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COMP9059 - Online Fraud Analytics

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Title:Online Fraud Analytics
Long Title:Online Fraud Analytics
Module Code:COMP9059
 
Credits: 5
NFQ Level:Advanced
Field of Study: Computer Science
Valid From: Semester 1 - 2017/18 ( September 2017 )
Module Delivered in 2 programme(s)
Module Coordinator: TIM HORGAN
Module Author: Samane Abdi
Module Description: Online transactions have recently raised big concerns, with some research showing that online transaction fraud is 12 times higher than in-store fraud. Special methods of data analysis is needed to detect and prevent online fraud. These methods exist in the areas of Knowledge Discovery in Databases, Data Mining, Machine Learning and Statistics. This module explores the basic concepts of online transactions and the techniques for detection and prevention of online fraud.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Analyse online businesses payment options.
LO2 Appraise the fraud threat landscape for online businesses.
LO3 Compare and contrast the fraud detection techniques.
LO4 Employ various types of fraud prevention models.
LO5 Apply machine learning techniques to predict and prevent fraud transactions.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

Indicative Content
Online Payment Options
The payment options such as mail order and telephone order, primary factors to evaluate and select payment options, role and importance of credit cards and alternative payments in the e-commerce channel
Basics of Online Fraud
Fraud Schemes, Theft on Credit Card Numbers, Consumer-Perpetrated Fraud, Card Generator Fraud, Consumer Satisfaction Fraud, Credit and Return Fraud, Repeat Offenders, Internal Fraud, Identity Theft, Identifying the Fraudsters
Fraud Detection Techniques
Card Security Schemes, Identity Authentication, Credit Check, E-Mail Authentication, Return Customer, Geolocation, Device identification, etc.
Fraud Prevention Models
Fraud scoring, Correlation analysis, Regression analysis, Pattern recognition, Time-series analysis, Calculations of performance metrics, Rules and visual anomalies, etc.
Machine Learning and Data Mining
Supervised learning methods: Data classification to 'fraudulent' or 'non-fraudulent'; Un-supervised learning methods: Group analysis, Break point analysis, Behavior in credit card accounts, three-level-profiling; Decision tree learning
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Written Report The student is expected to profile and analyse various aspects of an online transaction to detect and predict future behavior of the fraudsters. 1,2,3,4 40.0 Week 6
Project The student is expected to apply a rule-based algorithm/machine learning algorithm to the gathered data and deploy a model with the aim of increasing accuracy in detecting and preventing fraud transactions. 4,5 60.0 Sem End
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Lecture delivering theory underpinning learning outcomes. 2.0 Every Week 2.00
Lab Lab to support learning outcomes. 2.0 Every Week 2.00
Independent Learning Independent study. 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Lecture delivering theory underpinning learning outcomes. 2.0 Every Week 2.00
Lab Lab to support learning outcomes. 2.0 Every Week 2.00
Independent Learning Independent study. 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • David A. Montague 2010, Essentials of Online Payment Security and Fraud Prevention, 1-12, John Wiley & Sons Inc. [ISBN: 9780470638798]
Supplementary Book Resources
  • Mark Nigrini 2011, Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations, 18, John Wiley & Sons Inc. [ISBN: 9780470890462]
  • Ryszad S. Michalski, Ivan Bratko, Miroslav Kubat 1998, Machine Learning and Data Mining: Methods and Applications, John Wiley & Sons Inc. [ISBN: 9780471971993]
  • Timothy Braithwaite 2002, Securing E-Business Systems: A Guide for Managers and Executives, John Wiley & Sons Inc. [ISBN: 9780471072980]
Supplementary Article/Paper Resources
  • Phua, C., Lee, V., Smith-Miles, K. and Gayler, R. 2010, A Comprehensive Survey of Data Mining-based Fraud Detection Research, Clayton School of Information Technology, Monash University, 14
  • Richard J. Bolton , David J. Hand , David J. H 2011, Unsupervised Profiling Methods for Fraud Detection, Credit Scoring and Credit Control VII
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_KINSE_9 Master of Science in Information Security 1 Elective
CR_KINSY_9 Postgraduate Diploma in Science in Information Security 1 Elective

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie