New research from Qlik reveals how concerns around trust and regulatory compliance, as well as weaknesses in the data pipeline, are slowing the adoption of predictive analytics in Financial Services (FS), preventing insurance organizations from reaping the full value from their data.
The report “Unleashing the Potential of Predictive Analytics in Financial Services”, which surveyed 503 UK IT leaders in financial services organizations, of which more than 200 held roles in insurance, exposes the slow uptake of predictive analytics. Two-thirds (65%) of British insurance institutions have five or fewer predictive analytics use cases in implementation – in stark contrast with the leading 9% of institutions which have each introduced 40 or over.
Key issues IT leaders in insurance 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 (29%) of IT leaders in insurance 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 (47%). In fact, only half of those working in insurance (50%) trust predictive analytics to always give them the best price on their car insurance.
· Regulatory risk – In such a highly regulated industry, 39% of IT leaders in insurance fear they could be held personally responsible for decisions automatically triggered by predictive analytics solutions. The regulatory burden also weighs on them, with 37% reporting it outweighs the benefit the solution could offer.
· Flaws in the data pipeline – IT leaders in insurance also cite a number of technical barriers to implementation. Two fifths face issues with data quality (44%), data silos (41%) and the speed of data integration (36%). Data privacy (35%) and the use of inaccurate or outdated data sets (35%) were also common concerns. Almost half (45%) also fear they don’t have the skills to implement predictive analytics.
LOOKING FOR DATA PATTERNS
“The insurance industry is starting to see the value that can come from personalized insights provided at the right time and in the right way. Doing so can provide real value and transform user experience without eroding trust that is a risk when raw data is shared or sold,” said Nick Blewden, Head of Analytics at Lloyd’s of London.
“At Lloyd’s we use the Insights Hub to show each Lloyd’s insurer their portfolio with personalized benchmarks and market intelligence to support their strategic decisions in an easy-to-understand way. Lloyd’s are providing powerful analytics to all insurers in the global Lloyd’s market for free as they realize the power the right personalized analytics can have in transforming business outcomes.”
“Our research has shown that many IT leaders are yet to fully trust the insights from predictive analytics and the impact these decisions could have on their customers,” adds Adam Mayer at Qlik. “This trust needs to be built from the ground up. Real-time, hyper-contextual information, with clear data lineage and robust governance, must feed the analytics data pipeline, revealing insights that data literate employees can discerningly use to inform decisions. This will empower financial services organizations to look forward and take action, rather than react to business moments as they arise. Helping them truly achieve Active Intelligence from their data.”
Find out more in the full report here: Unleashing the potential of predictive analytics in financial services