A Discovery Workshop by Visionet
Tariq Khan is Chief Data Scientist at Visionet. His current role includes AI transformation strategy for enterprise, process translation to AI and establishing Gen AI CoE, delivery governance of products related to Gen AI, envisioning and enabling ROI of AI models. He has been working on embedding principles of fairness, accountability, transparency, explainability and mitigation of harms into the data science ways of working.
Insurance claims, customer outreach and underwriting require detailed analysis of unstructured documents to make case-by-case decisions; these decisions require knowledge-workers to spend time reading documents for inconsistencies, and endorsements, and verifying checklists of artifacts. These tasks although cumbersome for humans are very well suited to how Gen AI solves problems. This workshop provides a walkthrough of Visionet’s experience that we gained from the implementation of InsureGen, which is Visionet’s platform for Gen AI in insurance.
You will also learn about our experience on how to find opportunity areas for Gen AI use cases with high ROI in claims, underwriting, and customer outreach. There is a detailed section in this workshop about the Responsible AI Fabric on which our platform is built and how it guarantees regulatory compliance and responsible AI principles.
Welcome to ‘Strategizing for High ROI GenAI Use Cases in Insurance Process Flows while Complying with Responsible AI Practices’.
Join us for a day of discovery, collaboration, and strategic planning that will position your organization for success in the Gen AI era.
The recent history of Gen AI and its application in general intelligence. | |
Overview of Gen AI’s impact on the insurance industry and its potential to transform traditional processes. |
Strategies to discover processes within insurance that can benefit significantly from Gen AI intervention. | |
Examination of process areas inherently suited for Gen AI, such as claim submissions, which are heavy on image and text processing. |
Understanding and addressing regulatory constraints to identify viable Gen AI use cases. | |
Analysing infrastructural and cost considerations to filter out prohibitive use cases. |
Aligning Gen AI skills with tasks requiring high-value human decision-making. | |
Identifying and elaborating on the most impactful Gen AI applications in claims processing, underwriting, and marketing. |
Detailed discussion on the Responsible AI Fabric that underpins Visionet’s platform, focusing on regulatory compliance and adherence to responsible AI principles. | |
Strategies for implementing privacy-preserving and trustworthy Gen AI solutions in insurance. |
Approaches for continuous monitoring and ROI tracking of Gen AI initiatives in insurance. | |
Identifying metrics and methodologies for assessing the success and impact of Gen AI implementations. |
Breakout sessions tailored for audience-specific use case ideation and discussion. | |
Facilitated collaboration and strategic planning to align Gen AI initiatives with organizational goals. |
Summarizing key takeaways and strategies for moving forward with Gen AI in insurance. | |
Guidance on how organizations can prepare for and implement these insights to achieve high ROI and compliance with responsible AI practices. |
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