Generative AI: ChatGPT & Large Language Models
The adoption of generative AI within the insurance industry marks a significant step in industry-wide transformation. By leveraging generative AI algorithms, insurers can harness the power of automation, personalisation, and enhanced decision-making processes. From risk assessment to customer service, generative AI can revolutionise the way insurance leaders operate and redefine industry standards. Generative AI is revolutionising the insurance industry, offering limitless possibilities for innovation and transformation. In this comprehensive guide, we will explore the concept of generative AI and its potential impact on insurance leaders. From understanding its fundamental principles to exploring real-world use cases, we will provide you with the knowledge you need to navigate the dynamic landscape of generative AI in the insurance sector.
Organisations will need to understand the countries and manner in which they intend to roll out the use of generative AI, as well as the scope of potentially relevant laws, in order to identify the laws applicable to their procurement and use of generative AI. At the same time, China is working hard to show leadership both on AI investment, home-grown technology and regulation – addressing specific issues such as deep-fakes whilst seeking to minimise social disruption. For example, the Regulations on the Administration of Deep Synthesis of Internet Information Services focus on ‘deep fake’-type use cases as well as generative AI-based chat services. China has also issued for public consultation its draft measures on the administration of generative AI services. These targeted measures sit alongside important regional approaches, notably in Shanghai and Shenzhen. Regulators could themselves make use of Generative AI capabilities, helping to enhance our productivity and reduce costs for the taxpayer.
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Each DRCF regulator is also directly engaging with their regulated industries to hear how they are making use of this technology. In June, we held a workshop to identify common risks, discuss promising interventions, and consider opportunities for joint research and cross-regulator initiatives. Present at the workshop were colleagues from across the four regulator members, including representatives from our policy, technical and economic teams. The capabilities of the content produced by generative AI could revolutionise the retail space, supercharging the productivity of your team. On the other hand, used incorrectly, this technology has the capability to produce inaccurate or even plagiarised information – and, beneath all that, there is the worry about AI “replacing” human staff.
In a previous article, we discussed the approval by the EU Council of a proposal for the AI Act. Now, the proposal is in the hands of the European Parliament, and EU lawmakers are pushing to define AI more clearly. The EP version of the AI Act, however, only compels generative AI developers to generically prevent the generation of illegal content. While a step in the right direction, genrative ai much more concrete rules are needed, particularly if actors enter the scene who actively seek to avoid content moderation. Here, lessons may be learned from the EU Digital Services Act (DSA), which seeks to reign in harmful speech and illegal content on social networks. The DSA does not apply to generative AI developers directly—this is a loophole that must urgently be fixed.
Understanding the role of generative AI in insurance industry transformation
Generative AI tools are becoming accessible to a much wider audience and so will impact our teaching, learning, assessment and support practices in increasing ways. These technologies offer the potential to support academic staff in the creation and assessment of course material, and new opportunities to engage students in problem solving, critical thinking, analysis and communication. But to use these technologies effectively, academic staff will need to understand how generative AI tools work within the context of their disciplines and higher education more widely. Generative AI models can analyse extensive customer profiles and historical data to create personalised insurance policies that match individual needs and preferences. By offering tailored coverage, insurers can resonate with their policyholders on a deeper level, fostering loyalty and customer satisfaction.
Examples of generative AI include image and video synthesis, text generation, music composition etc.
To read more, please download this free white paper. Harry also emphasised the need to understand why product specific terms are important when purchasing an AI tool to use. When purchasing an AI product with potential personal data, intellectual property and confidentiality implications, clauses should be specific to that risk, what you are expecting from the tool and your business’s needs. Harry advised that a side letter or addendum should be used as a minimum to the purchasing agreement addressing these. Data protection impact assessments are a good starting point when considering these issues. As insurance leaders navigate the transformative potential of generative AI, they must stay informed, adapt to evolving technology, and collaborate with experts to leverage the vast opportunities it presents.
More importantly, what the above chart also shows is that just focusing on broader AI will mask the disruptive pockets of innovation within AI, which are the real source of disruption. The Technology Foresights model has identified more than 150 AI innovations to watch for, and above is a subset focusing only on GenAI. It is worth pointing out that patenting is a non-trivial pursuit, with significant resource requirements in terms of time, labour genrative ai and money. So, each patent represents a meaningful addition of knowledge to the field in expectation of monetary rewards in the future. Patenting activity in GenAI innovations has shown a massive acceleration in the last four years, growing almost 5x, from around 1,450 patents to 6,000+, or 83% compounded annual growth over the last five years. This rapid acceleration shows the belief of researchers in the immense potential GenAI holds.
We have developed this explainer to cut through some of the confusion around these terms and support shared understanding. This explainer is for anyone who wants to learn more about foundation models, and it will be particularly useful for people working in technology policy and regulation. However, LLM is not without limitations, as they may sometimes produce biased or offensive outcomes due to the nature of the data they were trained on.
Specific (specialist) examples of using generative AI
There are, in particular, legal and reputational risks in relation to any customer receipt of AI output that has not been identified as such, or misleading statements relating to AI. China’s emerging laws relating to AI also include labelling requirements for certain AI-generated content. In the US, the Federal Trade Commission is focusing on whether companies are accurately representing their use of AI. For many organisations, existing governance frameworks, including policies on advanced analytics innovation, data governance and IT risk management, could be a helpful starting point for governance of generative AI systems.
- This can lead to increased customer satisfaction and loyalty, ultimately benefiting insurance companies.
- Organizations are constantly seeking the next disruptor; a way to get a leg up on and stay ahead of the competition.
- While many members of the public believe these technologies can make aspects of their lives cheaper, faster and more efficient, they also express worries that they might replace human judgement or harm certain members of society.
- Generative AI systems may be processing legally or commercially sensitive data and may be deployed in the context of regulated or operationally critical processes, with varying degrees of human involvement.
- By leveraging AI to analyse employee data, HR teams can uncover valuable insights, identify patterns, and make data-driven decisions that lead to better employee performance and satisfaction.