[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118891-en":3,"doc-seo-118891-105":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},118891,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Advancing the power of machine learning in Financial Decision-making - Anomaly Detection, Fraud Identification, and Earnings Forecasting","The thesis advances machine learning methods for core financial decision-making tasks, focusing on three complementary objectives: anomaly detection in large accounting data, accounting fraud detection using contextual language learning, and the drivers of earnings predictability. A semi-supervised anomaly detection framework is developed and evaluated with benchmark experiments, including model construction via pseudo-labeling and gradient-boosting predictions. For fraud identification, the approach leverages constructed text and ensemble data with clear evaluation protocols, validation strategies, and practical investigator-oriented insights.","UvA-DARE (Digital Academic Repository)  \nAdvancing the power of machine learning in financial decision-making  \nAnomaly detection, fraud identification, and earnings forecasting Bhattacharya, I.  \nPublication date  \n2023  \nDocument Version  \nFinal published version  \nLink to publication  \nCitation for published version (APA):  \nBhattacharya, I. (2023) . Advancing the power of machine learning in financial decisionmaking: Anomaly detection, fraud identification, and earnings forecasting. [Thesis, fully internal, Universiteit van Amsterdam] .  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:28 Nov 2023  \nAdvancing  \nthe power of  \nmachine learning in Financial  \nDecision making:  \nAnomaly Detection Fraud identification Earnings Forecasting  \nindranil bhattacharya  \nAdvancing the Power of Machine Learning in Financial Decision-Making: Anomaly Detection, Fraud Identification, and Earnings Forecasting  \nACADEMISCH PROEFSCHRIFT  \nter verkrijging van de graad van doctor  \naan de Universiteit van Amsterdam  \nop gezag van de Rector Magnificus  \n[prof. dr. ir. P.P.C.C. Verbeek](prof. dr. ir. P.P.C.C. Verbeek)  \nten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op woensdag 11 oktober 2023, te 10.00 uur  \ndoor Indranil Bhattacharya  \ngeboren te Arambagh  \nPromotiecommissie  \nPromotor:  \nCopromotor:  \nOverige leden:  \nprof. dr. E.E.O. Roos Lindgreen prof. dr. J.F.M.G. Bouwens  \nprof. dr. A.H. Gold prof. dr. A. Shahim prof. dr. S.I. Birbil  \nprof. dr. E. Kanoulas  \nprof. dr. S. Klous  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam  \nVrije Universiteit Amsterdam  \nVrije Universiteit Amsterdam Universiteit van Amsterdam  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam  \nFaculteit Economie en Bedrijfskunde  \nBhattacharya, I  \nAdvancing the power of Machine Learning in Financial Decision-making: Anomaly Detection, Fraud Identification, and Earnings Forecasting  \nThesis, University of Amsterdam, Amsterdam  \nISBN: 978-94-6469-554-0  \n©I.Bhattacharya, 2023  \nAll rights reserved. No part of this thesis may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanically, by photocopying, recording, or otherwise, without prior permission of the author.  \nCover design by: Saagnik Paul  \nPrinted by: ProefschriftMaken ∥ [https://www.proefschriftmaken.nl](https://www.proefschriftmaken.nl)  \n4  \nContents  \n1 Introduction 11  \n2 A semi-supervised machine learning approach to detect anomalies in big accounting data 15  \n2.1 Introduction ..................................... 15  \n2.2 Background and Related Work .......................... 16  \n2.3 Anomaly Detection Framework .......................... 19  \n2.3.1 Pseudo-Labeling using DBSCAN ..................... 19  \n2.3.2 LightGBM Model to predict anomalies .................. 20  \n2.4 Experimental Setup ................................ 22  \n2.5 Experimental Results and Discussions .............","cbCaihMR16nmgo4V","https://ap.wps.com/l/cbCaihMR16nmgo4V","pdf",3907500,1,144,"English","en",105,"# Contents\n## 1 Introduction\n## 2 A semi-supervised machine learning approach to detect anomalies in big accounting data\n## 3 Accounting fraud detection using contextual language learning\n## 4 What Makes Earnings Predictable?","[{\"question\":\"What problem does the thesis address in financial decision-making?\",\"answer\":\"It addresses anomaly detection in large accounting data, accounting fraud detection, and earnings forecasting to support more reliable financial decisions.\"},{\"question\":\"How does the thesis approach anomaly detection?\",\"answer\":\"It proposes a semi-supervised machine learning framework that uses pseudo-labeling with DBSCAN and a LightGBM model to predict anomalies, followed by experimental evaluation.\"},{\"question\":\"What methodology is used for fraud detection?\",\"answer\":\"The thesis uses contextual language learning, constructing text and ensemble data and applying defined methods, validation strategies, and evaluation metrics, with insights for financial investigators.\"}]","Advancing the power of machine learning in Financial Decision-making - Anomaly Detection, Fraud Identification, and Earnings Forecasting | PDF",1785720819,363,{"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},"advancing-the-power-of-machine-learning-in-financial-decision-making-anomaly-detection-fraud-identification-and-earnings-forecasting","",{"@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/advancing-the-power-of-machine-learning-in-financial-decision-making-anomaly-detection-fraud-identification-and-earnings-forecasting/118891/",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 problem does the thesis address in financial decision-making?","Question",{"text":75,"@type":76},"It addresses anomaly detection in large accounting data, accounting fraud detection, and earnings forecasting to support more reliable financial decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis approach anomaly detection?",{"text":80,"@type":76},"It proposes a semi-supervised machine learning framework that uses pseudo-labeling with DBSCAN and a LightGBM model to predict anomalies, followed by experimental evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"What methodology is used for fraud detection?",{"text":84,"@type":76},"The thesis uses contextual language learning, constructing text and ensemble data and applying defined methods, validation strategies, and evaluation metrics, with insights for financial investigators.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]