ICM517: Insurance and Big Data

Module code: ICM517

Module provider: ICMA Centre; Henley Business School

Credits: 20

Level: 7

When you'll be taught: Semester 2

Module convenor: Dr Michalis Ioannides, email: michalis.ioannides@icmacentre.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: No

Last updated: 19 November 2024

Overview

Module aims and purpose

The availability of unprecedented amounts of data made available by ever increasing computing power and data storage capacity, widespread use of Internet of Things devices, and powerful communications networks, have led to major changes in the insurance industry. The services it provides, how they are offered to its customers and at what price, and insurance risk analysis are undergoing rapid innovations. In this module you will learn how the evolution of the sector is unfolding and gain insights to the challenges and opportunities that it is generating.Ìý

Module learning outcomes

By the end of the module, it is expected that students will be able to:Ìý

• Describe the principles of insurable risks and the legal basis for insurance contracts and; illustrate the main types of insurance and the business model used by insurers.Ìý
• Explain how the insurance industry is evolving and the new products, services and business models brought about by current technological changes (InsurTech trends and proposals) and; explain how Internet of Things (IoT), cloud technologies and big data are used in the insurance industry and their applications in insurance risk analysis, and pricing.ÌýÌý
• Describe and apply selected insurance models used in non-life, life insurance, asset and liability management, and the calculation of solvency capital requirements; and illustrate the regulatory environment and new ethical challenges resulting from the availability and use of big data in the insurance marketÌý
• Explain cyber risk and describe the insurance products that provide cover for such risksÌý

Module content

1. Introduction to insuranceÌý

1. Insurance and risk transferÌý

2. Products and business strategyÌý

3. Regulatory framework and principles of insuranceÌý

4. Process of underwriting and reinsuring insurance and financial risksÌýÌý

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2. The use of big data and Internet of Things in the insurance industryÌý

1. Risk methods and analysisÌý

2. Diffusion of next generation of digital technologies in the insurance industryÌý

3. Pricing of individual policyholder riskÌýÌýÌý

4. Internet of Things applicationsÌý

5. ÌýCloud technology and its use in insurance industryÌý

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3. Big data and use of models in insuranceÌýÌý

1. Machine learning in insuranceÌý

1. Generalised Linear Models (GLMs)Ìý

2. Applications of GLMs in pricing motor insurance premiumsÌý

1. Applications using big data recovered from actual insurance portfoliosÌý

2. Use and benefits of telematics in motor insurance: A case studyÌý

3. Looking through the Machine Learning (ML) lense – Theory and examplesÌýÌýÌý

2. Life insurance, health insurance and pensions: life expectancy predictions using traditional econometric models and new ML algorthimsÌý

3. Property insurance: cost and likelihood of flood damage, predicting subsidence, fire claims, hail storms, hurricane damageÌý

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4. Cyber Risk in insuranceÌý - Insurers in some jurisdictions are reporting an increasing number of malware and other cyber attempts.ÌýÌý

1. Learn more about the main consequences suffered by insurers following these cyber incidents and impact on business, policyholders and third parties.Ìý

2. Become familiar with the cyber-underwriting market and associated productsÌý

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5. Regulatory and accounting standards (asset liability management and capital requirements) and ethical challengesÌý

1. Become familiar with different regulatory and accounting regimesÌý

2. New products and venues opened with InsurtechÌýÌý

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6. The new insurance industry landscape: Case studiesÌý

1. The use of big data and cloud technologies in the calculation of solvency capital requirementsÌý

2. Matching adjustment and bulk annuity portfoliosÌýÌý

3. Application of portfolio theory and other option pricing techniques in insuranceÌýÌý

4. The use of equity release mortgage portfolio in life insuranceÌý

Structure

Teaching and learning methods

Lectures are used to introduce the concepts covered in this module.ÌýÌý

Seminars allow students to discuss the topics in more detail, consider applications, and receive feedback.Ìý

Study hours

At least 30 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

50% weighted average mark

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Reassessment

Additional costs