The business case for employing neurodivergent people within insurance and investment,
- Georgina Philippou
- 3 days ago
- 2 min read
Georgina Philippou, portfolio NED and Adviser, including Vice Chair of Armstrong Wolfe’s inclusion work stream, and Ambassador for The Diversity Project Charity shares her thoughts on the business case for employing neurodivergent people within insurance and investment,
“For me, everything starts and ends with the arts – that is where I find answers to questions (and sometimes questions to answers!). I looked to the arts for inspiration for the questions why should insurance and investment companies employ neurodivergent people, and why should neurodivergent people want to be employed in the insurance and investment sectors? So, what do the following famous visual artists have in common: Vincent Van Gogh, Andy Warhol, Yayoi Kusama, Leonardo Da Vinci, and Stephen Wiltshire? And what about the following famous authors: Ernest Hemmingway, Charles Dickens, and Jules Verne? They are all, or are believed to be, neurodivergent or to have neurodivergent traits. Perhaps more importantly, they have all expanded our imaginations, shown us that there are different ways to look at things, seen things that others haven’t, and shown us that diversity of thought aids creativity and innovation. They refused to be held back or pigeon holed. Now imagine the world without them.
"So for me, this encapsulates the business case for insurance and investment companies to make themselves open to the recruitment, retention and progression of neurodivergent colleagues, and the importance of them having the courage to put themselves forward for careers in the insurance and investment sector. Especially now when financial services is focused on better use of more data to shape strategies and policies and expand into new markets, and on using Artificial Intelligence to improve products and services, efficiency and effectiveness.
"Without wishing to stereotype the neurodivergent community, who better to spot patterns and anomalies in data, who better to do the detailed, concentrated work required to collect, structure, present and interpret data, who better to help design and stress test AI to ensure that it is accurate and fit for purpose and that it is unbiased and inclusive?"
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