Artificial intelligence, or “AI” as it is called, is dominating the IT field in industries across the board. Discussions about this new breakthrough dominate articles, blogs, newscasts, and podcasts. “What are the advantages? What are the disadvantages? What are the dangers?”… and on and on. Should insurance, or more specifically legacy business, be excluded from this robust debate? The answer, according to Rooney Gleason at Evolution IQ, is a resounding “no.”
In the Q and A that follows, Rooney explains how artificial intelligence can be harnessed to revolutionize claims handling, even for legacy business. His premise is simple: AI algorithms analyze vast amounts of data, streamlining claims management, reducing adjusters’ workload, and letting companies provide better, faster insurance services. But be careful. Unique challenges lurk in the high grass of reinsurance and runoff operations that can be addressed.
1. Can you describe EvolutionIQ and the motivation behind its founding, particularly its focus on leveraging artificial intelligence, machine learning, and natural language processing in claims analysis?
EvolutionIQ was founded with the mission to harness the power of AI, machine learning, and natural language processing to revolutionize claims analysis in the insurance industry. The company’s primary goal is to help injured and ill individuals return to their productive lives as efficiently and effectively as possible. In the insurance sector, there is an abundance of claims data accumulated over many years, providing a fertile ground for the development of AI and machine learning-based solutions.
The complexity of medical claims is growing at an unprecedented rate, fueled by the introduction of new treatments, drugs, and the rising incidence of population comorbidities. This increasing complexity, coupled with the high volume of claims that adjusters must manage, poses significant challenges for even the most efficient claims organizations. Adjusters often find it difficult to stay abreast of every claim, ensuring each one receives the attention and expertise it deserves.
EvolutionIQ addresses these challenges by employing sophisticated AI algorithms that analyze vast amounts of data, helping claims organizations identify critical cases and prioritize them accordingly. By integrating these advanced technologies, EvolutionIQ aims to streamline the claims management process, reduce the workload on adjusters, and ultimately provide better, faster support to those in need of insurance services. This approach not only improves the efficiency of claims handling but also contributes significantly to the wellbeing of claimants, aligning perfectly with the company’s core mission.
2. What are the specific benefits of applying these technologies to insurance claims?
Applying advanced technologies like AI and machine learning to insurance claims brings several specific benefits, streamlining the claims management process and enhancing overall efficiency. One of the key advantages is the daily prioritization of claims requiring higher levels of intervention. By identifying these claims early, insurers can allocate the necessary resources and attention, ensuring timely and effective handling.
Equally important is the identification of claims that do not require additional intervention from the First Notice of Loss (FNOL) or First Report of Injury (FROI). This capability significantly frees up resources, allowing them to be redirected towards claims that would benefit from more intensive engagement. Such an approach not only optimizes resource utilization but also accelerates the processing of straightforward claims.
Another critical benefit is the constant alerting on claim development with every new piece of information. This includes updates on medical conditions, changes in recovery status, the identification of additional comorbidities, and any impending legal involvement. This real-time monitoring and updating ensure that the claims handling process is responsive and adaptive to any changes or complications that might arise.
Lastly, the technology enables 24×7 monitoring of all these aspects, offering continuous oversight without the constraints of human working hours. This around-the-clock vigilance ensures that no critical changes or updates are missed, further enhancing the effectiveness and efficiency of the claims management process.
3. In the context of reinsurance and run-off operations, what challenges arise when applying AI, and how do these challenges affect their bottom line and operational efficiency?
In the context of reinsurance and run-off operations, the application of AI faces specific challenges that can impact both the bottom line and operational efficiency. One of the primary issues is the timing of available data. AI models require substantial data to train effectively, but in the early stages of any runoff engagement, data is often limited. This scarcity of data at the outset can hinder the ability of AI to provide accurate and timely insights.
Additionally, the availability of detailed data, including adjuster notes and documents, is crucial for AI systems to function optimally. Without access to this comprehensive information, the effectiveness of AI in analyzing and predicting claims outcomes is significantly reduced.
