Classification of hundreds of classes: A case study in a bank internal control department

Name: Classification of hundreds of classes: A case study in a bank internal control department
Author: Duman, Ekrem

İsim	Classification of hundreds of classes: A case study in a bank internal control department
Yazar	Duman, Ekrem
Basım Tarihi:	2023
Basım Yeri	- IOS Press
Konu	Banking, Data mining, Internal control, Multi-classification, Predictive modeling
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1064-1246
Kayıt Numarası	a76fa507-0446-4569-8d10-731484419693
Lokasyon	Industrial Engineering
Tarih	2023
Örnek Metin	The main function of the internal control department of a bank is to inspect the banking operations to see if they are performed in accordance with the regulations and bank policies. To accomplish this, they pick up a number of operations that are selected randomly or by some rule and, inspect those operations according to some predetermined check lists. If they find any discrepancies where the number of such discrepancies are in the magnitude of several hundreds, they inform the corresponding department (usually bank branches) and ask them for a correction (if it can be done) or an explanation. In this study, we take up a real-life project carried out under our supervisory where the aim was to develop a set of predictive models that would highlight which operations of the credit department are more likely to bear some problems. This multi-classification problem was very challenging since the number of classes were enormous and some class values were observed only a few times. After providing a detailed description of the problem we attacked, we describe the detailed discussions which in the end made us to develop six different models. For the modeling, we used the logistic regression algorithm as it was preferred by our partner bank. We show that these models have Gini values of 51 per cent on the average which is quite satisfactory as compared to sector practices. We also show that the average lift of the models is 3.32 if the inspectors were to inspect as many credits as the number of actual problematic credits.
DOI	10.3233/JIFS-223679
Cilt	45

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Classification of hundreds of classes: A case study in a bank internal control department

Yazar Duman, Ekrem

Basım Tarihi 2023

Basım Yeri - IOS Press

Konu Banking, Data mining, Internal control, Multi-classification, Predictive modeling

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1064-1246

Kayıt Numarası a76fa507-0446-4569-8d10-731484419693

Lokasyon Industrial Engineering

Tarih 2023

Örnek Metin The main function of the internal control department of a bank is to inspect the banking operations to see if they are performed in accordance with the regulations and bank policies. To accomplish this, they pick up a number of operations that are selected randomly or by some rule and, inspect those operations according to some predetermined check lists. If they find any discrepancies where the number of such discrepancies are in the magnitude of several hundreds, they inform the corresponding department (usually bank branches) and ask them for a correction (if it can be done) or an explanation. In this study, we take up a real-life project carried out under our supervisory where the aim was to develop a set of predictive models that would highlight which operations of the credit department are more likely to bear some problems. This multi-classification problem was very challenging since the number of classes were enormous and some class values were observed only a few times. After providing a detailed description of the problem we attacked, we describe the detailed discussions which in the end made us to develop six different models. For the modeling, we used the logistic regression algorithm as it was preferred by our partner bank. We show that these models have Gini values of 51 per cent on the average which is quite satisfactory as compared to sector practices. We also show that the average lift of the models is 3.32 if the inspectors were to inspect as many credits as the number of actual problematic credits.

DOI 10.3233/JIFS-223679

Cilt 45