Fraud detection by Denisa Banulescu-Radu

Banulescu-Radu (LEO) WiMLDS 13/04/2021 1 / 39

Data Science for Financial Fraud Detection
Denisa BANULESCU-RADU
University of Orléans, LEO
WiMLDS 13th of April 2021

Background
• Since 2015: Associate Professor – University of Orléans, LEO
• 2016: Young Researcher Award in Economics – Autorité des Marchés
Financiers
• 2015: Thesis Prize – Fondation Banque de France
• 2014-2015: Max Weber Postdoctoral Fellow – European University Institute
• 2011-2014: PhD in Economics – Maastricht University and University of
Orléans
Title dissertation: "Four essays in financial econometrics"

Main research interests

Outline
1 Econometrics vs Machine Learning
2 General aspects of fraud
3 Main challenges and solutions
4 Case studies
4.1 Case 1: Insurance fraud detection
4.2 Case 2: Social fraud detection
5 Conclusion

Econometrics vs Machine Learning
Outline
4 Case studies
5 Conclusion

“there are a number of areas where there would be opportunities
for fruitful collaboration between econometrics and machine
learning ”
Hal Varian (2014) - Professor of Economics (University of Michigan) & Chief Economist
(Google)

General aspects of fraud
Outline
4 Case studies
5 Conclusion

Fraud detection - Why is it important?

Definition of fraud
Definition
• Baesens et al. (2015)
Fraud is an uncommon, well-considered, imperceptibly
concealed, time-evolving, and often carefully organized crime
which appears in many types of forms.

Typologies of fraud

Main challenges and solutions
Outline
4 Case studies
5 Conclusion

Main CHALLENGES and solutions

Main challenges and SOLUTIONS
1. Main tools used to fight fraud

2. Deal with imbalanced datasets

3. Evaluation of fraud detection models

4. Improving the interpretability of fraud detection models
“if the users do not trust a model or a prediction, they will not use it”
(Ribeiro et al., 2016)
• LIME method
Ribeiro et al. (2016)
• SHAP (SHapley Additive exPlanations) value
Lundberg and Lee, (2017)
BUT ... to what extent do we need fraud detection models to be interpretable?

Case studies
Outline
4 Case studies
5 Conclusion

Case studies Case 1: Insurance fraud detection
Outline
4 Case studies
5 Conclusion

General framework
• Fraud claims represented 10% of the total number of claims in 2019 (Insurance
Europe)
• Negative record for France: e2.5 Billion in 2014. Only e219 million recovered.
(ALFA)

Methodology
DATA
• 45 954 house claims for the period 2013 to 2017
• French insurance company
• 0.76% of claims are fraudulent
Technical tools
• Logistic LASSO (Cox, 1958; Tibshirani, 1996)
• Random forest (Breiman, 2001)
• Extreme Gradient Boosting or Xgboost (Chen and Guestrin, 2016)
Resampling techniques to deal with imbalanced data
• Random Oversampling
• Synthetic Minority Oversampling TEchnique or SMOTE (Chawla et al., 2002)
• ADAptive SYNthetic sampling or ADASYN (He et al., 2008)
Performance metrics
• AUC-ROC, AUC-PR, Brier score, Log-Loss, F-measure

Methodology

• Interpretation of results: SHAP value method (global/individual level)
Figure 1: Fraudulent case
Figure 2: Non Fraudulent case

Case studies Case 2: Social fraud detection
Outline
4 Case studies
5 Conclusion

General framework
• Controlling the risks of social and fiscal fraud and combating illegal work are
also important problems for social justice and economic efficiency
• French mutual organization
• collects data systematically from their beneficiaries
• organizes regular controls on a subsample of their taxpayers
• manages a fraud detection system to identify those who do not pay
their contributions

General framework
Objective: Estimate the tax shortfall.
Definition
The tax shortfall is defined as the potential sum of the tax adjustments
that could have been imposed on companies having defrauded or made er-
roneous social declarations, if they had been effectively audited, whereas
they were not in reality.

Remarks
• the two decisions are neither sequential nor conditional
• the decisions are linked

Methodology: Estimation by Maximum Likelihood
Control decision
Ci =
(
1
0
if C∗
i = Xc,i βc + εc,i > 0
otherwise
∀i = 1, . . . , n (1)
Fraud decision
e
Di =

1
0
if D∗
i = Xd,i βd + εd,i 0
otherwise
∀i = 1, . . . , n (2)
Potential tax shortfall
M∗
i =
(
Xm,i βm + εm,i
0
if e
Di = 1
otherwise
∀i = 1, ..n (3)


εc,i
εd,i
εm,i

 ∼ N

0,
X
with
X
= DRD (4)
D =



σc 0 0
0 σd 0
0 0 σm


 R =



1 ρcd ρcm
ρcd 1 ρdm
ρcm ρdm 1


 (5)

Conclusion
Outline
4 Case studies
5 Conclusion

Conclusion
Thank you for your attention!

Conclusion

Fraud detection by Denisa Banulescu-Radu

Recommended

Recommended

More Related Content

Similar to Fraud detection by Denisa Banulescu-Radu

Similar to Fraud detection by Denisa Banulescu-Radu (20)

More from Paris Women in Machine Learning and Data Science

More from Paris Women in Machine Learning and Data Science (20)

Recently uploaded

Recently uploaded (20)

Fraud detection by Denisa Banulescu-Radu