Three Reasons Why the Financial Industry Still Hasn’t Harnessed Predictive Analytics
And One Way to Unleash Its Potential
Financial services organizations collect more information on their customers than any other industry. But, as Nick Blewden, Head of Data Products at Lloyds of London, states, “Raw data on its own doesn't necessarily hold that high a value. The most focused and high-value data products produce outcome-focused insights.”
Businesses need to arm their employees with insights that don’t just tell them what has happened, but what, based on the data, is likely to happen. This is critical to knowing what informed action to take in the moment.
Predictive analytics plays a key role in this. It combines historic data with intelligence gathered in real-time to understand and forecast how new scenarios are evolving. It might surprise you, therefore, to learn that adoption is slow amongst British financial institutions. More than half (53%) have five or fewer predictive analytics use cases in implementation – in stark contrast with the leading 10% which have each introduced over 50.
To better understand why adoption is so low, we surveyed more than 500 UK technology leaders in financial organizations about attitudes toward the use of data and predictive modeling. It revealed that, despite there being a clear appetite for the technology, there are three hurdles that are holding back more widespread adoption:
- Trust – Only half of IT leaders (51%) trust decisions made by predictive analytics solutions are without bias and always accurate (45%). Trust is so low that they wouldn’t even allow predictive analytics to influence their own financial transactions, with less than half (45%) trusting it to manage their children's pocket money. But, the main issue stems from the fact that a third (33%) fear their customers will not trust the decision made by predictive analytics solutions. And, customers won’t choose to bank with an organization that they don’t trust.
- Regulatory compliance – Over two-fifths (44%) of IT leaders fear they could be held responsible for the decisions made by solutions that trigger actions solely based on predictive analytics. Their concern is enough that 46% feel that the regulatory burden of these technologies outweighs the benefit.
- Technical capacity – Predictive analytics solutions must be fed with governed, real-time data from a robust analytics data pipeline to provide accurate, trusted insights. However, many IT leaders cite vulnerabilities in their data pipeline that pose a threat to its successful implementation. Two-fifths of IT leaders cited concerns over data quality (41%), data silos (40%) and the speed of data integration (36%). There is also a perceived lack of skills internally, and many fear they lack the technical expertise to deploy predictive analytics in their organization (42%).
These are not insubstantial hurdles. To get over them, financial services organizations need to integrate predictive analytics with modern business intelligence, which marries the best of human and machine intelligence, so that they are prepared to take more informed, accurate and trusted actions in the business moment.
What this looks like and how to achieve it are outlined in the wider report on the findings – “Unleashing the Potential of Predictive Analytics in Financial Services” – which you can read here.
From strengthening the analytics data pipeline, to actually deploying analytics to overcome many of the regulatory obstacles, the report provides practical and tangible steps that can help IT leaders take advantage of the potential for predictive analytics, while retaining faith in the data, the process and its outcomes.
Paul Carey, Manager of Data Management at HSBC, beautifully summarized why taking the time to get these different elements right is crucial, “Modern leaders have long known that they needed data. What we are learning now is just how powerful full data access is in the hands of the entire organization. Data quality, for example, is easier to achieve when the entire organization knows its role in maintaining data integrity. Making data-driven decisions is also simpler when the insights from financial statement analysis can be distilled into actionable lists for frontline employees. Companies where everyone contributes to business intelligence are better able to steer toward success.”
If you’re interested in learning more about how to get the most out of predictive analytics, download the report.