Enhanced Predictive Modeling for Financial Risk Assessment using Hybrid AI ( ML & DL) on Structured and Unstructured Data

Enhanced Predictive Modeling for Financial Risk Assessment using Hybrid AI ( ML & DL) on Structured and Unstructured Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhanced Predictive Modeling for Financial Risk Assessment using Hybrid AI ( ML & DL) on Structured and Unstructured Data Yogesh Kumar Jain, Shashi Kant Gupta, Deema Mohammed Alsekait, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8373542/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Financial institutions are finding it more challenging to accurately assess credit risk, particularly when dealing with a variety of data sources that include both unstructured textual data and structured financial records. In order to analyse financial risk utilising both structured and unstructured data, this research suggests an improved predictive modelling framework that combines conventional Machine Learning (ML) and Deep Learning (DL) techniques. The structured dataset comes from the Home Credit Default Risk dataset (307,511 applicant records, 122 characteristics). To give additional predictive capabilities, unstructured data from relevant news and social media sources is interpreted using Natural Language Processing (NLP) methods including sentiment analysis. A standard preprocessing pipeline, including SMOTEENN, label encoding, feature scaling, and missing value imputation, is utilised to resolve class imbalance in the fused dateset. While the ML pipeline employs XGBoost, LightGBM, and Random Forest in a Stacking ensemble, the DL pipeline utilises a CNN-LSTM architecture to capture both local patterns and temporal correlations. Model evaluation using Accuracy, Precision, Recall, F1-score, ROC-AUC, and confusion matrix analysis shows that the addition of unstructured variables significantly improves predictive performance over just structured baselines. While the Stacking Classifier has the best ROC-AUC of 0.9816 and the highest F1-score of 0.95, the CNN-LSTM has the highest ROC-AUC of 0.9592 and an F1-score of 0.91. The proposed hybrid approach emphasizes the advantages of combining structured and unstructured data for accurate and scalable credit risk assessment in modern financial systems. Financial Risk Assessment Ensemble Learning CNN-LSTM SMOTEENN Class Imbalance Feature Fusion Credit Scoring Deep Learning ROC-AUC Structured Data Unstructured Data Sentiment Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Feb, 2026 Reviews received at journal 19 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviews received at journal 14 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers invited by journal 13 Jan, 2026 Editor assigned by journal 17 Dec, 2025 Submission checks completed at journal 17 Dec, 2025 First submitted to journal 16 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8373542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574174981,"identity":"ad534ff9-f053-43af-8fe8-04e4eab70d5b","order_by":0,"name":"Yogesh Kumar Jain","email":"","orcid":"","institution":"IILM University","correspondingAuthor":false,"prefix":"","firstName":"Yogesh","middleName":"Kumar","lastName":"Jain","suffix":""},{"id":574174982,"identity":"5ff6358b-cf54-41c2-a496-01cbc2e1412a","order_by":1,"name":"Shashi Kant Gupta","email":"","orcid":"","institution":"Chitkara University Institute of Engineering and Technology. 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