This article is by Erez Barak, Chief Technology Officer at Earnix, and it looks at how machine learning, automated systems and AI need to be balanced by the human touch across the insurance sector.
Dynamic and fast-evolving tools like artificial intelligence (AI) and machine learning (ML) are reshaping the landscape of insurance, offering unparallelled growth opportunities, but also raising questions that the industry must address. While challenges exist, the focus is on harnessing the transformative power of AI and machine learning (ML) for good: to enhance operations, deliver personalised experiences, and drive profitability.
AI and ML adoption is on the rise, with European businesses projected to experience significant growth in implementation in 2024. A recent report from Amazon Web Services projects a 32% year-on-year increase in European businesses deploying AI in 2024. This trend could potentially contribute €600 billion in gross value added (GVA) to the European economy by 2030, equivalent to the value of the entire European construction industry.
The significance of this potential permeates all businesses. In the insurance sector, AI and ML are already revolutionising core systems, underwriting practices, and exposure modelling. These automated tools enable insurers to streamline operations and workflows, make data-driven decisions, and provide tailored experiences at a pace that was never possible. This is catalysing a seismic shift in how insurers perceive and harness the power of AI and ML in their core systems, and also in their underwriting practices and exposure modelling.
By leveraging these tools, insurers are reducing manual tasks, and enabling employees to focus on higher-value activities. They are also deploying advanced analytics and real-time systems that enable them to identify customer needs and market trends swiftly, tailoring products and services accordingly.
But despite the promise of AI, challenges persist, particularly in operationalising AI models effectively. The agility of product innovation has become paramount in this AI-driven landscape. Quite simply, insurers must embrace agile methodologies to swiftly iterate, adapt, and capitalise on emerging opportunities. It’s not merely about keeping pace with change; it’s about staying ahead of the curve and delivering on elevated customer expectations.
Insurers face the task of bridging the gap between AI innovation and practical implementation, demanding robust infrastructure and governance frameworks. However, proactive investment in these areas can pave the way for smoother integration and utilisation of AI capabilities.
The Role of Explainability
Amidst this rapid integration of AI and ML, the concept of explainability is a crucial component of every Responsible AI program. Understanding the inner workings of intelligent, automated systems and the outputs they generate is essential for insurers to build trust and ensure transparency.
With automated tools playing a role in predicting everything from claims likelihood to premium rates, the opacity of the algorithms and decision-making tools being deployed raises questions about fairness and trust. Focusing on the discipline of explainability enables insurers to delve into the layers of complex algorithms, revealing the “why” and “how” behind decisions. This transparency not only fosters customer confidence but also ensures ethical and responsible deployment of AI and ML.
Explainability should be a boardroom topic when it comes to ML and AI deployment, empowering insurers to shed light on their complex systems, enhancing transparency and human oversight. And explainability goes hand in hand with Fairness – which is critical to the future of AI and ML in insurance. Identifying, measuring, exploring, and mitigating fairness issues in predictive models is key, focusing on understanding and addressing potential biases or unfairness in model predictions, particularly concerning sensitive parameters or regulations.
Though predictive modelling has been around for decades, the focus on fairness and responsibility is a more recent development, particularly in the financial sector, where regulations are evolving.
Ultimately, the goal is to ensure that the future of insurance balances technological advancement with human understanding and trust. Insurers must proactively invest in governance frameworks and explore emerging best practice guidelines to ensure ethical AI use and mitigate risks associated with bias and transparency. Collaboration with technology providers and industry experts is essential to drive innovation and resilience forward in this field.
I am convinced that AI in insurance represents a journey toward faster decision making coupled with greater understanding and transparency. As insurers continue to embrace AI and ML, success hinges on proactive adaptation and strategic engagement on complex but critical questions around fairness and explainability. By navigating the evolving landscape with trusted partners and a focus on understanding, insurers can unlock the full potential of AI to drive efficiency, relevance, fairness and profitability in the insurance industr
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