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Generative AI in insurance: Is the promise of automation just hype?

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The insurance industry is vital to the growth of the global economy, providing financial protection and stability to individuals and businesses. Underwriters play a crucial role in assessing risks, determining policy terms, and ensuring the sustainability of the industry. However, challenges like the need for human intervention in routine processes, errors, etc., continue to keep underwriters from focusing on core strategic activities. This article explores how Generative AI can revolutionize insurance claims by optimizing underwriting processes, reducing manual work, and enhancing data analysis.

Challenges in underwriting

Multiple surveys have shown that underwriters spend only about a third of their time performing risk analysis on accounts. A staggering 40% of their time is consumed by administrative activities, such as data entry and manual analysis execution. This not only hampers efficiency but also contributes to rising industry loss ratios. External factors like inflation and increased climate-related events certainly play a role in this predicament. Still, it’s essential to acknowledge that underwriters themselves report the lowest levels of confidence in the quality of the underwriting process. Additionally, the sluggish pace at which new insurance products are introduced (an average of 18 months) and existing products are modified (about 6 months) further limits the industry’s adaptability to market conditions.

Optimizing the underwriting experience

To address these challenges, the insurance industry needs a transformative approach. There are several key areas where the underwriting experience can be significantly improved:
Improving data access and analysis:
Research from McKinsey reveals that AI in the insurance space can save time and costs. It can reduce claims regulation costs by 20-30% while minimizing processing costs by 50-65% and time by 50-90%. This will ultimately improve the customer service experience.
Reducing manual data entry:
Manual data entry from unstructured sources and forms remains a major bottleneck in underwriting processes. With AI-led automation, this process can be transformed, significantly reducing the need for manual intervention or errors.
Enhancing data quality:
The “Not Yet Good Enough” cycles in data quality management can be reduced by automating the quality improvement loops. This can improve data quality and delivery insights with greater accuracy.
Streamlining workflows:
Integrating workflows and underwriting business processes can save business a lot of time and reduce errors. It can improve overall visibility for all stakeholders, improving transparency across the value chain.
Improving workbench experience:
Enhancing the front-end workbench experience for underwriters and processor roles can boost overall efficiency.

Leveraging Technology Solutions

Addressing these issues requires a combination of technologies that can modernize and streamline the underwriting process:
a. Modernization into microservices with APIs:
This approach enhances the composability of applications and business flows. It also facilitates the productization of data that underwriters can innovate on, ensuring flexibility and adaptability.
b. Integration with third-party solutions:
Partnering with best-of-breed third-party solutions can help reduce manual processes in data validation and provide richer data for analysis.
c. Induction of a generative engine:
Generative AI can revolutionize document and unstructured data extraction, reducing the need for extensive training. It can also enable virtual agents for collaboration, training, policy adherence, rules testing, and contract/document generation.
d. Application automation:
Streamlining workflows across systems and processes can remove barriers to seamless integration. Additionally, machine learning and deep learning can be harnessed for risk scoring, automated analyses, and policy recommendations.
e. Auto-Analysis of Third-Party Data:
By applying machine learning and deep learning techniques to analyze third-party data according to insurer guidelines, manual analysis processes can be minimized.

Benefits of Generative AI in Insurance Claims

The incorporation of Generative AI into the insurance industry offers numerous advantages:
Efficiency and accuracy:
Generative AI reduces manual tasks, allowing underwriters to focus on risk analysis. This, in turn, enhances the accuracy of underwriting decisions.
With a modernized and integrated system, insurers can react more swiftly to market conditions, introducing new products and modifying existing ones with greater agility.
Data enhancement:
Access to richer, high-quality data can lead to more informed underwriting decisions and better risk assessment.
Cost savings:
By automating various processes, insurers can significantly reduce operational costs and improve their bottom line.

