ICM319-Insurance and Big Data

Module Provider: ICMA Centre
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Dr Michalis Ioannides
Email: michalis.ioannides@icmacentre.ac.uk

Type of module:

Summary module description:

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.

Aims:

The module focuses on (1) insurable risks and legal principles of insurance (2) types of insurance (3) insurers’ business model and intermediaries (4) big data applications in insurance risk analysis, and pricing (5) Insurance regulation and new ethical considerations (6) big data and insurance forecasting models and (7) InsurTech and the evolving industry landscape.

Assessable learning outcomes:

By the end of the module it is expected that students will:

• Describe the principles of insurable risks and the legal basis for insurance contracts


• Explain the main types of insurance and the business model used by insurers


• Discuss how the insurance industry is evolving and the new products, services and business models brought about by current technological changes


• Explain how big data is being used in the insurance industry and its applications in insurance risk analysis, and pricingÌý


• Discuss selected insurance models used in life insurance, asset and liability management, and apply the calculation of solvency capital requirementsÌýÌý


• Critically discuss the regulatory environment and new ethical challenges resulting from the availability and use of big data in the insurance market

Additional outcomes:

The module will use in-class case studies showing actual applications of big data in the insurance industry.Ìý

Outline content:

1.Introduction to insuranceÌý

a.Insurance and risk transferÌý

b.Products and business strategyÌý

c.Regulatory framework and principles of insuranceÌý

d.Process of underwriting and reinsuring insurance and financial risksÌý

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2.The use of big data in the insurance industryÌý

a.Risk methods and analysisÌý

b.Pricing of individual policyholder riskÌýÌýÌý

c.Internet of Things applicationsÌý

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3. Big data and use of models in insuranceÌýÌý

a. Life insurance, health insurance and pensions: life expectancy predictionsÌý

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

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4. Regulatory standards (asset liability management and capital requirements) and ethical challenges

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5.The new insurance industry landscape: Case studiesÌý

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Global context:

The material covered in this module discusses current developments in the insurance industry worldwide.Ìý

Brief description of teaching and learning methods:

The core theory and concepts will be presented during lectures. Problem sets will be solved in workshops.Ìý

Summative assessment- Examinations:

One final exam split into two parts, one part consisting of multiple choice questions and one part consisting of essay-based questions.Ìý

The length of the exam is 1hr 45m.

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Summative assessment- Coursework and in-class tests:

Formative assessment methods:

Seminar questions are assigned for each class. The seminar leader will facilitate discussion and offer feedback.

Penalties for late submission:

Penalties for late submission on this module are in accordance with the University policy.

Please refer to page 5 of the Postgraduate Guide to Assessment for further information:

http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:

50% weighted average mark

Reassessment arrangements:

Resit by exam during the Universtiy resit period in August/September.

Additional Costs (specified where applicable):

Last updated: 22 September 2022