With our lives increasingly moving online, especially in the wake of Covid-19, our personal data is at an all-time high risk of being misappropriated for nefarious purposes. ATO is one such example; this involves criminals gaining unauthorised access to a user’s account with the intention of dishonestly using their data. In the insurance industry, this can take the form of using policy information to modify coverage or to re-route a claims payment to themselves.
In a similar vein is application fraud, where fraudsters create fake accounts using someone else’s information. Once the fake account is active, the fraud actor typically opens an insurance policy for fictitious beneficiaries or falsifies application information to reduce the premium. During the pandemic, some fraudsters labelled themselves as NHS staff or key workers in order to benefit from concessions offered by insurers.
Fraud investigation used to be a manual, time-consuming process but is increasingly being assisted by technology. Fraud analytics, powered by machine learning can use multiple techniques such as text mining, and anomaly detection to unearth likely fraudulent behaviours. Artificial intelligence (AI) tools are especially useful as they can adapt to new circumstances and evolving fraud techniques, helping to stay one step ahead of the criminals. However, the driving force behind machine learning and AI is data, which needs to be both plentiful and accurate.
Technology is also aiding industry collaboration. In 2019, the Insurance Fraud Intelligence Hub (IFiHub) was launched, which allows members to view real-time data in a GDPR-compliant database and compare with their own intelligence to unearth the criminals.
In motor and fleet lines, it’s hoped that advancements in vehicle (such as telematics data or dashcam footage) will provide more information for supporting honest motor claims and identifying fraud.