Fraud Detection

Code:

CC4036

Acronym:

CC4036

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2021/2022 - 2S

Active?	Yes
Web Page:	http://www.dcc.fc.up.pt/~nmoniz/df2022
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master in Information Security

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:SI	15	Study plan since 2020/2021	1	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

The objectives of this course are the study of data analysis methodologies that are useful in the context of the detection/forecasting of fraudulent cases. With the growing use of data collection methods in practically all human activities, the need for the use of techniques allowing the automatic analysis of such data with the objective of detection/predicting situations that could be considered anomalous or potentially fraudulent is increasing.

Learning outcomes and competences

It is intended that the students:

1. Acquire theoretical knowledge on data analysis methodologies that are useful for the detection and prediction of fraud/anomalies;


2. Acquire practical experiência in developing and using software for the detection and prediction of fraud/anomalies;


3. Acquire expertise in fraud detection through the analysis of practical case studies on this type of problems.

Working method

Presencial

Program

Week 1
- Presentation
- Introduction to Data Mining
- Introduction to R
- Basic Concepts in R (1/2)

Week 2 
- Basic Concepts in R (2/2)
- Reporting in R

Week 3 
- Data Import in R
- Data Pre-Processing
- Data Summarization

Week 4 
- Data Visualization

Week 5 
- Introduction to Predictive Modelling
- Evaluation Metrics
- Tree-Based Models
- Naïve Bayes

Week 6 
- k-Nearest Neighbours
- Support Vector Machines
- Clustering

Week 7 
- Ensemble Learning

Week 8 
- Evaluation Methodologies
- Performance Estimation

Week 9 
Spring Break

Week 10
1st Test

Week 11
- Outlier Detection

Week 12
- Handling Big Data

Week 13
Student Week

Week 14
- Handling Imbalanced Domains (1/3)

Week 15
- Handling Imbalanced Domains (2/3)

Week 16
- Handling Imbalanced Domains (3/3)

Mandatory literature

Barnett Vic; Outliers in statistical data. ISBN: 0-471-99599-1
Torgo Luís; Data Mining with R. ISBN: 9781439810187 hbk

Complementary Bibliography

Han,J.; Kamber,M and Pei,J.; Data Mining: concepts and techniques (3rd edition)

Teaching methods and learning activities

Classes will combine theory and practice, with exposition of theory complemented with practical exercices on the computer.

Software

R statistical software

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Teste	40,00
Trabalho prático ou de projeto	60,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de projeto	0,00
Estudo autónomo	0,00
Frequência das aulas	0,00
Total:	0,00

Eligibility for exams

It is required that you obtain a minimum score of 7 in the theoretical test.

Calculation formula of final grade

Final classification is given by the following formula:

NF = 0.4 * NT + 0.6 * NP

where, NT is the grade of the theoretical test, and NP is given by the weighted average of the two practical assignments as: NP = 0.4*TP1 + 0.6*TP2, where TP1 is the grade of the first practical assignment and TP2 is the grade of the second.

Observations

Evaluation Committee:
- Nuno Moniz
- Rita P. Ribeiro