Forging the road ahead

The insurance industry faces substantial challenges in terms of underwriting efficiency and the quality of risk analysis. These challenges not only impact profitability but also the industry’s ability to adapt to changing market conditions. Generative AI presents a solution that can optimize underwriting processes, reduce manual work, and improve data analysis. By embracing the transformative power of Generative AI, the insurance sector can enhance its competitiveness and better serve its customers. The future of insurance claims lies in harnessing the capabilities of AI to streamline underwriting, ensuring the sustainability and success of the industry for years to come

In the quest to transform the insurance industry, it’s imperative to look at innovative solutions, and HTCNXT has emerged as a pioneering force in this landscape. At HTCNXT, we are committed to revolutionizing the underwriting experience by harnessing a potent combination of AI technologies, including generative engines and deep learning models. These cutting-edge technologies serve as the foundation for a dynamic and adaptable solution that can address the challenges outlined earlier.

Our AI platform, MAGE, places a strong emphasis on modularity and plug-and-play capabilities. We understand the importance of seamlessly integrating our solutions with existing investments, whether it’s widely used platforms like Guidewire or Duckcreek, or bespoke systems unique to your organization. The headless architecture of MAGE ensures that the transition to a more efficient and agile underwriting process is both smooth and cost-effective, safeguarding your investments in current systems while boosting operational excellence.

Furthermore, we recognize the vital role that data plays in the underwriting process. MAGE helps you integrate 3rd party data sources and, more importantly, enhance the analysis of this data to cater to specific underwriting scenarios. This data enrichment capability empowers underwriters with deeper insights and richer information, ultimately improving their ability to make well-informed decisions. The result is a more agile and responsive underwriting process, capable of adapting to evolving market conditions and customer needs.

By embracing MAGE, insurance companies can embark on a transformative journey towards greater efficiency, accuracy, and adaptability in their underwriting processes. The future of insurance claims management is being shaped by the innovation and versatility that HTCNXT brings to the table, ensuring that your business remains resilient and competitive in a rapidly changing world.

To know how we can help you improve your insurance business, book a demo today!

Generative API Security and Data security

The Generative APIs are integrated with enterprise security leveraging OAuth2/OpenID connect. The APIs for synthetic data generator adheres to data privacy, compliances, and reduced model bias.
MAGE platform automation is spread over different areas, from Data Engineering(ETL) to Industry Use cases.
ETL process
MAGE Platform ETL process integrated with Language Model (LLM) assistants powered by NLP, allowing business users to query domain-specific entities and fetch data effortlessly. ETL framework enables seamless connections to multiple data sources, facilitating easy data ingestion, transformations, and visualizations.
Auto EDA framework
The MAGE Platform, Auto EDA framework, leverages NLP to provide insights into data completeness, quality, and summary. Users can visualize correlations, helping them identify and resolve data issues swiftly. The framework is integrated with MAGE platform APIs seamlessly from any channel.
Web Scraping
The MAGE platform has built automation scripts to scrape large amounts of text or data from websites, social media, or other online sources. This data can be used as training material for generative AI models.
Document Parsers
The MAGE platform has in-built automation scripts to parse large amounts of text or data from Word and Excel documents and iMAGEs with OCR capabilities. It extracts key information that can be given to Generative AI models.
MAGE platform MLOps uses different practices and tools to streamline and automate the end-to-end machine learning lifecycle, from model development to deployment and monitoring with auto. MLOps can be integrated with various automation workflows to enhance automation capabilities in the context of machine learning and data science. The MAGE platform for MLOps can automatically track and version machine learning models and associated artifacts, making it easy to roll back to previous versions if necessary. Leverages automated monitoring of model performance, data drift, and system health to trigger alerts and notifications when anomalies are detected and retrigger the training pipeline. Triggers Responsible AI for explainability, Bias reporting, Mitigation, and Data anomaly identification that has integration with various APIs for reporting and data ingestion.
Automated Testing
The MAGE platform has the capability for automation testing to compare model versions and determine which performs best in production. Uses Generative AI APIs for A/B testing for different versions of data and content.
Error Handling and Remediation
The MAGE platform has inbuilt Automation scripts that can use APIs to monitor system health and respond to issues automatically.
Infrastructure as Code (IaC):
MAGE Platform leverages IaC modules like Terraform, which uses APIs to provision and manage infrastructure resources. Infrastructure changes are defined in code and executed through API-driven automation.
Resource Scaling:
MAGE Platform uses cloud autoscaling features to add or remove instances, allocate more CPU or memory, or adjust network configurations in response to traffic changes.

Shammo Ghosh

Senior Vice President, SME Insurance







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