver personalized, real-time messages to the right people at the right time and place, while simultaneously identifying behavioral patterns to make business decisions that will ultimately increase ROI. The analytics view of the bank and insurance company is 360-degrees, and analytics resources that can make data tangibly impactful are a critical investment.

Data Analytics

Data analytics is the catalyst for the widespread use of automation, robotization, and (AI); all enabled by data, but with different functions.

As explained by PWC, big data can be defined as demographic and psychographic information about consumers in structured, semi-structured, and unstructured formats, derived from a wide array of sources, including product reviews, blogs, social media content, mobile device data, and sensors. Data analytics is the act of analyzing that information in order to make meaningful inferences capable of making tangible impact.

The use of big data allows organizations in the financial sector to collect the information needed to identify the right markets and customers at the right time, empowering banks and insurance companies to make the most advantageous strategic decisions.

For example, many auto insurance companies are now offering telematics devices that customers can plug into their vehicles. The devices record driving behavior and customers who drive safely are rewarded with better auto insurance rates, all provided based on the mass amounts of data being collected.

Automation

Automation is the ability to enable a machine to automatically perform a single set of operations with the purpose of increasing efficiency and eliminating manual intervention. In the case of automation, only one set of operations can be followed, and they can’t be changed post-programming.

An example of automation in banking and insurance would be the 24-hour customer service lines available throughout the majority of banks and insurance companies. When customers call customer service, an automated system receives the call and provides a uniform set of options for the caller to choose from, ultimately guiding them to the correct area of assistance, and often eliminating the need to talk to an actual representative.

Other examples:

  • Leveraging robotics for fraud detection
  • Automating mundane tasks like data validation and entry

Artificial Intelligence (AI) and Robotization

AI and robotization are very closely related and widely used throughout financial services, but they aren’t mutually exclusive. Robotics, or robots themselves, are autonomous or semi-autonomous machines that carry out activity independently of external commands—they can carry out activity on their own.

AI is software with the ability to learn and improve based on information generated from repeatedly carrying out a given task; it’s often used within robots in order to give them the ability to achieve more tasks through self-learning.

Fraud detection in banking is a common case of the concurrent use of AI and robotics. Continuously, a bank will automatically record the details of a consumer’s purchasing behaviors, including geographic location, frequently used merchants, and average time of purchases, in order to generate accurate predictions about how a customer is likely to spend their money. If a banking customer makes a purchase in a disparate location at an unpredictable hour, the bank will automatically generate a fraud warning to the customer, preventing the purchase from being processed without confirmation of authenticity—this is the basis of fraud detection, and it’s an autonomous robotic activity that can change as consumer behavioral patterns change.

The Ultimate Impact on Banks and Insurance Companies, and Their Customers

Overall, data analytics and the advanced technologies which have followed it are presenting unprecedented opportunities for the financial sector to reduce costs, improve consistency, free employees to focus on high-value activities, and provide an optimized experience to consumers by supporting the initiatives of digitalization.

Making the best possible use of analytics capabilities may require leadership to hire the best possible internal resources, while leaving the related ancillary IT activities to outside resources. Most banking and insurance companies do not want to be in the business of IT, and this is where working with a technology partner can enhance even the most sophisticated data analytics strategies.

The backend infrastructure of IT is a key component of properly delivering systems and analysis, as well as enabling data storage—in a HIPAA and PCI-compliant fashion, moreover. The sensitivity of data being collected by banks and insurance companies requires that information be handled 100% securely, while also remaining available. Carrying out all of these technology requirements can be a greater burden than internal resources can feasibly manage, which is why working with a reliable partner specialized in the financial services industry, whether for cloud or colocation, is a helpful solution for making data analytics investments successful and worthwhile.

“We are an insurance company, we are not an IT company, and we want to do things as efficiently as possible and provide maximum value to our customers. We also have compliance issues to deal with. I would say we are cloud enabled, cloud curious. Cloud positive.”

– Director of IT at regional insurance provider

If your organization is in the midst of optimizing your data analytics strategy and would like input on how shifting your IT activities can help, Peak 10 is here to assist. Contact us today at www.peak10.com/contact-us or (866) 473-2510 to speak with one of our experts.

The Peak 10 Financial Services and IT Study: Tackling the Digital Transformation resulted in considerable candid feedback from IT decision makers within banks and insurance companies on how they’re managing the Age of Digitalization, as well as current challenges and solutions of delivering technology, managing IT budgets, and dealing with security and compliance.

Learn more about what your financial industry peers are doing to make IT an ROI generator. See the Peak 10 Industry Spotlight: Financial Services and IT .

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