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Are Banks Ready for Next-Generation Analytics?

by internationalbanker

Ambreesh-KhannaBy Ambreesh Khanna, Vice President, Products and Solutions, Oracle Financial Services



Consumers today undoubtedly have high expectations. They now demand access to the products, services and information they desire and need anytime and anywhere.

This heightening of expectations has also carried over to consumers’ relationships with their banks. Financial institutions are now being forced to keep pace with rapidly changing customer demands if they wish to secure market share in the face of their competitors.

Analytics is the technology on the front lines of this quest. As customer expectations continue to evolve so, too, do the analytics solutions available to banks. In this environment, are banks actually prepared to adopt and benefit from these next-generation analytics solutions and maximise their business opportunities?

To answer this question, it is essential to first look at how analytics have evolved, and how we ended up where we are today. Thomas H. Davenport, research director of the International Institute for Analytics and a pioneer of Analytics 3.0, views the evolution of analytics as a three-stage process:

  • Analytics version 1: This generation of analytics, born in the mid-1950s, incorporates basic business intelligence and key performance indicators (KPIs) to assess past performance. Analytics 1.0 allowed managers to examine data from production processes and customer interactions to improve the performance of their companies.
  • Analytics version 2: Analytics 2.0 was emphasised by the big-data boom of the mid-2000s, allowing businesses to use the power of predictive analytics and pull data from multiple sources outside of the organisation. 
  • Analytics version 3: This latest generation of analytics merges the descriptive and predictive qualities of previous technologies and adds an increased focus on prescriptive analytics, otherwise known as the use of models to specify optimal behaviours and actions based on data.

Analytics 3.0 marks the point at which analytics are embedded as part of real-time decision-making and serves as a bank’s core of intelligence, allowing it to develop a more complete customer view than ever before.

Putting the customer first 

With Analytics 3.0, financial institutions will be equipped to truly put their customers first (otherwise known as a “Customer-In” approach), in contrast to a more traditional “Product-Out” approach by which they focus principally on their products and services. This shift promises to generate value for both banks and their customers by providing the former with real-time insights into peoples’ present and future needs, and by helping to ensure that the latter will have these met.

What this means is that financial institutions can create personal, tailored services for customers based on the data they collect. For example, by capturing rich data from peoples’ social-media channels, such as information indicating that an individual is planning to purchase a new car, a bank can customise its services for that customer to support that purchase. By allowing banks to assess customer transactions in real time and map their behaviour against past trends, the latest wave of analytics technologies enables these institutions to coach customers toward behaviours that can effectively optimise their investments and credit standings, and ultimately improve their customer relationships.

That said, before financial institutions can fully reap the benefits of Analytics 3.0, they need to make sure they have the technologies and practices in place to do so. They can gauge this by asking themselves the following four questions:

  1. Will our infrastructure withstand the performance power needed? High performance, scalable infrastructure is essential to delivering the insights and intelligence a bank requires in a timely fashion. 
  2. What types of data can be handled, and is it the right data? With the 360-degree customer view for which banks are now pushing, they must make sure they can deal with multiple types of data. This will involve adopting a model that can accommodate both structured and unstructured data from any and all sources. 
  3. Can analytical applications put real-time insight into the hands of employees? Real-time insight is the “holy grail” of analytics, and banks must ensure they are putting meaningful data at the fingertips of their employees so they can best serve their customers.
  4. How well is the institution integrated with enterprise resource planning; enterprise risk management; governance, risk, compliance; customer insight; and enterprise performance-management environments? When isolated in siloes, data doesn’t deliver the meaningful, actionable insights that banks need. It is vital for banks to ensure they have a solid integration between their analytical apps and existing enterprise apps so that valuable information is available to the right people across the organisation. 

Financial institutions today are in the process of developing their analytics capabilities and constantly working to do more with their data. The ability to gather, manage and analyse vast amounts of data means ensuring that all the critical components are in place for them to improve the way they engage with existing customers and attract new ones.


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