Leverage AI, Big Data, and RAG to optimize pricing, claims management, and customer experience
Artificial intelligence (AI) is transforming many industries, and insurance is no exception. Participants will explore how AI and big data can be used to improve risk analysis, claims management, and customer relations. They will also discover RAG (Retrieval-Augmented Generation) technology, an innovative approach that combines information retrieval and text generation to improve the relevance and accuracy of responses provided by a language model.
Is it for you ?
Insurance professionals
Prerequisites
• 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.
What You'll Walk Away With
- ✓ Understand AI, Big Data, and RAG fundamentals in an insurance context
- ✓ Analyze and model risk using scoring, segmentation, and predictive techniques
- ✓ Detect fraud and anomalies using machine learning tailored to insurance data
- ✓ Automate claims management and customer interactions with intelligent assistants and chatbots
- ✓ Design an AI solution integrating RAG to improve information retrieval and decision-making
Training content
1 Fundamentals
- 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
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.)
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
📌 Practical information
Our training sessions are offered in Montreal or Quebec City, in person or in a virtual classroom. Dates and locations are specified when you select your session below. If you have any questions, check out our FAQ.
