AI, Machine Learning, Robotics, Chat-bots, Digitising Claims, Fraud Prevention, automating workflows- too many insurers wade into such projects without first addressing the data silos and data inaccessibility stopping effective analytics.
It is refreshing to see Todd Fancher Chief Digital Transformation Officer at AmFam state "it’s really about data and how we’ll utilize it".
Not just the sexy external data but the boring internal data generated over years of customer engagement, selling policies and claims management.
Every insurer needs the data management skills, staff and tools like 360Retrieve to access, analyse and visualise both structured and unstructured data.
Take the Auto Claims Notification Form (CNF) UK claimants send to their insurers after accidents, theft or loss. Only a handful of the fields are actually analysed whilst the wealth of data in free text fields is neither accessed nor analysed. Yet is is here that the fraudster leaves compelling yet hidden evidence of over-exaggeration, omission and lies. It is one of the reasons that whiplash claims are so high in the UK.
Get this right and the insurer has a significant competitive advantage over full stack insurtech newcomers and the digital conglomerate GAFA enterprises that will target distribution and succeed unless insurers anticipate this by effective data management to better:-
- Make optimal decisions
- Execute them
- Compete digitally
Because “digital transformation” is an ongoing evolution for us, rather than any single initiative with a start and end date. At a high level, it means infusing the business with advanced digital capabilities to improve the customer experience, achieve operating efficiencies and drive industry change. More specifically, our definition will include leveraging technologies, such as AI, machine learning or robotic process automation, but it’s really about data and how we’ll utilise it. Like most insurers, we have decades of outcome data. Now, with evolving capabilities, the availability of new data sources and advances in analytical modelling, we can marry historical data with real-time data to create something more powerful. The ultimate goal is establishing new ways of working that are more data-driven in order to become more proactive and valued in our customers’ lives.