Another challenge is the nature of actuarial analysis, which is typically conducted in the aggregate. Imperfect actuarial analysis, especially when acquiring a run-off block, can lead to inaccurate predictions and negatively affect outcomes. This problem is exacerbated when there’s a lack of AI guidance that incorporates all core and supporting data, documents, and adjuster notes. Without this, claims deep in the inventory may develop unchecked, leading to unforeseen issues.
Furthermore, driving third-party administrator (TPA) performance without comprehensive AI guidance can lead to inefficiencies and oversight challenges. This results in a significant need for human resources to manage and oversee all the claims in the block, creating a scaling problem for run-off carriers. The intensive labor required for this oversight not only increases operational costs but also can impact the overall effectiveness of managing the run-off operation. These challenges underscore the need for robust, data-rich AI solutions tailored to the unique demands of reinsurance and run-off operations.
4. How does EvolutionIQ, with its advanced data analytics and AI-driven insights, enhance claims assessment and policy management for reinsurance and run-off companies?
EvolutionIQ’s advanced data analytics and AI-driven insights significantly enhance claims assessment and policy management for reinsurance and run-off companies. By utilizing AI technology, EvolutionIQ offers a nuanced approach to managing claims, ensuring that they align with the unique requirements of these companies.
One of the critical features of EvolutionIQ is its ability to apply reinsurance filters. These filters are designed to trigger notifications for claims that need to be flagged to reinsurers. This feature is crucial for maintaining transparency and compliance in the reinsurance process, ensuring that all relevant claims are appropriately managed and communicated.
Moreover, EvolutionIQ employs aggregate algorithms to analyze trends within the entire book of acquired claims. This holistic view allows companies to identify patterns and anomalies that might not be apparent at an individual claim level. By understanding these aggregate trends, reinsurance and run-off companies can make more informed decisions about their overall claims strategy.
The AI-driven oversight model provided by EvolutionIQ offers deep insights across the claims block. This comprehensive guidance allows for a more thorough understanding of each claim, facilitating better decision-making. It surfaces critical information such as comorbidities, claimant sentiment, and real-time opportunities for claims resolution. By highlighting these aspects, EvolutionIQ enables companies to address claims more effectively, considering the broader context of each case.
In summary, EvolutionIQ’s integration of AI-driven analytics and insights into the claims management process provides reinsurance and run-off companies with the tools necessary for efficient and effective claims assessment and policy management.
5. Could you elaborate on the specific data sources and technologies utilized to achieve this?
To achieve the comprehensive analysis required for effective claims management, a variety of data sources and technologies are utilized. The foundation of this approach is the inclusion of all structured data related to each claim. This encompasses a wide range of information, from basic claim details to more complex data points that offer deeper insights into each case.
Additionally, all adjuster notes are meticulously analyzed. These notes often contain critical, nuanced information that can significantly impact the direction and outcome of a claim. By incorporating these insights into the analysis, a more complete and accurate picture of each claim is formed.
Moreover, every document associated with a claim is thoroughly examined. This includes not just the standard paperwork but extends to correspondence, recorded statements, legal documents, and any other relevant materials. The thorough examination of these documents ensures that no detail, however small, is overlooked.
Lastly, to complement and augment the data gathered from the above sources, third-party data is also sourced. This external data plays a crucial role in enriching the analysis, providing additional context, and offering broader perspectives that might not be evident from internal data alone.
By combining these diverse data sources and leveraging advanced technologies, a holistic and comprehensive analysis of each claim is achieved, ensuring that the most informed and effective management decisions can be made.
6. Regarding risk mitigation and portfolio optimization, how do your Claims Guidance solutions aid reinsurance and run-off companies? Are there any case studies or examples that demonstrate these benefits?
Our Claims Guidance solutions offer significant benefits to reinsurance and run-off companies by focusing on risk mitigation and portfolio optimization. At the heart of our approach is a deep understanding of actionable claims. This understanding is crucial for identifying and prioritizing claims that require immediate attention, ensuring that resources are allocated efficiently.
One of the key features of our solution is the daily prioritization across the entire portfolio, which is continuously updated based on all new incoming data points. This dynamic approach allows for real-time adjustments in strategy, ensuring that the most critical claims are always at the forefront. By staying abreast of the latest information, our system can quickly identify trends and outliers that might otherwise go unnoticed.
