New research from data leader Qlik, are slowing the adoption of predictive analytics in Financial Services (FS), preventing UK organizations from reaping the full value from their data and maximizing the potential with business intelligence platforms.

The report, “Unleashing the Potential of Predictive Analytics in Financial Services”, which surveyed more than 500 UK IT leaders in FS, exposes the slow uptake of predictive analytics. More than a third (38%) of British financial institutions have five or fewer predictive analytics use cases currently in operation – in stark contrast with the leading 7% of institutions, which have each introduced 50 or more.

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Key issues IT leaders in FS face when implementing predictive analytics include:

• A question of trust – Every decision a financial services organization makes can have a major impact on a customer’s life, from agreeing to an overdraft to making payday or approving a mortgage application. Yet one-third (30%) of IT leaders admit fearing algorithms could unfairly impact their customers. This is perhaps unsurprising given only half of respondents (50%) trust decisions made by predictive analytics solutions are without bias and are always accurate (45%). Over two-fifths (43%) of respondents go far enough to say they wouldn’t even trust predictive analytics to manage their kids’ pocket money.
• Regulatory risk – In such a highly regulated industry, 44% of IT leaders fear they could be held personally responsible for decisions automatically triggered by predictive analytics solutions – rising to 81% amongst those working in funds and investments. The regulatory burden also weighs heavily on them, with 46% reporting it outweighs the benefit the solution could offer.
• Flaws in the data pipeline – IT leaders also cite a number of technical barriers to implementation. Two-fifths face issues with data quality (40%), data silos (40%) and the speed of data integration (36%). Data privacy (30%) and the use of inaccurate or outdated data sets (30%) were also common concerns. Just over two-fifths (43%) also fear they don’t have the skills to implement predictive analytics.

Improve trust by marrying human & machine intelligence

Many of the concerns relating to predictive analytics are underpinned by a lack of human oversight of its decisions – both in terms of outcomes and explainability. To overcome these issues, more than two-thirds (69%) of IT leaders in FS advocate incorporating predictive analytics into business intelligence (BI) platforms.

Most believe integrating the powerful forecasting of predictive analytics into the BI platforms that already inform employee decision-making has the potential to:

• help organizations comply with regulatory frameworks (72%)
• significantly improve employee decision-making (70%)
• deliver a better customer experience (68%)
• identify areas of cost saving (68%)
• democratize forecasting (67%)

However, ensuring employees have the requisite data literacy to understand, question and apply the predictive forecasts to their decision-making process is key to maintaining trust and compliance. Three-quarters (76%) of IT leaders in FS highlighted the importance of data literacy training in enabling employees to recognize the limitations of the technology. And in helping them explain to customers and other stakeholders how decisions using predictive analytics are made (77%).

“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,” said Richard Speigal, BI Centre of Excellence Leader at Nationwide. “Integrating predictive analytics into BI empowers our organization to harness its benefits for improved employee decision-making, while retaining upmost trust that it will only result in fair outcomes for our customers and stakeholders.”

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