Gen AI advancements are redefining the world in which we live, and the insurance sector is no exception. During a recent MGAA members-only webinar, experts from Encora shared insights into how Gen AI is revolutionizing key processes like underwriting, claims handling, fraud detection, and customer service.
Current State of Adoption of AI in Insurance
Research indicates that AI is increasingly shaping the UK insurance sector, with a strong uptake, particularly among brokers. Eight in ten brokers are already using AI daily, and nearly half recognize its potential benefits while also being aware of its challenges.
Key capabilities of AI within the insurance sector include AI-powered chatbots for customer support, automated claims processing, predictive analytics for risk assessment, and personalized policy recommendations. However, there is a consensus on the need for human oversight of AI-generated results to ensure a high level of service for customers.
Despite the insurance industry being in the nascent stages of AI adoption, the positive attitude towards AI reflects its readiness to embrace technological advancements. This optimism suggests that the insurance industry is becoming one of the foremost adopters of AI, leading the way for other sectors.
The potential applications of Gen AI in insurance are extensive:
- Underwriting: Leveraging AI to analyze large datasets for more accurate risk assessment and streamlining the underwriting process.
- Quote & Bind: Using AI to draft and customize insurance policies based on individual customer needs, enhancing the customer experience.
- Claims Processing: Automating claim submission, verification, and settlement, while analyzing claims data to identify patterns and anomalies.
- Fraud Detection: Using pattern recognition and anomaly detection to identify and prevent fraudulent claims proactively.
- Customer Service: Implementing AI-driven chatbots and virtual assistants to handle customer inquiries and provide personalized advice.
Real-World Examples
Several players within the insurance industry are already harnessing the power of Gen AI, with early initiatives having been launched in the past few months. In the days ahead, we can expect to see more experiments and bake-off endeavours mature andsee the light of production environments. Some recent examples of Gen AI in action within the insurance industry include:
- Miller Insurance Services launched MillerMo, an employee AI tool using Microsoft and OpenAI technologies.
- Clearcover introduced a generative AI tool to expedite claims processing and elevate the customer experience.
- Munich Re unveiled REALYTIX ZERO Co-Pilot, an AI assistant translating need-specific requests into detailed product proposals.
Ethical Considerations & Challenges
As insurers embrace generative AI, they must navigate ethical considerations and challenges:
- Addressing AI bias and ensuring fairness in decision-making processes: Encora acknowledges the challenge of precisely determining insurance premiums. Errors in premium calculations can rapidly erode customer satisfaction and, in the worst cases, when there is an absence of clear evidence to substantiate pricing decisions, escalate into legal disputes. We believe that while data scientists may employ models, they often lack the nuanced understanding of insurance models possessed by industry experts.
- Safeguarding data privacy and security, especially sensitive insurance data: Encora has announced a strategic partnership with Protecto.ai, a leader in data privacy and security across the Gen AI project lifecycle. Through this alliance, Encora integrates the advanced APIs of Protecto.ai into its robust suite of AI solutions. The APIs act as a digital shield, ensuring the highest levels of data security and privacy compliance.
- Complying with evolving AI regulations in the insurance sector: Encora emphasiszes the need for close collaboration among software developers, legal and compliance teams, and the need to foster robust relationships with insurance departments. This collaborative synergy ensures that AI-driven innovations meet regulatory prerequisites and elevate the overall customer experience.
- Minimizing AI “hallucinations” or inaccurate, misleading outputs: Encora has demonstrated that the disciplined application of Generative AI tools across well-structured, real-life scenarios can shorten innovation cycles, and improve product market fit, while simultaneously strengthening code quality. They believe that significant performance gains may be achieved across each stage of the software development cycle.
The Vision: Gen AI Insurance Copilots
Encora envisions a future where "Insurance Copilots," powered by generative AI, augment various stages of the insurance lifecycle:
- Quote Copilot & Underwriter Copilot for the quote-to-bind journey
- FNOL Copilot & Claims Handler Copilot for the claims journey
By evolving from static forms to dynamic, AI-guided conversations, insurers can deliver more personalized, efficient experiences. Here’s an example on how AI reshapes a given insurance process (Claims).
|
Claims Process Stage |
Traditional Process |
Gen AI-Enhanced Process |
|
1. Claim Initiation |
Customer contacts the insurance company to report a claim. This could be via phone, email, or in person. |
Customer reports a claim through an AI-powered chatbot or voice assistant, which is available 24/7. The AI can understand the nature of the claim from the customer’s natural language input. |
|
2. Claim Assessment |
A human adjuster reviews the claim, which may involve contacting the customer for more information, reviewing documents, and possibly visiting the site of the incident. |
An AI system automatically reviews the claim using machine learning algorithms. It can analyze photos of the incident, review policy documents, and even predict the likelihood of fraud. |
|
3. Decision Making |
The adjuster makes a decision based on the assessment and company policies. This can be a time-consuming process. |
The AI system can decide instantly based on its assessment. It can also clarify its decision using explainable AI techniques. |
|
4. Communication |
The decision and next steps are communicated to the customer. This could take some time if the adjuster is handling multiple claims. |
The AI system instantly communicates the decision to the customer, along with clear next steps. It can also answer any questions the customer might have. |
|
5. Claim Settlement |
The claim is settled manually, which can involve issuing a cheque or arranging for repairs. |
The AI system can automate the settlement process, such as by issuing an electronic payment or scheduling repairs with a trusted provider. |
The Way Forward
As the insurance industry stands at the threshold of an AI-driven transformation, forward-thinking insurers are already piloting generative AI projects to harness its potential. Strategic planning and value-driven execution will be key to successfully navigating this exciting new frontier. With the right approach and expert guidance, generative AI promises to redefine insurance as we know it.
References
- Generative AI: Paving the Path to Democratized Coding and Transformation in Insurance and Healthcare (encora.com)
- Encora Strengthens AI-Driven Data Security Capabilities...
- Encora Launches New Generative AI Technology Practi...
- How AI and ML are changing the insurance industry for the better - Excellarate (encora.com)
- 10 ways AI is transforming the insurance industry (encora.com)
- The New Age of Insurance: Why your Business Needs to Embrace Big Data Analytics - Excellarate (encora.com)
- Generative AI Application for Better Productivity | Encora
- https://www.miller-insurance.com/news-and-insights/latest-news/miller-launches-millermo
- https://www.insurance-canada.ca/2024/03/25/clearcover-launch-generative-ai-claims-tool/
- https://www.reinsurancene.ws/munich-re-launches-generative-ai-realytix-zero-co-pilot/
