In episode 3 of Unstructured Unlocked, hosts Tom Wilde and Michelle Gouveia discussed the transformative impact of AI and machine learning in the insurance industry with Kelly Cusick, Managing Director at Deloitte Consulting. The conversation focused on how these technologies are reshaping underwriting, risk assessment, and overall operational efficiency.
Listen to the full podcast here: Unstructured Unlocked season 2 episode 3 with Kelly Cusick, Managing Director at Deloitte
Automation in underwriting
Kelly Cusick, with over 25 years in the property and casualty insurance industry, emphasized the longstanding journey of automation in underwriting. Traditionally, efforts have concentrated on the “bookends” of the process: initial submissions and end-of-process transactions. Automation has significantly reduced manual re-keying and streamlined document management. This shift towards sophisticated automation moves beyond basic processing, enabling more nuanced risk assessments and decision-making capabilities. Indico’s AI-driven Intelligent Document Processing (IDP) solutions are part of this shift, offering advanced tools to enhance these underwriting processes.
Enhancing underwriter efficiency
AI in intelligent document processing plays a crucial role in synthesizing vast amounts of information, which can be overwhelming for human underwriters. Cusick explained, “You as a human can only synthesize so much information at any one time. So if you have better tools and models that can do that for you, then it’s providing you guidance…it’s saving time from having to poke around a website, and then go do a search, and do a bunch of pivot tables on your own data, and that sort of thing.”
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This ability to quickly process and present relevant data helps underwriters focus on high-value tasks, such as portfolio management and strategic decision-making. And the purpose of AI in this case is not to replace human employees, but to enhance their efficiency and efficacy—a goal that Indico helps insurance agencies achieve through its intelligent document processing technology.
Leveraging predictive analytics and real-time data analysis
Machine learning (ML) in document processing is instrumental in identifying predictive signals within data. For instance, it enables underwriters to better understand the combination of factors that make a risk more acceptable or problematic. On this point, Cusick shared an example from the food industry: “So I may know that… this is a restaurant that has a fryer that’s more of a risk, but if I have a bunch of restaurants that have fryers, what is that other thing or other factor that, in combination, make this a good risk versus a not so good risk? So again, the human brain can only process so much, so how can AI or some sort of model give me that [assistance]?”
Predictive analytics enables underwriters to spot trends and make informed decisions about risk management and policy adjustments that they wouldn’t be able to without the added edge of information that ML provides. Indico’s machine learning capabilities provide the tools that insurance agencies need to derive these insights, enhancing the predictive power of their underwriters.
Related content: Artificial intelligence underwriting: Transforming industries with precision and efficiency
The integration of AI and ML also enhances real-time data analysis, which is vital for managing risk concentrations and underwriting portfolios. Cusick underscores the critical need for current data to avoid prematurely halting business in certain areas. With real-time data, underwriters can make more informed decisions regarding capacity and risk distribution.
Unstructured data and actuarial models
A significant challenge in the insurance industry is the vast amount of unstructured data, such as documents in archives. This is a conversation that Indico is well versed in, as our advanced document processing capabilities are specifically designed to handle unstructured data efficiently—making it easier for insurers to extract valuable insights. AI and machine learning can unlock the value of this data, feeding it into actuarial models to improve risk assessment and underwriting accuracy. Our thoughts align with Cusick’s on the matter: “If you can start to use large-language-type models… to pull those things together and then start to pull out the themes, I think that’s where you’re going to start to get some really interesting insights.” Simply put, one of intelligent document processing’s main functions is extracting meaning from huge amounts of unstructured data in order to feed that data into actuarial model hypotheses—resulting in real-world direction and insights for insurance companies.
Compliance and transparency
In a conversation about integrating AI in intelligent document processing, we would be remiss to not mention compliance and transparency, especially amid the evolving regulations of the industry. Indico’s commitment to transparency and compliance ensures that our IDP solutions meet these critical standards, but it’s certainly no simple task. Cusick said that “The regulators are trying to figure out ‘How are we going to regulate this?’ because they have the principles that they need to abide by, but then they also have the practical reality of ‘We in the insurance department cannot hire legions of data scientists.’” Ensuring that AI models are explainable and transparent is crucial for meeting regulatory requirements and maintaining trust; however, insurance agencies must also successfully manage their own costs while maintaining compliance.
Looking ahead: the transformative impact of AI and machine learning on IDP
By focusing on continuous improvement and staying informed about the latest advancements in AI and ML, organizations can ensure that their IDP systems remain at the cutting edge of technology. This proactive approach will enable businesses to harness the full potential of intelligent document processing, driving greater efficiency, accuracy, and overall business performance.
Related content: Transforming underwriting: Challenges, opportunities, and the AI advantage
We’re so grateful to Kelly Cusick for joining us on this episode and sharing her insights on the insurance industry—we hope that you walked away with some new information and knowledge about where our industry is going. Thank you as always for listening! Be sure to subscribe and stay updated on future episodes, and you can check out the full podcast episode on your favorite platforms, including:
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Frequently asked questions
- What specific examples of AI-driven tools or models, besides Indico’s IDP solutions, are currently being used in the insurance industry to enhance underwriting and risk assessment? In the insurance industry, several AI-driven tools and models are enhancing underwriting and risk assessment beyond Indico’s IDP solutions. For instance, companies are using AI-powered platforms like Shift Technology and Cape Analytics, which employ machine learning to detect fraud and analyze property data from satellite images, respectively. Additionally, IBM’s Watson offers predictive analytics and natural language processing capabilities to streamline underwriting processes and improve risk evaluation.
- How does the integration of AI and machine learning in intelligent document processing impact the job roles and skill requirements for underwriters and other insurance professionals?The integration of AI and machine learning in intelligent document processing significantly impacts job roles and skill requirements for underwriters and other insurance professionals. Underwriters are now expected to have a solid understanding of AI and data analytics to effectively utilize these advanced tools. This shift necessitates continuous learning and upskilling in areas such as data science, machine learning, and AI ethics. While AI handles routine data processing and initial risk assessments, underwriters focus more on strategic decision-making, portfolio management, and complex risk evaluations that require human expertise.
- What are some of the main challenges or limitations that insurance companies face when implementing AI and machine learning technologies, and how can these be addressed?Despite the benefits, insurance companies face several challenges when implementing AI and machine learning technologies. One major challenge is the integration of these technologies with existing legacy systems, which can be complex and costly. Data quality and consistency also pose significant hurdles, as AI models require large amounts of high-quality data to function effectively. Additionally, there are concerns around data privacy, security, and compliance with evolving regulations. To address these challenges, companies need to invest in robust IT infrastructure, ensure thorough data governance practices, and stay informed about regulatory changes to maintain compliance and build trust.
