April 25, 2024
How Will AI Impact the Insurance Industry?
“Generative AI value-creation opportunities abound across the insurance value chain,” according to a new report by Deloitte.
However, the report says, since Gen AI is in its infancy, insurers are trying to strike the right balance between harnessing value from an emerging technology and managing risks appropriately. The report offers insurance buyers a peak into the future of how insurance companies will try to use AI to make their operations more efficient in various sectors.
Property and Casualty (P&C)
Generative AI can help streamline the claims process and optimize risk management for P&C insurers.
- Streamlined claims processing (Value drivers: enhance workforce productivity, enable cost savings and efficiencies): Gen AI-powered chatbots can record and respond to first notice of loss and give customers real-time information on triage and repair services to improve turnaround time and customer experience.
- Enhanced loss prevention and control (Value drivers: enhance workforce productivity, generate new revenue streams): Gen AI can play a crucial role in risk identification and mitigation. By analyzing claims data from multiple sources and forms (e.g., Internet of Things, video, text), historical claims, and external factors (e.g., weather patterns), Gen AI models can help P&C insurers identify areas prone to losses, which can aid in the development of risk mitigation strategies and plans (e.g., recommending safety improvements, suggesting policy adjustments to reduce future losses).
Group
Group insurers can leverage Gen AI to provide additional value to employers and employees through tailored insights and recommendations.
- Customized group plans (Value drivers: generate new revenue streams, enhance workforce productivity): Gen AI can analyze data from a variety of sources (e.g., demographic information, health profiles, and historical claims data of group members) to generate insights and inform the design of customized group insurance plans and tailored benefits packages and options, enabling the employer to improve the value proposition of the benefits package.
- Improved member engagement (Value drivers: generate new revenue streams, enhance workforce productivity): Leveraging and analyzing member data (e.g., preferences, purchase history, social media data, etc.), Gen AI models can generate personalized health tips or provide financial advice to proactively equip members with informative insight and promote overall wellness.
Risks and Regulatory Implications of AI in Insurance
Gen AI presents potential risks and adoption may be slow if the following are not considered when scaling:
- Malicious hallucinations and deep fakes, phishing and prompt injections, and ambivalent actors can expose the attack surface and erode customer trust.
- Gen AI is prone to mimicking biases and propagating discriminatory behavior if implemented without guardrails and continuous monitoring.
- Models will be trained on a corpus of proprietary and often private data, requiring regulatory compliance, node isolation, and source traceability.
- Customer servicing and engagement within insurance companies requires a heightened sense of empathy and softer human interaction skills, especially during claims processing. Overemphasis on AI-driven automation may result in a lack of human touch, potentially leading to reduced customer satisfaction and loyalty.
- Insurance regulators want oversight on insurers’ AI models and expect insurers to manage AI risk. AI oversight activity at the state level is forging ahead, with laws in place or contemplated, to bulletins from insurance commissioners asserting authority under multiple state and federal laws.
To mitigate these challenges, insurance companies should prioritize ethical AI development, leverage diverse and representative training data, evaluate and audit their AI systems on a consistent basis through a robust governance model, and maintain transparency in decision-making.