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Predicting Fraud Behaviour: A Data Mining Approach for Anti-Money Laundering
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Resumo(s)
The present dissertation evaluates the importance of using data mining techniques to prevent and
detect cases of financial fraud. The most common examples of financial fraud are money laundering,
credit card fraud, financial statement fraud, insurance fraud and securities and commodities fraud. A
business must prevent and detect fraud behaviour in real time to avoid money losses, fines from the
regulator, and exposure to financial and operational risk. Being a Bank or an Insurance, it is important
to use data mining techniques to detect and prevent fraud behaviour. This study's main objective is to
build a predictive model using a data mining approach and machine learning to predict money
laundering in the banking sector using transaction data. The supervised learning algorithms applied to
predict money laundering transactions are Logistic Regression, Neural Networks, Decision Trees,
Random Forests, Light Gradient Boost and Ensemble. The dataset used in this study was highly
imbalanced, and it is necessary to apply an oversampling technique that combines K-means clustering
with SMOTE. The empirical results show that the Light Gradient Boost is the model with the best
performance, showing a strong discriminatory power (AUC=99,9% and Gini=0,998), a strong precision
(98,4%) and recall (96,4%). It achieved the highest value of f1-score (97,4%), showing that the model
correctly identifies a high number of fraudulent transactions while minimizing the false positives and
false negatives. This study proves that by monitoring and analyzing transaction data, fraudulent
transactions can be predicted with high levels of success achieved. It also presents a strong evidence
that data mining techniques can continuously be used to detect cases of fraud behaviour, especially
cases of financial fraud in the Banking sector.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
Palavras-chave
Fraud Behaviour Financial Fraud Money Laundering Supervised Learning Imbalanced data Oversampling Data Mining SDG 8 - Decent work and economic growth SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals