AI & AUTOMATION: THE AGE OF HYPER-AUTOMATION
AI, BIG DATA AND ‘US’ As insurance speeds towards hyper-automation, Aniqah Majid weighs up the benefits and bear traps
Automation has been imperative to the insurance industry for decades. With the inception of ACORD, the sector’s standards-setting organisation, in 1970, and the IBM personal computer, large-scale machine processing used by insurance firms and independent agents was an obvious response to rapid consumer growth and demand. Aviva was one of the first to bring automation to scale with its pensions division in the early 2010s. It employed a low-code, Cloud-based task management platform provided by Appian that integrated with the data contained in its legacy infrastructure and was used to match employee skills to claims cases. The insurer saw a 40 per cent increase in efficiency, with customer queries
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resolved in a matter of minutes. Encouraged by the results, and as the insurance landscape became more competitive, Aviva went on to install 40 automated applications, all of them running on top of existing infrastructure in pensions, health and general insurance. The ball was rolling and others followed. Between 2016 and 2021, the amount insurance companies spent on IT increased by around 650 per cent, according to Statista. Now, the arms race is between AI and robotic process automation (RPA).
MOVING UP A GEAR Used together, AI and RPA herald the new era of hyper-automation. “It’s about automating non-standard variables,” Leon Fretz, the financial services director of insurance and investment at Microsoft UK, explained during a recent webinar. “(It’s about processing) outside of the normal factors, that require more intelligence and insight than robotic processing automation [alone].”
In real life, that translates in motor insurance, for example, to RPA assessing individual factors around a claim while the likelihood of fraud as part of the risk assessment is predicted through AI. A report released last year from McKinsey, Insurance 2030 – The Impact Of AI On The Future Of Insurance, presented a controversial image of hyper-automation, claiming that by the end of this decade, virtually all decisions an insurance company makes, from underwriting to claims processing, will be informed by AI. It based that projection on the inexorable desire to prioritise customer experience and maximise profits, all the while cutting costs. Insurers, said McKinsey, will ensure this through the adoption of big data tools and specialised learning models. Bastiaan De Goei, the insurance industry leader at Instabase, explains: “Unlike the first document-understanding solutions, which were template-based or rules-based, today’s deep learning models understand a document’s context and content in its raw form and can process highly variable and complex documents without human intervention. They become smarter over time, generalising their learnings across diverse document types and evolving, using human-in-the-loop processes.” ffnews.com
