[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119193-en":3,"doc-seo-119193-105":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":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},119193,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predictive Analysis of CFPB Consumer Complaints Using Machine Learning - Platform Overview and Results","This paper presents the Consumer Feedback Insight & Prediction Platform that applies machine learning to the Consumer Financial Protection Bureau (CFPB) Complaint Database, a public dataset larger than 4.9 GB. Using complaint records spanning from 2007 to April 2024, the platform predicts both the timeliness of company responses and the response outcome category. Latent Dirichlet Allocation (LDA) topic modeling further extracts recurring themes and emerging consumer issues, supporting consumers’ wait-time expectations and regulators’ risk-focused scrutiny.","Predictive Analysis of CFPB Consumer Complaints Using Machine Learning  \nDhwani Vaishnav, Manimozhi Neethinayagam, Akanksha Khaire, Jongwook Woo  \nDepartment of Information Systems, California State University Los Angeles  \n[dvaishn2@calstatela.edu](dvaishn2@calstatela.edu), [mneethi@calstatela.edu](mneethi@calstatela.edu), [akhaire3@calstatela.edu](akhaire3@calstatela.edu), [jwoo5@calstatela.edu](jwoo5@calstatela.edu)  \nAbstract: This paper introduces the Consumer Feedback Insight & Prediction Platform, a system leveraging machine learning to analyze the extensive Consumer Financial Protection Bureau (CFPB) Complaint Database, a publicly available resource exceeding 4.9 GB in size. This rich dataset offers valuable insights into consumer experiences with financial products and services. The platform itself utilizes machine learning models to predict two key aspects of complaint resolution: the timeliness of company responsesand the nature of those responses (e.g., closed, closed with relief etc.) . Furthermore, the platform employs Latent Dirichlet Allocation (LDA) to delve deeper, uncovering common themes within complaints and revealing underlying trends and consumer issues. This comprehensive approach empowers both consumers and regulators. Consumers gain valuable insights into potential response wait times, while regulators can utilize the platform's findings to identify areas where companies may require further scrutiny regarding their complaint resolution practices.  \n1. Introduction  \nThe Consumer Financial Protection Bureau (CFPB) is a U.S. government agency responsible for ensuring banks and other financial institutions treat consumers fairly. The CFPB maintains a publicly available Consumer Complaint Database [1] . This ever-growing CFPB Complaint Database offers a rich resource for understanding consumer experiences in the financial marketplace. This paper introduces a Consumer Feedback Insight & Prediction Platform that leverages machine learning models trained on consumer complaints data from 2007 to April 2024, the platform extracts actionable insights, predicting company response times and the nature of complaint resolution. Furthermore, it utilizes topic modeling to identify recurring themes within complaints, revealing prevalent consumer issues. This information equips both consumers and regulators with valuable tools. Consumers gain insights to make informed decisions, while regulators can leverage the platform to prioritize their efforts, ultimately fostering a fairer financial marketplace.  \n2. Related Work  \nThe field of customer complaint analysis using machine learning is rapidly evolving, offering significant potential for improved customer service. Our work builds upon this foundation, drawing inspiration from several key studies.  \nSingh et al. (2023) explored the application of machine learning, specifically Logistic Regression and Support Vector Machines (SVM), for analyzing and predicting CFPB customer complaint data [2] . Their research demonstrates the effectiveness of this approach, with SVM achieving slightly better performance.  \nLi et al. (2023) focused on applying machine learning models specifically to predict complaint outcomes at Wells Fargo [3] . Their findings suggest that a Random Forest model can achieve high accuracy in predicting different complaint resolution paths.  \nWhile not directly related to machine learning, the CFPB’s Consumer Response Annual Report 2023 underscores the importance of analyzing consumer complaint data for regulatory purposes [4] . The report details how the CFPB utilizes various techniques like text analytics and data visualization to monitor risks, assess company performance, and identify trends within the financial sector.  \nThese studies all highlight the growing adoption of machine learning for customer complaint analysis. Our work expands upon this existing research in two keyways.  \nFirst, we aim to predict not only the likelihood of a timely response ","cbCaiixcjnLg45XE","https://ap.wps.com/l/cbCaiixcjnLg45XE","pdf",790239,1,4,"English","en",105,"# Introduction\n# Related Work\n# Specifications\n## Dataset and Hardware Setup\n# Workflow","[{\"question\":\"What does the Consumer Feedback Insight \\u0026 Prediction Platform predict?\",\"answer\":\"It predicts the timeliness of a company’s response and the nature of the complaint resolution outcome (e.g., closed categories). It also uses topic modeling to surface recurring complaint themes.\"},{\"question\":\"Which dataset does the platform use and what timeframe does it cover?\",\"answer\":\"The platform uses the CFPB Consumer Complaint Database, containing complaint details continuously updated from 2011 to April 2024 (as described in the specifications section).\"},{\"question\":\"How does topic modeling contribute to the analysis?\",\"answer\":\"Latent Dirichlet Allocation (LDA) is used to identify common themes within complaints, helping reveal underlying trends and prevalent consumer issues for both consumers and regulators.\"}]","Predictive Analysis of CFPB Consumer Complaints Using Machine Learning - Platform Overview and Results | PDF",1785723028,10,{"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},"predictive-analysis-of-cfpb-consumer-complaints-using-machine-learning-platform-overview-and-results","",{"@graph":36,"@context":85},[37,53,68],{"@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":21},"https://docshare.wps.com/document/predictive-analysis-of-cfpb-consumer-complaints-using-machine-learning-platform-overview-and-results/119193/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the Consumer Feedback Insight & Prediction Platform predict?","Question",{"text":75,"@type":76},"It predicts the timeliness of a company’s response and the nature of the complaint resolution outcome (e.g., closed categories). It also uses topic modeling to surface recurring complaint themes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset does the platform use and what timeframe does it cover?",{"text":80,"@type":76},"The platform uses the CFPB Consumer Complaint Database, containing complaint details continuously updated from 2011 to April 2024 (as described in the specifications section).",{"name":82,"@type":73,"acceptedAnswer":83},"How does topic modeling contribute to the analysis?",{"text":84,"@type":76},"Latent Dirichlet Allocation (LDA) is used to identify common themes within complaints, helping reveal underlying trends and prevalent consumer issues for both consumers and regulators.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"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":29,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":29,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]