Fraud Detection on Simulated Financial Transactions

Purpose:

The purpose of this study is to identify fraudulent transactions in an extremely unbalanced dataset.

Data Dictionary:

	type	description
step	int	1 hour unit of time
type	obj	kind of transaction (ie payment, transfer, cash_out)
amount	float64	dollar amount of transaction
nameOrig	obj	identifier of origin account
oldBalanceOrig	float64	pre-transaction balance of origin account
newBalanceOrig	float64	post-transaction balance of origin account
nameDest	obj	identifier of destination account
oldBalanceDest	float64	pre-transaction balance of destination account
newBalanceDest	float64	post-transaction balance of destination account
isFraud	int64	boolean, 1 for Fraud, 0 for not
isFlaggedFraud	int64	boolean, 1 for Fraud, 0 for not

Phase 1 - Problem Definition

1.1 Broad Goals  
1.2 Data Source  
1.3 Problem Statement   

Phase 2 - Data Gathering

2.1 Load Files   

Phase 3 - Exploratory Data Analysis

3.1 Dataset Shape   
3.2 Fraud by Transaction Type   
3.3 Examining Confirmed Fraud Samples   
3.4 Exploring Engineered Features   
3.5 Correllation to Fraud   

Phase 4 - Modeling

4.1 Load/ Clean
4.2 Feature Engineering
4.3 Train/Test/Split  
4.4 Methods for Handling Imbalanced Classes   
    - oversampling   
    - undersampling  
    - SMOTE 
4.5 Logistic Regression    
4.6 Random Forest Classifier   

Phase 5 - Model Analysis

5.1 Baseline Score  
5.2 Model Selection  
5.3 Production Model Evaluation

Phase 6 - Conclusions

6.1 Revisit 1.3 Problem Statement  
6.2 Conclusions  
6.3 Recommendations for Further Research 

Acknowledgements

PaySim first paper of the simulator:

E. A. Lopez-Rojas , A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016

Engineered error columns inspired by Arjun Joshua's award winning kaggle notebook: https://www.kaggle.com/arjunjoshua/predicting-fraud-in-financial-payment-services?rvi=1&scriptVersionId=2200418&cellId=1

Imbalanced Learning Techniques Inspired by Tom Fawcett https://www.svds.com/learning-imbalanced-classes/