• clean_code: notebooks with the final code producing important results
• notebooks: exploritary notebooks which are not included in final result
• laundrywomen_presentation: our pitch presentation

===

DATA SOURCES

Data provided by CreditSuisse:

'large.csv' (http://bit.ly/36jCXYi)
'small.csv' (http://bit.ly/33kJboM)
'country.csv' (http://bit.ly/36nQ5f4)
'jeopardy.csv' (http://bit.ly/2NCgFZ8)

Open source data:

1. Basel AML Index: independent annual ranking that assesses the risk of money laundering and terrorist financing around the world https://www.baselgovernance.org/basel-aml-index/public-ranking

'AML_risk_ranking.csv'

2. Corruption Perceptions Index 2018 by Transparency International: ranks 180 countries by their perceived levels of public sector corruption according to experts and businesspeople https://www.transparency.org/cpi2018

'corruption_index_cross-over.csv'

DATA PRE-PROCESSING

• Feature augmentation log and norm transformations

• Introduced 4 categories: 0: Normal individual 1: Company 2: Other institution 3: is_pep individual

• Adding new features from external sources
  0: AML risk score and rank for countries
  1: Corruption index for countries

DATA EXPLORATION

general statistics

• type of data
• structure and relationship of features

visualize data

test outlier detection method LOF

MODELS

Model choice: to understand the model's behaviour

DecisionTreeClassifier CatBoost SVM

Steps:

• train-test split
• upsampling of minority class in training set
• cross-validation on training set; aim: optimize f1_micro score (detect minority class)
• test with test set
• evaluation: classification report, ROC

INTERPRETATION

• Visualization feature importance from CatBoost and DecisionTree
• Plotting distribution of most important features
• 2D-distribution of highly interaction features
• Confirming / rejecting hypothesis about external data sources ( CIP, - AML)
• Usual outlier detection method (LOF) is not suitable to detect suspicious behaviour