This PDF document describes the course, Developing Technology Foresight: Case Study of AI in InsurTech, and includes learning outcomes and a course outline

McShane, Michael

Pinto, C. Ariel

English

Tahami, Hesamoddin

Fakhravar, Hengameh

Old Dominion University

Old Dominion University ODU Digital Commons Developing Technology Foresight: Case Study of AI in InsurTech Open Course Materials 2022 Introduction to the Course Michael McShane Old Dominion University C. Ariel Pinto Old Dominion University Follow this and additional works at: https://digitalcommons.odu.edu/oer_developingtechnologyforesight  Part of the Artificial Intelligence and Robotics Commons, and the Insurance Commons Repository Citation McShane, Michael and Pinto, C. Ariel, "Introduction to the Course" (2022). Developing Technology Foresight: Case Study of AI in InsurTech. 4. https://digitalcommons.odu.edu/oer_developingtechnologyforesight/4 This Introduction is brought to you for free and open access by the Open Course Materials at ODU Digital Commons. It has been accepted for inclusion in Developing Technology Foresight: Case Study of AI in InsurTech by an authorized administrator of ODU Digital Commons. For more information, please contact digitalcommons@odu.edu. An Advanced Course Development Proposal by Old Dominion University for the Spencer Educational Foundation, Inc., 2020  Page | 1   Developing Technology Foresight:  Case Study of Artificial Intelligence (AI) in InsurTech  Description: This course helps students gain competencies in forecasting and application of technologies - such as AI - in the modeling, analysis, and management of risk, as a preparation to becoming future insurance industry leaders. The course covers introduction to InsurTech, Artificial Intelligence (AI), and Technology Foresight. Length of Course: 3 x 1.5 hours (approximate) Level of Instruction:  ● Appropriate for both undergraduate and graduate-level programs with risk assessment and management components. ● Graduate-level case studies, projects, and readings are included  Learning Setting: This course is suitable with minimal modifications for in-person, online, or hybrid modes of delivery, compatible with most LMS. Course Learning Outcomes:  Upon completion of this course, students will be able to: • Identifying emerging technologies and how they may address the needs of the Insurance Industry  • Explain key concepts of AI, the Insurance Industry, and summarize current and future roles of AI in InsurTech • Analyze, simplify, and associate various applications of AI to current needs of the Insurance Industry Module 1 Learning Outcomes:  Upon completion of Module 1, students will be able to explain key concepts of AI, the Insurance Industry, and summarize current and future roles of AI in InsurTech. In particular: ● Importance of Technology Foresight in the Insurance Industry ● The definition of AI and ML, their concepts and application area ● State-of-art in InsurTech and current problems Module 2 Learning Outcomes:  Upon completion of Module 2, students will be able to analyze, simplify, and associate various applications of AI to the current needs of the Insurance Industry. In particular: An Advanced Course Development Proposal by Old Dominion University for the Spencer Educational Foundation, Inc., 2020  Page | 2   ● AI in Customer Service. Usage-based insurance, proactive analytics, chat-bots. ● AI in Marketing. Classification, summarization, template generation, mix-and-match policy, insurance advice. ● AI in Underwriting. The risk score, instant insights, and aids for the underwriter. ● AI in Fraud Detection and Claim Processing. Fraud detection, information validation, damage assessment, claims processing. Module 3 Learning Outcomes:  Upon completion of Module 3, students will be able to identify emerging technologies to address the needs of the Insurance Industry. In particular: ● Identify current needs of InsurTech in the modeling, analysis, and management of risk ● Map functionalities of various emerging technologies to these current needs of InsurTech Homework: To be determined Module Exercises: To be determined Learning Materials: To be determined Technical Specifications: To be determined Grading: To be determined Course Outline:  Module 1. Introduction to Technology Foresight, Risk Management, and InsurTech 1.1. Fundamentals of Technology Foresight • Introduction to principles of Technology Foresight • Common applications of Technology Foresight 1.2. Fundamentals of AI • Introduction to Artificial Intelligence • Introducing Machine Learning • Natural Language Processing (NLP) overview 1.3. Introduction to InsurTech • Current challenges in the insurance industry  • Origin and current state of InsurTech An Advanced Course Development Proposal by Old Dominion University for the Spencer Educational Foundation, Inc., 2020  Page | 3   Module 2. Case studies of AI and InsurTech 2.1. AI in Customer Service • Usage-based insurance • Proactive analytics 2.2. AI in Marketing • Classification, summarization, template generation. • Mix-and-match policy • Insurance advice 2.3. AI in Underwriting • AI and risk score • AI and the underwriter 2.4. AI in Fraud Detection and Claim Processing • Fraud detection • Information validation • Damage assessment • Claims processing Module 3. Technology Foresight and InsurTech 3.1. Technology gaps & trends in InsurTech • IoT in Insurance industry • Blockchain in Insurance industry • Cloud Computing in Insurance industry 3.2. InsurTech Case Studies  

Introduction to the Course

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Introduction to the Course

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