Despite this, a major challenge remains: persuading insurers and agents to trust this powerful new technology.
Insurtechs, positioned at the intersection of innovation and tradition, have a critical role to play in fostering a collaborative GenAI future, but all of this must be rooted in trust and transparency.
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Insurtechs need to have honest conversations about the risks of delaying GenAI adoption. While legacy systems are familiar and deeply ingrained, they are also costly to maintain and stifle innovation.
Imagine a scenario where underwriters spend countless hours sifting through mountains of data to assess risk, only to be overtaken by competitors using AI-powered systems that produce nuanced risk profiles in a few moments. This is not a distant future but the current reality facing the insurance industry.
Why should insurers abandon reliable systems for the uncharted territory of AI?
The answer lies in the inherent limitations of legacy systems. Maintaining these systems continually drains resources, and security vulnerabilities are increasingly problematic. Legacy systems are becoming increasingly difficult and costly to secure. More recently, cloud-native and digital AI solutions can offer a future-proof solution, seamlessly integrating with existing infrastructures, overcoming the limitations of legacy systems and ushering in a new era of efficiency and security.
By vividly illustrating these risks and presenting a compelling vision of the benefits of AI, insurtechs can motivate early adoption and prevent insurers from falling behind.
Cultivating trust
Building trust requires more than just highlighting potential benefits. Insurtechs must demonstrate an unwavering commitment to strong security, strict compliance, and transparent communication.
Insurtechs must be upfront about their capabilities and limitations, highlighting their expertise through a proven track record of AI innovation and ethical conduct. This approach fosters a partnership mindset, ensuring insurers are not ceding control to an opaque system.
As AI integration becomes more widespread, it is critical to prioritize compliance, data privacy, and bias reduction, supported by strong governance structures that ensure AI operates in a manner that respects ethical standards. Alignment is key here: ensuring that the goals and behaviors of an AI system are aligned with human values and intentions. As AI systems become increasingly sophisticated and autonomous, it becomes imperative to ensure that their actions remain beneficial to humans.
Misalignment can occur when AI systems optimize for goals that, while aligned with their programmed goals, may diverge from the nuanced and complex ethical standards that govern human decision-making. For example, an AI designed to maximize efficiency in claims processing might inadvertently reject valid claims to reduce costs, thereby neglecting the ethical obligation to treat policyholders fairly.
Continuous monitoring and auditing are essential to ensure that AI behaviors remain consistent with human values and decisions over time, especially when these systems are faced with new and unforeseen situations. This ongoing monitoring can help mitigate risks such as bias, discrimination, and unintended adverse consequences by ensuring that AI systems act predictably and in a manner consistent with the broader goals of their human operators. Insurers should develop actionable processes that connect AI with ethical and regulatory guidelines.
Huge potential benefits
The benefits of AI are manifold and translate into tangible benefits for insurers, MGAs, and agents. One of the key drivers is cost reduction. Automating tedious and error-prone tasks like data entry and claims processing frees up valuable human resources for higher-value activities. Imagine an AI-powered system like ChatGPT that assists underwriters by locating and summarizing critical documents, identifying key information, and streamlining workflows. This not only reduces processing time, but also allows experienced underwriters to focus on complex cases, leveraging their expertise where it matters most.
GenAI also dramatically improves the accuracy of risk assessment, leading to better pricing and reduced fraud. AI-powered analytics outperforms traditional methods, interpreting data with exceptional accuracy and creating detailed risk profiles. This leads to more informed decisions, faster policy issuance, and increased customer satisfaction. Carriers, MGAs, and large agencies across the industry have already adopted AI to automate repetitive aspects of their workload and reprioritize that time into more meaningful functions. Additionally, AI creates efficiencies in claims processing, fraud detection, customer service, and reducing expense ratios.
Another crucial advantage of GenAI is the transparency it provides, especially when using methods such as recovery augmented generation (RAG). This helps clarify the reasoning behind its decisions, unlike traditional opaque models. These capabilities minimize inaccuracies and build trust in AI-generated documents. In underwriting, RAG can ensure accurate policyholder information and regulatory compliance, building trust between insurers and regulators.
Strategic steps
To successfully harness the power of this innovative technology, it is imperative to collaborate with credible AI vendors in the insurtech sector. Insurers should look for partners with proven track records and success in AI innovation, and who have scalable solutions that can grow with their business portfolio. Developing an ecosystem of trusted collaborators and partnerships maximizes operations and delivers real, tangible results.
Successful integration of GenAI should be driven by identifying use cases and pilot projects. A phased deployment strategy effectively manages risks, starting with pilot programs and gradually expanding the use of AI tools. Building a robust cloud infrastructure ensures scalable computing power and storage, making it easier to process large data sets.
AI performance measurement is critical to ensuring the accuracy and fairness of decisions. Implementing relevant underwriting KPIs enables effective model calibration, aligning AI models with business objectives and industry standards. Continuous improvement enables underwriting departments to leverage AI’s transformative potential responsibly and effectively.
Leandro DalleMule is Global Head of Insurance and Managing Director of Planckan AI-driven risk research and data solutions company for commercial insurers and producers and a subsidiary of Allied systemsThe opinions expressed here are those of the author.
