[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118892-en":3,"doc-seo-118892-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118892,16904993612988,"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, Earnings Forecasting","This doctoral thesis advances machine learning techniques for financial decision-making by addressing three connected challenges: anomaly detection, accounting fraud identification, and earnings forecasting. It proposes a semi-supervised framework for detecting anomalies in big accounting data using pseudo-labeling with DBSCAN and a LightGBM model, supported by benchmark evaluation and experiments. For fraud detection, it develops an approach based on contextual language learning and constructs text and ensemble datasets with a validation strategy and evaluation metrics, yielding practical insights for financial investigators. The work further studies factors that make earnings predictable within a structured theoretical framework.","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 .............","cbCaimD3a60J6Ozs","https://ap.wps.com/l/cbCaimD3a60J6Ozs","pdf",3907500,1,144,"English","en",105,"# Introduction\n## A semi-supervised machine learning approach to detect anomalies in big accounting data\n## Accounting fraud detection using contextual language learning\n## What Makes Earnings Predictable?","[{\"question\":\"What problem does the thesis address in financial decision-making?\",\"answer\":\"The thesis targets three tasks: detecting anomalies in large accounting data, identifying accounting fraud, and forecasting earnings.\"},{\"question\":\"How is anomaly detection performed in the proposed approach?\",\"answer\":\"It uses semi-supervised learning with pseudo-labeling via DBSCAN and a LightGBM model to predict anomalies, evaluated through experimental results and benchmark evaluation.\"},{\"question\":\"What approach is used for accounting fraud detection?\",\"answer\":\"Accounting fraud detection is built on contextual language learning, using constructed text and ensemble datasets, a defined validation strategy, and evaluation metrics to generate results and practical insights.\"}]","Advancing the power of machine learning in financial decision-making - Anomaly Detection, Fraud identification, Earnings Forecasting | PDF",1785720821,363,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"advancing-the-power-of-machine-learning-in-financial-decision-making-anomaly-detection-fraud-identification-earnings-forecasting","",{"@graph":36,"@context":86},[37,54,69],{"@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-earnings-forecasting/118892/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address in financial decision-making?","Question",{"text":76,"@type":77},"The thesis targets three tasks: detecting anomalies in large accounting data, identifying accounting fraud, and forecasting earnings.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is anomaly detection performed in the proposed approach?",{"text":81,"@type":77},"It uses semi-supervised learning with pseudo-labeling via DBSCAN and a LightGBM model to predict anomalies, evaluated through experimental results and benchmark evaluation.",{"name":83,"@type":74,"acceptedAnswer":84},"What approach is used for accounting fraud detection?",{"text":85,"@type":77},"Accounting fraud detection is built on contextual language learning, using constructed text and ensemble datasets, a defined validation strategy, and evaluation metrics to generate results and practical insights.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]