Sectorial Analysis Impact on the Development of Credit Scoring Machine Learning Models

被引:0
|
作者
El-Qadi, Ayoub [1 ,2 ]
Trocan, Maria [3 ]
Frossard, Thomas [2 ]
Diaz-Rodriguez, Natalia [4 ,5 ]
机构
[1] Sorbonne Univ, Paris, France
[2] Tinubu Sq, Issy Les Moulineaux, France
[3] Inst Super Elect Paris, Issy Les Moulineaux, France
[4] Univ Granada, Comp Sci & Artificial Intelligence Dept, Andalusian Res Inst Data Sci & Computat Intellige, Granada, Spain
[5] Univ Granada, Res Ctr Informat & Commun Technol, Granada, Spain
关键词
Machine Learning; SHapley Additive eXplanations; Credit Scoring; Small and Medium Enterprises; Economic Sectors; FINANCIAL RATIOS; PREDICTION;
D O I
10.1145/3508397.3564835
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Small and Medium-sized Enterprises play an essential role in the growth of the global economy. The access to credit for these companies allows them to fund the development of their activities. Artificial Intelligence has emerged as a potential tool to help financial and insurance institutions to assess Small and Medium-sized companies and thus, accelerate their activities. The economic sector in which companies operate is an essential factor when it comes to determining the risk of default. On the other hand, to introduce Artificial Intelligence in a highly regulated industry, the principal actors need to understand the behavior of the models. In this paper, we focus on the development of Artificial Intelligence-based models for different economic sectors Furthermore, we analyze the model behavior using SHapley Additive exPlanations. We compare both the performance and the explanations of the different economic sector models with the global model. Our study shows that there is a slight improvement in terms of performance when creating the different sectorial models. The comparison between the explanations for each model reveals certain disagreements in terms of the most relevant features.
引用
收藏
页码:115 / 122
页数:8
相关论文
共 50 条
  • [31] Evolutionary Extreme Learning Machine with novel activation function for credit scoring
    Tripathi, Diwakar
    Edla, Damodar Reddy
    Kuppili, Venkatanareshbabu
    Bablani, Annushree
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 96
  • [32] A Discretized Enriched Technique to Enhance Machine Learning Performance in Credit Scoring
    Saia, Roberto
    Carta, Salvatore
    Recupero, Diego
    Fenu, Gianni
    Saia, Marco
    KDIR: PROCEEDINGS OF THE 11TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL 1: KDIR, 2019, : 202 - 213
  • [33] Subagging for credit scoring models
    Paleologo, Giuseppe
    Elisseeff, Andre
    Antonini, Gianluca
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2010, 201 (02) : 490 - 499
  • [34] JointLIME: An interpretation method for machine learning survival models with endogenous time-varying covariates in credit scoring
    Chen, Yujia
    Calabrese, Raffaella
    Martin-Barragan, Belen
    RISK ANALYSIS, 2024,
  • [35] Credit Scoring using Support Vector Machine: A Comparative Analysis
    Harikrishna, S.
    Farquad, M. A. H.
    Shabana
    MATERIALS SCIENCE AND INFORMATION TECHNOLOGY, PTS 1-8, 2012, 433-440 : 6527 - +
  • [36] SAnDReS 2.0: Development of machine-learning models to explore the scoring function space
    de Azevedo Jr, Walter Filgueira
    Quiroga, Rodrigo
    Villarreal, Marcos Ariel
    da Silveira, Nelson Jose Freitas
    Bitencourt-Ferreira, Gabriela
    da Silva, Amauri Duarte
    Veit-Acosta, Martina
    Oliveira, Patricia Rufino
    Tutone, Marco
    Biziukova, Nadezhda
    Poroikov, Vladimir
    Tarasova, Olga
    Baud, Stephaine
    JOURNAL OF COMPUTATIONAL CHEMISTRY, 2024, 45 (27) : 2333 - 2346
  • [37] Network-aware credit scoring system for telecom subscribers using machine learning and network analysis
    Gao, Hongming
    Liu, Hongwei
    Ma, Haiying
    Ye, Cunjun
    Zhan, Mingjun
    ASIA PACIFIC JOURNAL OF MARKETING AND LOGISTICS, 2022, 34 (05) : 1010 - 1030
  • [38] Borrowers' credit quality scoring model and applications, with default discriminant analysis based on the extreme learning machine
    Pang, Sulin
    Hou, Xianyan
    Xia, Lianhu
    TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2021, 165
  • [39] Predicting Credit Repayment Capacity with Machine Learning Models
    Filiz, Gozde
    Bodur, Tolga
    Yaslidag, Nihal
    Sayar, Alperen
    Cakar, Tuna
    32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024, 2024,
  • [40] Impact of resampling methods and classification models on the imbalanced credit scoring problems
    Xiao, Jin
    Wang, Yadong
    Chen, Jing
    Xie, Ling
    Huang, Jing
    INFORMATION SCIENCES, 2021, 569 (569) : 508 - 526