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Predictive Analytics in the Financial industry – The art of what, how and why

16th of June 2016
Report

This first white paper of the new series discusses the value of predictive analytics for the financial industry and answers the
question why this is the right time to start with predictive analytics and how to empower entire organisations to use it.
As mobile technology evolves and everything around us – not just our mobile devices– is becoming connected we are
entering a new era of connected experiences. The customer journey in the financial industry is completely digitized. This
exponentially increases the number of interactions between a financial service company and its customers. Customers
expect banks to understand their context and the challenge for financial industry is to be relevant at all these interactions.
Given the gold mine of data that banks have access to, the field of predictive analytics offers a range of untapped
opportunities in doing so. When implemented successfully, predictive analytics will lead to vast improvements of existing
static business rules and achieve progress like reducing cost, increasing revenues and improving customer experience.
Mobey Forum expects that predictive analytics skills will soon be essential for banks to keep their position in the market
against non-banks but also other banks that will be using predictive analytics as a competitive weapon. That’s why building
skills that go beyond the conventional descriptive analytics and focusing on the question “what will happen?” has to be top
priority for every financial institution.

To understand the concept of predictive analytics the paper discuss the difference between descriptive and predictive
analytics. The majority of business analytics at financial institutions is currently still focussed on the ‘rear view mirror’,
resulting in descriptive analytics. The questions more concerned with the future mainly remained unanswered from an
analytics point of view. These decisions are often made intuitively and are seldom fact based, often resulting in sub-optimal
decisions. With predictive analytics we do identify and address questions concerned with the future. We learn from our
experience and predict future behaviour in order to drive better decisions.

Used effectively, there are several areas of application for predictive analytics where financial institutions could make
profitable investments while at the same time improving the attractiveness of their services. Mobey Forum is mentioning
examples like card linked offers, next best action, pricing, claim handling, risk assessment to mention few.

But as always, where are many opportunities, there are also many challenges to be solved. Effective usage of predictive
analytics is only possible in a data-driven organization. It all comes down to having access to right “past data” and using
right skills, techniques and tools to find business relevant patterns that can be used to solve similar problems in the future.
The challenges in the different phases of the predictive analytics lifecycle are discussed in the paper.

Considering the challenges and opportunities ahead, the final advice is to start now. But start small, create successful
examples and iterate towards leveraging predictive analytics in to your organization.

This paper will help you to start. It explains the most important components, challenges and key application areas of
predictive analytics in the financial industry.

Download the Report

 

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