FITE7410代做、代写R编程语言

日期：2023-12-03 07:59

THE UNIVERSITY OF HONG KONG

DEPARTMENT OF COMPUTER SCIENCE

FITE7410 Financial Fraud Analytics

First Semester, 2023-2024

Mini Case Study: Real-life Fraud Detection Scenario

(Due Date: 4 Dec, 2023 (Mon) 23:59)

(1) Learning Objectives

a. Analyze a real-world dataset to promote fraud analytics thinking.

b. Identify which explanatory variables may be good predictors or red flags associated with

fraud.

c. Work through the stages in model building and validation.

d. Apply the built model to classify a case based on the predicted risk of fraud.

e. Make a scenario-based decision informed by data analyses.

(2) Instructions

You are provided with a real-world dataset (ENRON case) containing fraud transaction

information. Your task is to analyze the dataset and develop a fraud detection model using

machine learning techniques. Follow the steps below to complete the assignment:

a) Define the scope and objective of the case study.

b) Exploratory Data Analysis:

? Explore the dataset to understand its structure, features, and statistical properties.

? Perform exploratory data analysis techniques, such as data visualization and

statistical analysis, to gain insights into the relationships between variables and

fraud.

? Conduct a thorough analysis of the dataset to identify which explanatory variables

are good predictors or red flags associated with fraud.

? Perform data cleaning and preprocessing as necessary.

c) Model Building and Validation:

? Select appropriate at least TWO machine learning algorithms or any appropriate

data analytics techniques (e.g. social network analysis, statistics analysis) for fraud

detection.

? Split the dataset into training and testing sets.

2

? Develop a fraud detection model using the chosen algorithm(s) and train it on the

training set.

? Evaluate the performance of the model using appropriate evaluation metrics.

? Iterate on the model building process, adjusting hyperparameters or trying different

algorithms, to improve the model's performance.

d) Fraud Scenario Identification:

? Develop a scenario related to financial fraud detection, such as a suspicious

transaction or a potential fraudulent activity.

? Use the trained model and the available data to make a data-informed decision

regarding the given scenario.

? Justify your decision based on the insights gained from the data analysis and the

model's predictions.

e) Non-data analytic element:

? What are the risks and red flags of the case, with the objective to prevent similar

financial frauds in future?

? What are the other non-data analytic elements that should be considered (e.g.

corporate governance and controls)?

? Do you have any suggestions on how to prevent similar financial fraud in future?

(3) Submission Guidelines

1. Report

Prepare a comprehensive report, documenting each step of your analysis, including

explanations, visualizations, and any insights gained. Include the results of model

evaluation and performance metrics. Present your scenario-based decision and provide

a clear rationale for your choice.

The report is max 8 pages long (not including Appendix) and should contain:

? Your name and student ID

? Title of the project

? Background and objectives of the case study

? Description of the dataset and the fraud data analytics method

? Describe and interpret the result of the new fraud detection model

? Summary and recommendation

? Cite any references (such as websites, book chapters, articles, etc) you may have

used

2. Program

Submit your R program on moodle.