Leveraging the Power of Predictive Analytics in the Financial Services Industry

Adam Mayer, Senior Manager at Qlik, and Richard Speigal, Senior Business Intelligence Manager at Nationwide Building Society, discuss how to unleash the potential of predictive analytics.

The COVID-19 pandemic has propelled many industries into accelerated data and digital transformation, transforming workflows and thinking. In turn, pressure increased on analytics teams to answer questions faster than ever before, helping the business understand what the impact and consequences would be of making certain changes in the rapidly evolving climate.  

Data has always been at the heart of financial services. Yet the industry has proved sluggish in the adoption of more advanced data technologies. This conservative approach to digital innovation may not be too surprising for such a heavily regulated sector, but there is no question that those organisations at the leading-edge of data transformation are driving better business and customer outcomes.  

Predictive analytics is one of the technologies with huge potential in the sector, but which has seen slow adoption. Persistent issues around trust and regulation, as well as weaknesses in organisations’ data pipelines have held them back from unleashing its potential to drive more value from the data at their fingertips. So, can data and IT leaders do to overcome these challenges?  

Keeping Trust Alive

It is no secret that trust is at the heart of the service industry – particularly financial services. Everything from opening a bank account to applying for a mortgage is buttressed by the notion of trust. This can be challenging when introducing more advanced analytical solutions that move humans one step further from the decision-making process, such as predictive analytics and machine learning. So, how the algorithms behind predictive analytics are built, as well as the quality and lineage of the data that informs them, will underpin this question of trust.  

In fact, recent Qlik research found that only half of IT leaders trust decisions made by predictive analytics systems are always accurate (45%) and without bias (50%). This is a major challenge for employees who need to prove to their customers that they are able to make fair and consistent decisions relating to their finances every single time. That’s why ensuring that predictive analytics algorithms are informed by up-to-date, complete and governed data will be critical to building trust in its insights. 

Getting to Grips with Regulations

The financial services industry is a notoriously regulated arena. Indeed, more than $270 billion (£198.5 billion) per year is spent on compliance and regulation alone. However, an overly cautious approach to regulation may stunt digital innovation, leaving it lagging behind other industries that have been proactive in adopting more advanced tools. When surveyed, 46% of IT leaders reported that the regulatory burden of predictive analytics outweighed the benefit the solution could offer. 

But while many IT leaders fear it might add to the regulatory headache, predictive analytics can help overcome the dizzying world of data regulations by proactively managing data compliance. Simpler approaches include managing data retention policies, while more sophisticated use cases could involve operationalising predictive analytics to calculate risk, so organisations can make proactive steps to resolve a situation before it becomes a problem. 

Making Your Data Pipeline Flow

A key success factor when implementing predictive analytics is the data pipeline that underpins it. Analytic data pipelines are not only the conduit for raw data, but they also transform data into analytical-ready information, making it continuously available to the rest of the business at scale, while maintaining security and governance. As such, any flaws in an organisation’s data pipeline will mean for a shaky foundation when implementing predictive analytics.  

Research shows that when asked about the challenges that would prevent them from introducing predictive modelling or analytics into their organisation, two fifths of IT leaders cited concerns over data quality (40%), data silos (40%) and the speed of data integration (36%). IT leaders must work to holistically look at their data pipeline to identify potential cracks where value is lost. If the data pipeline isn’t robust, it’s impossible to trust that actions being taken on the data are correct. 

A More Intelligent Approach to Analytics

Improving the output and outcome of analytics, starts with building high performant analytic data pipelines that deliver real-time data. But another critical element for many financial services organisations remains the human oversight into these decisions. Indeed, this cannot be expressed better than the words of Richard Speigal, Senior Business Intelligence (BI) Manager at Nationwide Building Society, from Qlik’s recent report“We are very clear about what our customers, our members, mean to us, and that extends to how transparent we are in decision-making processes. We would never want to make a customer feel like decisions were being made about them that couldn’t be explained. A human has to be able to explain those decisions.” 

That’s why more than two thirds (69%) of IT leaders in financial services advocate incorporating predictive analytics into BI platforms. The improved explainability and accountability of the decisions being made when humans retain overall control of the decision and, in turn, the overcome helps to remove some of the hurdles around trust and regulation. 

Predictive analytics is the fuel that will empower financial services organisations to make better decisions and pursue bigger and better results for their customers. Addressing issues around trust and regulation, by integrating its predictive insights into BI platforms, could finally unleash the power of predictive analytics for financial services organisations.  

By Adam Mayer, Senior Manager at Qlik, and Richard Speigal, Senior Business Intelligence Manager at Nationwide Building Society. 

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