The Power of Prescriptive Analytics

The power and role of analytics has grown exponentially in financial services firms, starting with analytics 1.0, which incorporates basic business intelligence and key performance indicators to assess past performance. More recently, the quest to make the most of Big Data dominated the era of analytics 2.0, which continues to explore the power of predictive analytics. Yet, even as the industry continues its quest to master Big Data, industry visionaries are now beginning to focus on Analytics 3.0, which will be characterized by the operationalization of analytics across the business.

Thomas H. Davenport, research director of the International Institute for Analytics and a pioneer of Analytics 3.0, identifies three types of analytics: descriptive, which report on the past; predictive, which use models based on past data to predict the future; and prescriptive, which use models to specify optimal behaviors and actions. Analytics 3.0 includes all types but with an increased emphasis on prescriptive analytics.

In the 3.0 era, analytics will be embedded as a part of real-time decision making. In essence, it will become the bank’s intelligence core and enable institutions to place the customer at the center of the enterprise like never before.

Intelligent Partnership

The rise of analytics 3.0 enables a more intelligent partnership between financial institutions and their customers that optimizes value to both parties. For example, banks will expand their ability to know in real time what their customers want and need and will be able to effectively create and offer up products during the customer’s online, mobile, in-person, chat or ATM interactions  that are uniquely positioned or tailored to them.

Financial institutions will be ideally suited to deliver this personalization due to the volume and diversity of customer and transactional data that they capture across their enterprises. External sources, such as social media channels, hold the opportunity to capture additional rich data, such as information on life events that might drive financial purchases, including relocation, marriages, childbirth or acquiring a new job. This type of insight enables precision marketing that strengthens relationships and customer intimacy while empowering financial institutions to optimize the use and impact of their marketing dollars.

With analytics 3.0, banks can also offer a new level of value-add to customer relationships. For example, the ability to analyze transactions in real time and map behavior against past trends can give financial institutions the ability to effectively coach customers toward behaviors that can optimize their investments, credit standing and relationship with the bank. Specifically, a bank could alert a customer that current checking account transactions are trending above average and remind the customer of related expenses. This provides an opportunity to help the customer proactively manage finances, possibly avoid an overdraft charge and present offers for overdraft protection services.

The possibilities are endless when it comes to creating personal banking services that are attainable for the mainstream consumer. These types of services can create a point of differentiation for early adopters and strengthen the increasingly fragile bond between customer and bank.

As financial institutions continue to build out their analytics capabilities (regardless of where they are in the adoption curve), there are several questions they need to consider to help prepare for their progression to analytics 3.0, including:

Can our infrastructure deliver the extreme performance that we need? Analytics 3.0 is all about speed and real-time insight. Organizations are amassing data at an unprecedented pace and must deal with more types of structured and unstructured information than ever before, including images, video and social media content. To reap the full benefits of this valuable data and deliver real-time insight and intelligence to associates in the front- and back-offices, as well as to automate systems, firms need high performance infrastructures that can scale easily with their needs while delivering cost efficiency from day one.

Can we easily handle all types of data? To gain a 360-degree view of the customer, which is critical to calculating and optimizing profitability, firms require a unified or industry-specific data model that can accommodate information – structured and unstructured – from all critical sources, including social media channels and customer relationship management, risk, and enterprise performance management systems.

The creation of a unified data model supporting all critical applications enables financial services organizations to improve data quality and quantity, thereby boosting user confidence in the results. It also ensures that all parties across the enterprise are “speaking the same language” when assessing profitability and other customer dimensions. Creating the data model is one of the most critical parts of any successful enterprise analytics initiative as it forms the foundation for all insight. It is also historically one of the most expensive and time-consuming components.

Banks can save considerable time and costs with a commercially available data model, which is purpose-built for the industry and incorporates the company’s vast experience in the financial services sector.

Do our analytical applications put the power of real-time insight into the hands of line-of-business managers? Business intelligence ages rapidly in today’s dynamic financial services sector and immediate, real-time insight is the absolute foundation for analytics 3.0. As such, business users need up-to-the-minute information at their fingertips. In addition, the ability to quickly create robust personalized dashboards, which incorporate drill-down capabilities, further extends executive insight and should be an important part of any analytics initiative.

Do we have deep, native, and/or flexible integrations between our enterprise resource planning; enterprise risk management; governance, risk, compliance; customer insight; and enterprise performance management environments? Complete and reliable integration between these transactional and analytical applications enables firms to rapidly connect business intelligence with business processes and deliver the agility needed to bring new levels of intimacy to client relationships. Firms should seek solutions with pre-built integrations as well as a service-oriented architecture to accelerate time to value and simplify ongoing management.

Davenport notes that “some firms are embedding analytics into fully automated systems based on scoring algorithms or analytics-based rules. Others are building analytics into consumer-oriented products and features. In any case, embedding the analytics into systems and processes not only means greater speed, but also makes it more difficult for decision-makers to avoid using analytics – usually a good thing.”

As financial institutions build out their analytics capabilities today, they must also cast an eye toward future requirements. Being able to capture, manage, and analyze the enormous amount of data flowing to organizations from a myriad of channels cost effectively depends directly on an institution’s technology infrastructure and capabilities. Ensuring these critical components are in place will empower banks to strengthen relationships and optimize profitability while providing customers with a tailor-made experience – a true win-win scenario for both parties.

Mr. Khanna is vice president, Oracle Financial Services Analytical Applications, with Redwood City, Calif.-based Oracle Corp. He can be reached at [email protected]