[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122994-en":3,"doc-seo-122994-105":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},122994,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning","Quantum kernels are considered promising for early-stage usefulness in quantum machine learning, yet beating strong classical models is difficult without sacrificing interpretability, especially when large datasets enable heavy classical exploitation. Classical approaches often degrade when data is scarce and skewed. Quantum feature spaces aim to uncover stronger links between input features and target classes, improving generalization under such constraints. This work proposes Systemic Quantum Score (SQS) and reports preliminary evidence of production-grade advantages in the finance sector, including better pattern extraction from fewer samples and improved performance versus data-hungry baselines such as XGBoost for FinTech and Neobanks.","arXiv :2404 .00015v3 [ q-fin .RM] 3 Apr 2024  \nEmpowering Credit Scoring Systems with Quantum-Enhanced  \nMachine Learning  \nJavier Mancilla 1,2 , Andr´e Sequeira 1 , Tomas Tagliani 1 , Francisco Llaneza2 , and Claudio  \nBeiza2  \n1 Falcondale LLC  \n2 Fintonic Servicios Financieros SL  \nApril 4, 2024  \nAbstract  \nQuantum Kernels are projected to provide early-stage usefulness for quantum machine learning. However, highly sophisticated classical models are hard to surpass without losing interpretability, particularly when vast datasets can be exploited. Nonetheless, classical models struggle once data is scarce and skewed. Quantum feature spaces are projected to find better links between data features and the target class to be predicted even in such challenging scenarios and most importantly, enhanced generalization capabilities. In this work, we propose a novel approach called Systemic Quantum Score (SQS) and provide preliminary results indicating potential advantage over purely classical models in a production grade use case for the Finance sector. SQS shows in our specific study an increased capacity to extract patterns out of fewer data points as well as improved performance over data-hungry algorithms such as XGBoost, providing advantage in a competitive market as it is the FinTech and Neobank regime.  \n1 Introduction  \nThe financial sector is a competitive market where minimal improvements significantly impact company’s revenue. Business processes such as default detection or score assignment are key business processes where the number of factors linking particular individuals to assigned labels makes them susceptible to be tackled by machine learning techniques. Large corporations such as JP Morgan & Chase [20, 11] have been focused on last-mile innovation to boost that extra percentage that allows them to be more competitive, save resources, and increase revenue. Conversely, smaller entities such as Neobanks and FinTechs face the challenge of competing with severely limited amounts of data due to their market focus. Thus, optimally exploiting the scarce data they may have becomes a crucial strategy to compete in the financial arena [4] .  \nQuantum computing represents a cutting-edge technology that financial institutions have heavily invested in, recognizing its potential for specific, near-term applications [2] . Nearly a decade ago that Quantum Support Vector Classifier (QSVC) [22] was proposed, demonstrating how quantum computers could enhance data classification by improving class separability with the promise of polynomial speedups for the least-squares formulation of the Support Vector Machine (SVM) [27] . Yet, the advantage of quantum-enhanced models is yet to be fully understood in the Noisy Intermediate Scale Quantum (NISQ) regime [5] .  \nSome works suggest the potential of these quantum-kernel based approaches may fall into the scenarios where scarcity of data exists [15] that way models widely used in the industry requiring large datasets, like XGBoost [8] may struggle while simpler models with enough expressively may succeed in such a challenging task. Quantum kernels have demonstrated remarkable capabilities in capturing complex, non-linear relationships with minimal quantum resources [21, 12] However, the design of quantum feature maps plays a crucial role on its ability to generalize, underscoring the importance of kernel architecture in quantum computing’s effectiveness [28, 3] .  \nIn this work we propose an end-to-end model composition algorithm, focusing on the development and integration of efficient quantum kernels to address the limitations of classical models, particularly for unbalanced datasets with a small number of samples. Such scenarios are common in the financial technology sector, including Neobanks and FinTech companies. By examining the specific case of Fintonic’s loan and fraud model, we illustrate the potential advantages and early-stage applicability of QML for this particular scenario.","cbCaijklhy635VrA","https://ap.wps.com/l/cbCaijklhy635VrA","pdf",679856,1,16,"English","en",105,"# Introduction\n## Quantum kernels\n## Systemic Quantum Score (SQS)\n# Background\n## Quantum kernels\n## Data scarcity in finance\n## Quantum-enhanced classification vs classical models","[{\"question\":\"What problem does the paper target in credit scoring systems?\",\"answer\":\"It targets improved score assignment and default detection in finance, especially in settings where data is scarce and skewed, making classical models harder to optimize and compare.\"},{\"question\":\"How does the proposed Systemic Quantum Score (SQS) differ from purely classical models?\",\"answer\":\"SQS uses quantum kernels designed through an end-to-end composition algorithm with evolutionary algorithms, aiming to extract patterns from fewer samples and enhance generalization beyond classical baselines.\"},{\"question\":\"Why are quantum feature spaces expected to help when data is limited?\",\"answer\":\"The paper argues quantum feature spaces can map data into feature representations that better relate inputs to the target class, maintaining generalization capability even under scarcity and imbalance.\"}]","Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning | 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problem does the paper target in credit scoring systems?","Question",{"text":76,"@type":77},"It targets improved score assignment and default detection in finance, especially in settings where data is scarce and skewed, making classical models harder to optimize and compare.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed Systemic Quantum Score (SQS) differ from purely classical models?",{"text":81,"@type":77},"SQS uses quantum kernels designed through an end-to-end composition algorithm with evolutionary algorithms, aiming to extract patterns from fewer samples and enhance generalization beyond classical baselines.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are quantum feature spaces expected to help when data is limited?",{"text":85,"@type":77},"The paper argues quantum feature spaces can map data into feature representations that better relate inputs to the target class, maintaining generalization capability even under scarcity and 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