Episode 67
Advancing AI Across Insurance by KPMG. Report Summary
In this (AI generated) episode we explore AI in Insurance!
Because there's a more engaging way to g through reports - someone telling you the key take aways, like a short story. So this is what I've done with the help of Google NotebookLM!
This week this is our report of choice:
KPMG. Advancing AI across insurance: Unlocking transformation with speed and agility
Download HERE
We ask notebook to answer the following:
- How does the insurance industry approach AI transformation?
- What are three key findings from the KPMG Insurance CEO Outlook?
- What are three risks associated with AI adoption in the insurance industry?
- AI Strategy Breakdown
Is it as good as taking 30mins to read through the report in detail? Nop!
Is it a great way to stay up to speed with what's going on in AI in the FinTech and Financial Services Industry? Definitely Yes!
Efficiency and practical works.
Key Takeaways (Of the pod & report - manually extracted):
- Gen AI is expected to become a US$1.3 trillion market by 2032
- 47% of insurance organizations that are experimenting with AI have set up AI centres of excellence.
- Identify your data sources and assess their quality as part of the internal review. It’s important that insurance organizations have accurate and reliable data before building and training machine learning models to rely on this
- Operational effciency (including task automation and employee experience), along with advanced pattern detection, are the top two short-term AI goals identifed by insurance organizations
- KPMG proposes an "AI maturity assessment framework" assessing 6 elements (see page 19)
- 61% of organizations are wary about trusting AI systems, with 84 percent citing cybersecurity as a top concern
- Remember that AI works best when it is supporting and augmenting people and processes — not replacing them. Keep a human in the loop
- KPMG's Trusted AI Framework is a set of ethical standards for the technology to act for the public's interest, based on principles for AI that are values-driven, human-centric, and trustworthy.
Strategy:
- Assessment of your current AI capabilities
- Define vision and goals. What you actually want to achieve with AI and how it will actually help you achieve your business objectives
- Pilot with a specific use case
- Upskill your workforce!
Risks
- Bias. Perpetuate existing bias based on existing data.
- Privacy. Systems rely on a lot of persona data -- needs to be secured.
- AI decision making with unintended consequences. Ensure AI is aligned with human values!
Use cases
- Enhancing actuarial processes though Gen AI
Case studies:
- PassportCard
- Israeli financial services group
- Beazley
Successful adoption is about people, processes and considerations!
Don't be afraid to experiment and iterate, be willing to try and learn!
This is a journey, not a destination, enjoy!
Don’t forget to like and subscribe for more episodes like this!
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- LinkedIn: https://www.linkedin.com/in/monicamillares/
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Remember this is an AI generated podcast. If you want to listen to human interactions, head to my Purpose Driven FinTech Podcast. Cheers, Monica
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Disclaimer: This episode does not constitute professional nor financial advice and does not represent the opinion nor views of my current, past or future employers. The guest has agreed to record and release our conversation for the use of this podcast and promotion in social media.