A particularly important aspect for reinsurance and run-off companies is our focus on claims that have the potential to adversely impact the run-off of said claims. Our system is designed to recognize these high-risk claims early in the process, allowing for proactive management and mitigation strategies to be implemented. This early intervention is key in preventing escalations that can lead to significant financial impacts.
While specific case studies are confidential, our work with various industry leaders has consistently demonstrated the effectiveness of our solutions. Clients have observed marked improvements in claim outcomes, reduced costs, and more efficient allocation of resources, all contributing to a healthier and more stable portfolio. Our approach not only streamlines the claims management process but also provides strategic insights that are invaluable for long-term planning and risk management.
6. What kind of return on investment (ROI) can reinsurance and run-off companies anticipate from implementing your solutions? Are there notable cost-saving or revenue-generating opportunities?
Implementing our solutions offers reinsurance and run-off companies significant opportunities for return on investment (ROI), particularly through cost-saving and revenue-generating avenues. A key factor to consider is the nature of claims in portfolios managed by run-off carriers, which are predominantly long-tail. Our technology effectively reduces the duration of these claims, directly impacting the ROI positively by lowering the indemnity costs associated with the purchased block of claims.
Additionally, our solutions streamline the claims handling process, reducing the need for internal claims examiners. This not only cuts down on operational expenses but also enhances efficiency, allowing for a leaner and more focused claims management team.
Another critical aspect of our technology is its capability to flag claims that are eligible for reinsurance recovery. By accurately projecting the ultimate cost of claims, we enable run-off companies to identify potential recoveries more effectively. This feature is particularly beneficial as it helps in optimizing the financial performance of the portfolio, turning potential losses into recoverable assets.
In summary, by implementing our solutions, reinsurance and run-off companies can expect a substantial ROI through reduced claim durations, lowered operational costs, and enhanced ability to identify and recover costs through reinsurance. These strategic benefits position our solutions not just as tools for managing claims, but as integral components for financial optimization and revenue generation in the run-off and reinsurance sectors.
7. Looking forward, how do you envision the role of AI evolving in reinsurance and run-off sectors in the coming years?
In the coming years, AI’s role in the reinsurance and run-off sectors is expected to undergo a significant transformation. The evolution will likely see the run-off sector adapting to prioritize claims data analysis before making competitive offers to the seeding insurer or facility. This shift will enable a more sophisticated use of Machine Learning (ML) and Artificial Intelligence (AI) for a thorough evaluation of each claim in a claims block.
This approach involves using AI to perform a comprehensive, ground-up analysis of every individual claim. The result is an aggregated exposure assessment for the entire claims block, leading to more accurate and confident offers. With AI’s ability to provide detailed insights and a definitive strategy for each claim, the traditional method of sample file audits for quality assessment will become obsolete. Instead, AI’s comprehensive analysis will offer a more precise and efficient means of evaluating claims blocks.
This evolution will likely result in a more streamlined, accurate, and efficient process for the reinsurance and run-off sectors, significantly enhancing decision-making capabilities and operational effectiveness.
8. Are there any upcoming developments or features, such as generative AI that captures the sentiment of claimants, injured workers, and carriers, that you can share about EvolutionIQ’s future plans?
In response to your query about upcoming developments at EvolutionIQ, we’re excited to share that generative AI is a key component in our current and future plans. Currently utilized across all EIQ products, generative AI plays a pivotal role in offering precise guidance for the “Next Best Action” and ensures transparency in the reasoning behind these recommendations. Additionally, we’re leveraging generative AI in tandem with Computer Vision and data extraction techniques to assess both employee and employer sentiment.
Looking ahead, we are focusing on expanding the capabilities of generative AI in our offerings. This includes advanced features for capturing and analyzing the sentiment of various stakeholders, such as claimants, injured workers, and carriers. By doing so, we aim to deepen our understanding of their experiences and perspectives, which is crucial for enhancing the effectiveness of our solutions in claims management. This development signifies our commitment to evolving our technology in ways that align closely with the needs and expectations of our users, ensuring that our solutions remain at the forefront of innovation in the insurance industry.
