Paul Donnelly at Fineos on Data & Analytics at FinTech LIVE London 2023

Summary

Paul Donley, Executive Vice President at Fineos, discusses the complexities of integrating data analytics and AI in the life insurance industry. He highlights the sensitivity of personal data, the long-term storage of information, and the impact of decisions on individuals' lives. Donley explores the challenges presented by data privacy regulations like GDPR, societal attitudes towards AI, and the necessity for explainability in AI technologies. He also shares examples of how AI can be effectively used for non-intrusive processes like optimizing data flow and claims triage, while emphasizing the importance of striking a balance between technological advancements and ethical considerations.

Takeaways

• 😲 The sensitivity of personal data in the life insurance industry is high due to the detailed information collected during the underwriting process.
• 📈 The life insurance industry is facing societal and regulatory challenges in the use of AI and data analytics, especially with GDPR and data protection acts.
• 🏥 In Europe, insurers can access detailed personal health information, including lab reports and genetic testing, which is quite different from the US practices.
• 🚫 The 'right to forget' cancer in France allows survivors to limit the impact of their medical history on future insurance and financial services.
• 🚧 The use of AI in life insurance underwriting and claims is advancing, but the industry must tread carefully due to the significant impact on individuals' lives.
• 📊 Fineos, a company specializing in life and health insurance software, uses AI to optimize data processing and improve efficiency without infringing on privacy.
• 🛂 The application of AI in claims processing can help in triaging and routing claims to the appropriate handlers, thus improving the claimant's experience.
• 🏢 Group insurance is an area where AI can streamline the process of managing large data files without directly impacting individual privacy.
• 🚫 The concept of explainability in AI is crucial for the insurance industry, especially when making material decisions that affect policyholders.
• 🌐 Regional differences in data protection and privacy laws significantly impact how AI can be deployed in the life insurance industry across the globe.

Q & A

What is the main challenge in operationalizing AI and predictive analytics in the life insurance industry?

The main challenge is the sensitivity of personal data, the long-term retention of this data, and the highly regulated nature of the industry. Additionally, societal attitudes towards data collection and AI are hardening, leading to increased scrutiny and potential legal restrictions on the use of AI in decision-making processes.

Why is the life insurance industry considered highly regulated?

The life insurance industry is considered highly regulated because of the sensitive personal data it handles, including health information and lifestyle details. This data is often retained for long periods, and its use in decision-making processes can have significant impacts on individuals, necessitating strict oversight.

What are some examples of personal data collected by life insurers?

Life insurers collect a wide range of personal data, including financial secrets, health information, lifestyle habits, family medical history, and in some cases, genetic testing results. This data is used in the underwriting process to assess risk and determine policy terms.

How does the concept of 'right to be forgotten' relate to the life insurance industry?

The 'right to be forgotten' is a concept where individuals can request the removal of their personal data from records, particularly in cases like cancer survivors who have been clear for five years. This presents a challenge for the life insurance industry, as it may conflict with their need to assess risk based on historical health data.

What is the significance of GDPR and data protection laws in the context of AI in life insurance?

GDPR and data protection laws are significant because they provide individuals with rights over their data, including the right not to be subject to decisions based solely on automated processing. This can limit the application of AI in the life insurance industry, especially in making material decisions like policy acceptance or claim approval.

How does the use of AI in life insurance differ between the US and Europe?

In the US, there is a more lenient approach to data aggregation and use in life insurance, with third-party businesses able to provide aggregated data to insurers. In contrast, Europe has stricter regulations, such as GDPR, which protect individual data and limit the use of AI in decision-making processes.

What is the role of telematic and genetic data in the life insurance industry?

Telematic and genetic data can provide additional insights into an individual's risk profile. Telematic data from wearables and vehicles can indicate lifestyle and behavioral risks, while genetic data can reveal predispositions to certain health conditions. However, the use of such data is complex due to privacy concerns and regulatory restrictions.

How can AI be used to optimize processes within the life insurance industry without infringing on privacy?

AI can be used to optimize processes such as data shuffling within organizations, improving the efficiency of claims processing, and triaging claims to appropriate teams without making final decisions. This can enhance operational efficiency while respecting privacy and regulatory constraints.

What are some examples of AI applications in the life insurance industry that do not involve direct decision-making?

AI can be used for tasks such as predicting outcomes based on HR data, triaging claims to appropriate handlers, and identifying inconsistencies in claims data. These applications assist human decision-makers and improve process efficiency without directly making decisions that impact individuals.

How does the concept of explainability in AI affect its use in the life insurance industry?

Explainability is crucial in the life insurance industry due to regulatory requirements and the need for transparency in decision-making. AI systems that make decisions, especially material ones, must be able to explain their reasoning to comply with data protection laws and ensure fairness and accountability.