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: 2020/1

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.Ìý

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 models (7) InsurTech and the evolving industry landscape.Ìý

Assessable learning outcomes:

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

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  Understand the principles of insurable risks and the legal basis for insurance contractsÌý

  
  

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  Be familiar with the main types of insurance and the business model used by insurersÌý

  
  


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  Understand how the insurance industry is evolvin g and the new products, services and business models brought about by current technological changesÌý

  
  


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  Understand how big data are being used in the insurance industry and the applications in insurance risk analysis and pricingÌýÌý

  
  


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  Be familiar with selected insurance models in life insurance, asset and liability management, and the calculation of solvency capital requirementsÌýÌýÌý

  
  
  
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  Be aware of the regulatory environment and new ethical challenges resulting from the availability and use of big data in the insurance market.Ìý

  
  

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 underwrit ing 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 ins urance: cost and likelihood of flood damage, predicting subsidence, fire claims, hail storms, hurricane damageÌý

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4.Asset liability management and capital requirements, regulatory standards and ethical challengesÌý

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5.Th e 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:

Summative assessment- Coursework and in-class tests:

Students will be asked to complete an individual report (50%) to be submitted in week 2 of the summer term, and an in class multiple choice tests (50%) in week 1 of the summer term.

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:

By individual report, to be submitted in August/September

Additional Costs (specified where applicable):

Last updated: 4 April 2020