[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118952-en":3,"doc-seo-118952-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118952,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Credit scoring models: Evolution from standard statistical methods to machine learning techniques","The work discusses the evolution of credit scoring models over time, moving from traditional statistical techniques toward advanced machine learning approaches, and highlights the operational value of these methods within lending. Two perspectives guide the analysis: the role and application areas of credit scoring across the credit creation and disbursement process, including the practices banks and financial institutions should follow to measure default risk. The study then examines key statistical models and clarifies differences between standard and advanced approaches in terms of performance and reliability.","UNIVERSITA’ DEGLI STUDI DI PADOVA  \nDIPARTIMENTO DI SCIENZE ECONOMICHE ED AZIENDALI  \n“M. FANNO”  \nCORSO DI LAUREA IN ECONOMIA  \nPROVA FINALE  \n“Credit scoring models: Evolution from standard statistical methods to machine learning techniques”  \nRELATORE:  \nCH.MO PROF. WEBER GUGLIELMO  \nLAUREANDO: DONGHI FRANCESCO  \nMATRICOLA N. 2010152  \nANNO ACCADEMICO 2022 – 2023  \nDichiaro di aver preso visione del \"Regolamento antiplagio\" approvato dal Consiglio del Dipartimento di Scienze Economiche e Aziendali e, consapevole delle conseguenze derivantida dichiarazioni mendaci, dichiaro che il presente lavoro non è già stato sottoposto, in tutto o in parte, per il conseguimento di un titolo accademico in altre Università italiane o straniere. Dichiaro inoltre che tutte le fonti utilizzate per la realizzazione del presente lavoro, inclusi imateriali digitali, sono state correttamente citate nel corpo del testo e nella sezione'Riferimenti bibliografici'.  \nI hereby declare that I have read and understood the \"Anti-plagiarism rules and regulations\"approved by the Council of the Department of Economics and Management and f am aware of the consequences of making false statements. I declare that this piece of work has not been previously submitted — either fully or partially — for fulfilling the requirements of an academic degree, whether in Italy or abroad. Furthermore, I declare that the references used for this work including the digital materials have been appropriately cited and acknowledged in the text and in the section 'References ' .  \nFirma (signature)  \nABSTRACT IN ITALIANO ................................................................................................................. 5  \n1. INTRODUCTION ......................................................................................................................... 6  \n2. LENDING ORIGINATION PROCESS...................................................................................... 9  \n2.1 GUIDELINES FOR CREDIT SCORING MODELS: A GENERAL OVERVIEW .............. 9  \n2.2 CREDITWORTHINESS ASSESSMENT ............................................................................ 11  \n2.2.1 CUSTOMER LENDING .............................................................................................. 12  \n2.2.2 CORPORATE LENDING ............................................................................................. 14  \n3. CREDIT SCORING MODELS ................................................................................................. 16  \n3.1 OVERVIEW OF THE EVOLUTION OF CREDIT SCORING ........................................... 16  \n3.2 LINEAR DISCRIMINANT ANALYSIS .............................................................................. 18  \n3.2.1 ALTMAN’S Z-SCORE MODEL .................................................................................. 19  \n3.3 LOGISTIC REGRESSION MODEL .................................................................................... 20  \n3.4 INTRODUCTION TO OTHER MACHINE LEARNING MODELS .................................. 21  \n3.4.1 DECISION TREES AND ENSEMBLE METHODS ................................................... 22  \n3.4.2 NEURAL NETWORKS................................................................................................ 23  \n3.5 MEASURES FOR MODEL EVALUATION ........................................................................ 24  \n4. THE HYBRID DNN-GBT MODEL: AN EXAMPLE OF PROGRESS IN CREDIT SCORING ............................................................................................................................................ 27  \n4.1 LIMITATIONS OF CREDIT SCORING MODELS............................................................. 27  \n4.2 DATA AND MODEL PRESENTATION .............................................................................. 28  \n4.3 REVIEW OF THE MODEL ADVANCEMENTS ................................................................ 29  \n4.3.1 PERFORMANCE COMPARISONS .....","cbCaijbAuygn4ewt","https://ap.wps.com/l/cbCaijbAuygn4ewt","pdf",1594817,1,36,"English","en",105,"# Abstract\n# Introduction\n# Lending Origination Process\n## Guidelines for Credit Scoring Models: A General Overview\n## Creditworthiness Assessment\n### Customer Lending\n### Corporate Lending\n# Credit Scoring Models\n## Overview of the Evolution of Credit Scoring\n## Linear Discriminant Analysis\n### Altman’s Z-Score Model\n## Logistic Regression Model\n## Introduction to Other Machine Learning Models\n### Decision Trees and Ensemble Methods\n### Neural Networks\n## Measures for Model Evaluation\n# The Hybrid DNN-GBT Model: An Example of Progress in Credit Scoring\n## Limitations of Credit Scoring Models\n## Data and Model Presentation\n## Review of the Model Advancements\n### Performance Comparisons\n### Interpretability\n### Systemic Risk Predictions\n# Conclusions\n# References","[{\"question\":\"What is the main goal of the document?\",\"answer\":\"To explain how credit scoring models have evolved from standard statistical methods to machine learning techniques and what this implies for performance, reliability, and application in lending.\"},{\"question\":\"How does the document treat credit scoring from two perspectives?\",\"answer\":\"It first outlines the importance and applications of credit scoring in the credit creation and disbursement process. Then it analyzes statistical techniques used for credit scoring and contrasts standard versus advanced models.\"},{\"question\":\"Which modeling approaches are presented or discussed?\",\"answer\":\"The document covers an overview of the evolution of credit scoring, linear discriminant analysis including Altman’s Z-score, logistic regression, and introduces other machine learning models such as decision trees/ensemble methods and neural networks, including a hybrid DNN-GBT model example.\"}]","Credit scoring models: Evolution from standard statistical methods to machine learning techniques | PDF",1785721166,91,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"credit-scoring-models-evolution-from-standard-statistical-methods-to-machine-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/credit-scoring-models-evolution-from-standard-statistical-methods-to-machine-learning-techniques/118952/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the document?","Question",{"text":75,"@type":76},"To explain how credit scoring models have evolved from standard statistical methods to machine learning techniques and what this implies for performance, reliability, and application in lending.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the document treat credit scoring from two perspectives?",{"text":80,"@type":76},"It first outlines the importance and applications of credit scoring in the credit creation and disbursement process. 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