For insurance leaders, there is a uniquely bilateral aspect to these challenges as not only is it necessary to wrestle with the risks posed by the use of AI in an operational setting but also one needs to consider what AI’s use means to those consumers and businesses covered by insurers’ policies.
The breadth of the subject is vast and each organisation is very different, so I cannot hope to even scratch the surface in this piece but hopefully I can provide some actionable ideas for insurance leaders to map onto their organisations.
The overarching focus for insurance leaders who are seeking to minimise and mitigate risk when it comes to operational deployment of AI is upskilling colleagues. AI already touches many areas of operation yet there remains a large void is understanding for many people in those organisations deploying it. Whilst it is true to say that an organisation’s digital architecture and IT team’s capability is foundational to safe and ethical use of AI, the understanding of the wider ‘non-technical’ cohort is no less important to this aim. And leaders are not immune from that need for upskilling, “I don’t do technology” is not an acceptable position to adopt!
Beyond their and their business’s skills, the main areas with a need to be addressed that are applicable to both insurer operations and the operations of those they insure are:
1. Regulatory and Compliance Risks
Regulatory frameworks surrounding AI are rapidly evolving. With new legislative initiatives, such as the EU’s AI Act and emerging UK regulatory standards, insurance businesses must navigate an increasingly complex compliance environment. Ensuring transparency, fairness, and accountability of AI systems is vital. Leaders must establish a capability to proactively monitor regulatory developments to remain compliant and avoid significant fines or reputational damage. The rapidity of change and variability of ethos between territories cannot be over-stressed.
2. Liability and Accountability
How one determines accountability in decisions made or influenced by AI systems remains uncertain. As AI-driven claims processing, underwriting, and fraud detection systems become more commonplace, thorny liability questions will inevitably arise. Insurance leaders will need to understand AI’s decision-making processes clearly, document them robustly, and develop frameworks that delineate liability clearly among stakeholders, including software providers, users, and insured parties. Even where, as a result of the ‘black box’ nature of GenAI, it may not be possible to precisely explain how a decision has been made, being able to explain the design principles and safeguards in the system will be key. This system design is unlikely to exclude a ‘human in the loop’ for some time to come.
3. Data Privacy and Security
AI’s reliance on vast datasets poses significant privacy and cybersecurity risks. High-profile data breaches or misuse of sensitive personal information can severely impact customer trust. Therefore, stringent data governance practices, aligning closely with IT and compliance teams to secure AI platforms and comply rigorously with data protection laws, including the UK’s Data Protection Act and GDPR must be in place.
4. Ethical and Bias-Related Concerns
AI systems are susceptible to biases arising from their training data, potentially leading to discriminatory outcomes in claims assessments, underwriting, and customer interactions. Bias in AI could result in reputational damage, regulatory scrutiny, and potential legal actions. Regular audits of the end-to-end process in which AI systems play a part are vital to ensure transparency in AI decisions, and uphold ethical standards to mitigate biases effectively.
5. Operational and Performance Risks
AI deployment may introduce operational uncertainties, particularly when systems fail or make erroneous decisions – so called hallucinations. AI performance relies heavily on data quality, system robustness, and accuracy. Rigorous and on-going testing, validation, and continuous monitoring of AI-driven processes, with contingency plans for system failures, to maintain reliability and performance must be in place.
6. Strategic and Competitive Risks
The adoption of AI can significantly alter market dynamics, creating competitive risks for insurers who are slow to adapt. Conversely, poorly managed AI strategies may result in unsustainable costs or misplaced investments. Senior leaders must approach AI strategically, aligning AI initiatives closely with organisational objectives, customer expectations, and market positioning.
AI’s potential for operational improvement for insurers and their policyholders is significant, but navigating its associated risks demands vigilance, preparation, and strategic foresight.
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