Objectives of the training
Acquire a thorough understanding of the fundamental concepts of AI and its specific applications in the insurance industry.Targeted audience
Insurance professionalsPrerequisite
• A basic understanding of insurance concepts and business processes. • A basic understanding of artificial intelligence and machine learning concepts. • Experience working with data and familiarity with data management tools.Trainers
Benefits for Participants
• Explore the role of Big Data and AI in risk analysis, claims management, and customer relations.
• Discover RAG (Retrieval-Augmented Generation) technology, an artificial intelligence approach that combines information retrieval and text generation to improve the relevance and accuracy of responses provided by a language model and its impact on insurance process automation.
• Learn how to use AI algorithms for risk assessment and fraud detection.
• Identify the ethical and regulatory challenges associated with AI and RAG models in insurance.
• Design an AI application with RAG to meet the needs of the insurance industry.
Course architecture
Fundamentals of AI and Big Data in Insurance
1- Fundamentals of AI and Big Data in Insurance:
⦁ Definition and history of AI
• Different types of algorithms (machine learning, deep learning, NLP, RAG)
• Difference between AI, automation, and process robotization
• Concrete case studies of AI applications in insurance:
• Dynamic pricing
• Fraud detection
• Intelligent customer service
2- Big Data and AI in insurance
• Definition of Big Data and its role in insurance
• The 5Vs of Big Data (Volume, Variety, Velocity, Veracity, Value)
• Structuring and managing customer and claims data
• Tools and infrastructure needed to leverage AI and Big Data
Risk modeling, claims management, and introduction to RAG
3- Risk modeling and analysis using AI
• Scoring algorithms and customer segmentation
• Predictive analytics for risk assessment and contract underwriting
• Optimizing insurance pricing using AI
• Fraud and anomaly detection using machine learning
4-Introduction to RAG technology and its applications in insurance
What is RAG technology?
• Principle of retrieval and augmented generation
• Difference between RAG and traditional AI models
Why is RAG relevant to insurance?
• Searching for and generating accurate answers about contracts
• Intelligent customer support
• Detecting inconsistencies in claims reports
• Examples of tools and frameworks (Haystack, LangChain, etc.)
Automation with AI & RAG, regulation, and hands-on workshop
5- Automation of insurance processes with AI and RAG
• Automation of claims management
• Use of intelligent chatbots and virtual assistants
• Integration of AI and RAG solutions into insurers' workflows
• Examples of implementation with AI APIs
6- Regulation, ethics, and challenges of AI & RAG in insurance
• Current regulations (law 25, AI Act)
• Ethical issues and algorithmic bias
• Transparency and explainability of decisions made by AI and RAG
• Impacts on insurance professions and skills
7- Hands-on workshop: Developing an AI & RAG solution for insurance
• Defining business needs and choosing data
• Designing an AI model and integrating a RAG module
• Implementing a RAG chatbot for claims management
• Presentation and discussion of the solutions developed
Pedagogical details
Training architecture
• Alternating between theory (60%) and practical application (40%) • Real-life case studies from the insurance sector • Simulations and testing of AI & RAG tools • Group work on an applied project
Type of training
Private or personalized training
If you have more than 8 people to sign up for a particular course, it can be delivered as a private session right at your offices. Contact us for more details.
Request a quotePrivate or personalized training
If you have more than 8 people to sign up for a particular course, it can be delivered as a private session right at your offices. Contact us for more details.
Request a quote