AI technologies offer major opportunities for the insurance sector.
InsurTech startups are creating compelling new uses and applications for data, algorithms and artificial intelligence (AI) across the sector. The potential of utilising this technology is enormous, however, the question is how to put it into practice.
A recent survey by KPMG International, (The robots have arrived. Are you ready?) shows the insurance industry is very clear on the value AI can deliver. However, 91 percent of insurance CEOs also voiced concerns about the challenge of integrating automation, AI and cognitive robotics into their existing business and operating models.
For now, most leading insurers are focused on machine learning. This uses trained algorithms, based on historical labelled data and decisions, to answer a specific business question. An insurer would be able, for example, to train the machine on historical claims then allow it to make routine claim decisions.
Machine learning excels at the more ‘narrow’ applications, that focus on improving the speed, consistency and volume of business decisions. Insurers are starting to employ the technology in these areas – and it’s already delivering tangible results.
Forward-looking executives know that innovation in today’s environment is all about building a culture of experimentation. They also know that they need to improve the efficiency of their operations. And they are increasingly aware that machine learning sits right at the heart of these demands.
1. Secure a great team: How will you secure and develop the right talent to ‘drive the machines’? How will the teams be structured and work with the business?
2. Create global alignment: Do your projects and methods drive the corporate strategy? Are your global technology stacks aligned?
3. Educate leadership: Do your executives truly understand the value of machine learning? Do they understand the key terms and use cases?
4. Invest at scale: Are you investing enough in technology, people, alliances and capabilities? Are you growing your machine learning team?
5. Be obsessive about data collection: Are you collecting and curating enough of the ‘right’ data – and labeling it fast enough?
6. Take a portfolio approach: Do you balance the risks and rewards of your machine learning projects as a portfolio of investments, rather than hedging your bets on one big mega-project? Do you continuously track the benefits of the investments?
7. Embrace streaming data: Are your systems and processes capable of managing streaming data? Are you able to apply machine learning in the stream and take business decisions in real-time?
8. Review your cloud strategy: Are you able to quickly scale your machine learning platform to meet your IT needs? Do you have a clear DevOps strategy to ensure your cloud is automated and deployments are repeatable?
9. Open up your architecture: Does your architecture enable collaboration with technology providers and suppliers? Are you driving integration through your open application programme interface?
10. Think about production: Do you have a model for pushing new machine learning models into production? Can your underlying infrastructure enable elasticity and keep up with demand?
Ultimately, the greatest challenge for insurance executives is to be comfortable with failure. The reality is that – as with any area of experimentation and innovation – not all machine learning investments will translate into commercial results. The trick is to fail fast, learn from your experience then exploit your new knowledge in the next project.
Above all, the beauty of today’s technology is that it’s perfect for this type of speedy and cost-effective experimentation.
AI technologies offer major opportunities for the insurance sector. The spoils will go to those who start by getting the foundations right.
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