The window of opportunity is closing for banks to leverage their data to get ahead in digital services, says a new report from the Mobey Forum.
Predictive Analytics in the Financial Industry – The Art of What, How and Why contends that banks and financial institutions that are not yet engaged with predictive analytics need to start now if they are to maintain future competitiveness – writes Joyrene Thomas.
“Banks have great data but if they want to compete in the digital age they need to get more strategic and more professional about how they use it,” comments Amir Tabokovic, BigML and co-chair of the predictive analytics working group, Mobey Forum. “The PSD2 regulations will soon force banks to provide access to account data through third party APIs. This means that if the banks don’t leverage their data soon, someone else will.”
THE PERFECT STORM
We are in the midst of a perfect storm for predictive analytics. The ‘dataverse’ is growing exponentially. From smartphone data and social media sites to online banking, the Internet of Things and wearables, we create around 2.5 quintillion bytes of data every day, according to IBM. Additionally the price of data storage and computing power is falling and becoming more available.
Not for the rst time, technology giants such as Google, Apple, Facebook and Amazon (GAFA) are setting the standard in terms of customer expectations and what can be done with data. Smartphones as the perfect communication channel to customers are already deployed in large numbers. So much so GAFA are moving from mobile- rst to AI- rst companies. They are evolving from companies that retrieve and display information to those which inform and assist.
Smart use of data is no longer a nice-to- have but a must-have, especially in the wake of upcoming changes in regulation. In Europe, nancial services such as payments may be further commoditised as a result of new regulation. As a result, the value-add around payments will become ever-more important. Under the PSD2, any entity with a payment licence can compete for customer payment data. New areas of competition will emerge once customers can decide with whom they wish to share their transactional data. Banks will compete with each other and with non-banks for the right to use their customers’ data, so they need to leverage their data to remain relevant to customers.
PREDICTIVE ANALYTICS EXPLAINED
Traditional business intelligence used to understand the past typically includes descriptive and diagnostic analytics. This answers the questions ‘what happened?’ and ‘why did it happen?’. According to research rm Gartner, there are two further types of analytics: predictive and prescriptive. These answer the questions ‘what will happen?’ and ‘how can we make it happen?’.
Through predictive analytics, companies can predict changes in customer needs, target customers with well-timed and appropriate o ers, and build loyalty by o ering contextual information and advice that improves the customer experience. For example, Net ix and Amazon use predictive analytics to show recommendations of movies and books to customers, based on search history and the past purchases of customers and others like them. The 2012 Obama presidential campaign used predictive analytics to in uence individual voters with particular types of messaging and contact.
The report suggests several areas of application for predictive analytics. These include card-linked o ers with merchants and ‘next best action marketing’. This o ers advice and support to customers based on their nancial and lifestyle habits, rather than products. From a risk point-of-view, more accurate predictions about future behaviour would give organisations the chance to assess risk better. Use cases for predictive analytics include risk assessments, advanced early warning systems, credit collection analysis, fraud detection analysis and pricing. On the operational side, predictive analytics can help with predictive maintenance of systems
to prevent failure and minimise downtime.
THINK BIG, START SMALL, START NOW
Naturally there are hurdles organisations have to overcome before predictive analytics can deliver bene ts. Not least of which is a cultural change within companies towards e ective use of data. This cannot be considered an IT department-only project or responsibility. The report sets out ten further challenges in the predictive analytics lifecycle, ranging from data management to analysis and modelling, and deployment.
At the same time, it contends that banks and nancial institutions should think big, start small and start now with regard to predictive analytics. They should work on building a digital services infrastructure, with data analysis at the centre, which can be leveraged to support the whole business over the long term.
“The threat is real, but there is also huge opportunity for banks in this eld,” said Sirpa Nordlund, executive director, Mobey Forum. “From now on, it’s all about the data. With predictive analytics, banks can establish greater relevance and appeal, increase trust and ultimately create a more stable commercial and operational footing in the digital age.”