This episode of Unstructured Unlocked comes from a webinar titled “From Expertise to AI” with industry experts from Indico Data, the Swiss InsureTech Hub, and Microsoft for an in-depth discussion on the future of insurance underwriting. The panel, comprising Tom Wilde, CEO of Indico Data; Silvia Scherrer, President and Co-Founder of the Swiss InsureTech Hub; and Naveen Dhar, Senior Director at Microsoft, explored the industry’s talent acquisition challenges and the transformative potential of artificial intelligence (AI) in addressing these issues.
Listen to the full podcast here: Unstructured Unlocked season 2 episode 6 with Silvia Signoretti of Swiss InsurTech Hub and Naveen Dhar of Microsoft
Addressing the talent gap in underwriting
The panel highlighted the pressing issue of talent shortages in the insurance industry, exacerbated by the impending retirement of experienced professionals. As Tom Wilde pointed out, “71% of property-casualty insurers expect to increase their staff in the next 12 months.” However, he also noted the challenge posed by the aging workforce: “While most insurers are planning to grow over the next 15 years, the Chamber of Commerce estimates that 50% of the insurance workforce will be at retirement age.” This situation underscores the need for efficient knowledge transfer and training for the next generation of underwriters.
Silvia Scherrer emphasized the shift in the industry, noting, “The challenge of talent acquisition in insurance generally is not a new topic… it is becoming a more clear issue as we see from demographic statistics.” She added that there is a growing need to “embed technological solutions to improve processes” and to “really look at underwriting functions in a way that integrates technical skills with emerging technology.”
AI platforms like Indico are serving to lessen the impact of talent losses in the industry by equipping today’s underwriters with the best possible tools and data for their jobs. Our vision and hope is to help produce an even more highly skilled generation of underwriters through unstructured data extraction and insights using AI.
Related content: Risk assessment redefined: The role of automation in insurance underwriting
The role of AI in modern underwriting
The discussion delved into how AI and other advanced technologies can enhance underwriting efficiency and accuracy. Naveen Dhar from Microsoft highlighted the benefits of using AI to assist underwriters, saying, “It’s not to replace the underwriter, but it is to help the underwriter, and that productivity gain comes through.” He elaborated on the use of AI tools like chatbots and custom copilots, which can streamline processes and reduce the time underwriters spend on administrative tasks.
Tom Wilde also discussed the evolution of AI capabilities, from predictive to interpretive, stating that the industry has moved on from the days predictive use cases and into more interpretive uses. This shift allows for more complex data analysis and decision-making support, which is crucial in managing the increasing volume and complexity of insurance data.
Leveraging AI for enhanced underwriting efficiency
This transition from simple data extraction to complex interpretation allows for a more nuanced understanding of risks and customer needs. Wilde elaborated on AI’s ability to augment human effectiveness, stating, “…Your ability to apply what is now characterized as ‘human in the loop’ is vital… This is all about making the team more efficient, more effective than applying a robot to do this work, which is just not on the near term horizon.”
Overcoming challenges with unstructured data
One of the significant challenges in underwriting is managing unstructured data. Naveen Dhar explained the difficulties associated with this, describing the way data intake and management has traditionally worked in the industry: “documents could be financial documents, it could be medical documents, it could be an ID to identify who it is. Traditionally what we had was… a shared center that would actually take those documents, read them and identify and summarize what those documents were before it went on to the underwriter. But that process… used to go back and forth multiple times, because guess what? The underwriter gets it and says, well, I’m missing one document. The process starts all over again.” He emphasized the importance of using AI to extract data efficiently and accurately, thus improving the overall underwriting process.
However, Tom Wilde added that traceability and transparency are crucial for AI regulation and security: “People would show up with models that they would bring to banks or insurance companies and say, Hey, this is our proprietary risk model. Don’t worry about what’s inside the black box. It just works. And that’s no longer going to work… AI presents some challenges around traceability and explainability that you have to carefully think through when building that out.” The ability to trace and explain decisions made by AI systems is vital for maintaining compliance in the industry.
Related content: Enhancing underwriting processes with intelligent document processing
Embracing AI for a brighter future
Our round table discussion covered the multifaceted role that AI is playing in the insurance industry right now, taking a retrospective look at the industry’s past and a hopeful look at its future. The integration of AI tools can address the talent gap by enhancing the efficiency and accuracy of underwriting processes. As the industry continues to evolve, the successful implementation of AI will depend on balancing technological innovation with the need for human expertise and judgment. Our guests provided great insights into how AI can be harnessed to create a more efficient and responsive insurance industry.
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Frequently asked questions
- How are insurance companies addressing the need for continuous learning and upskilling in the face of rapid AI advancements? The insurance industry is actively investing in continuous learning and development programs to keep its workforce abreast of the latest AI advancements. Companies are collaborating with educational institutions and professional training organizations to offer specialized courses and certifications in AI and data analytics. Additionally, many firms are establishing internal AI academies and workshops to facilitate ongoing education. These initiatives help ensure that underwriters and other insurance professionals are well-equipped to leverage new AI tools and methodologies, thereby enhancing their decision-making capabilities and overall efficiency.
- What specific AI tools and technologies are currently being used by underwriters to manage unstructured data? Underwriters are using a variety of AI tools and technologies to handle unstructured data, including natural language processing (NLP) and machine learning algorithms. Tools such as optical character recognition (OCR) are employed to digitize and extract information from scanned documents. AI platforms like Indico Data leverage NLP to analyze and interpret large volumes of textual data, enabling underwriters to extract key insights and make more informed decisions. Additionally, custom-built AI copilots and chatbots assist in organizing and summarizing unstructured data, reducing the manual workload and improving accuracy.
- What are the ethical considerations and potential biases associated with the use of AI in underwriting, and how are they being addressed? The use of AI in underwriting raises several ethical considerations and potential biases, such as the risk of discriminatory practices and lack of transparency in decision-making processes. To address these issues, insurance companies are implementing robust AI governance frameworks that include bias detection and mitigation strategies. Regular audits of AI models are conducted to ensure fairness and compliance with regulatory standards. Moreover, transparency is being enhanced through explainable AI (XAI) techniques, which allow underwriters and regulators to understand and trace the decision-making processes of AI systems. This ensures that AI-driven decisions are accountable and can be justified based on clear and ethical criteria.
