In the financial risk domain, particularly in credit default prediction and fraud detection, accurate identification of high-risk class instances is paramount, as their occurrence can have significant economic implications. Although machine learning models have gained widespread adoption for risk prediction, their performance is often hindered by the scarcity and diversity of high-quality data. This limitation stems from factors in datasets such as small risk sample sizes, high labeling costs, and severe class imbalance, which impede the models' ability to learn effectively and accurately forecast critical events. This study investigates data pre-processing techniques to enhance existing financial risk datasets by introducing TriEnhance, a straightforward technique that entails: (1) generating synthetic samples specifically tailored to the minority class, (2) filtering using binary feedback to refine samples, and (3) self-learning with pseudo-labels. Our experiments across six benchmark datasets reveal the efficacy of TriEnhance, with a notable focus on improving minority class calibration, a key factor for developing more robust financial risk prediction systems.
Introduction Data imbalance is one of the crucial issues in big data analysis with fewer labels. For example, in real-world healthcare data, spam detection labels, and financial fraud detection datasets. Many data balance methods were introduced to improve machine learning algorithms' performance. Research claims SMOTE and SMOTE-based data-augmentation (generate new data points) methods could improve algorithm performance. However, we found in many online tutorials, the valuation methods were applied based on synthesized datasets that introduced bias into the evaluation, and the performance go…
Machine learning has automated much of financial fraud detection, notifying firms of, or even blocking, questionable transactions instantly. However, data imbalance starves traditionally trained models of the content necessary to detect fraud. This study examines three separate factors of credit card fraud detection via machine learning. First, it assesses the potential for different sampling methods, undersampling and Synthetic Minority Oversampling Technique (SMOTE), to improve algorithm performance in data-starved environments. Additionally, five industry-practical machine learning algorith…
Adaptive kernel-density approach for imbalanced binary classification
Graduate School of Science and Technology, Nara Institute of Science and Technology, Japan（科学技术研究生院，科学与技术国立研究所，日本）；Data Science Center, Nara Institute of Science and Technology, Japan（数据科学中心，科学与技术国立研究所，日本）
University of Bridgeport（桥港大学）；University of the Potomac（波托马克大学）；Westcliff University（韦斯特克利夫大学）；Gannon University（甘农大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Washington University of Science and Technology（华盛顿科学技术大学）；Trine University（特莱恩大学）
Tampere University of Technology（坦佩雷理工大学）；Aarhus University（奥胡斯大学）
Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies. In addition, different data modalities often embed different nonlinear relationships which are difficult to capture by human-designed models. To tackle the supervised learning task in financial time-series prediction, we propose the application of a recently formulated algorithm that adaptively learns a mapping function, realized by a heterogeneous neural architecture composing of Generalized Operational Perceptron, given a set of labeled data.…
With growing credit card transaction volumes, the fraud percentages are also rising, including overhead costs for institutions to combat and compensate victims. The use of machine learning into the financial sector permits more effective protection against fraud and other economic crime. Suitably trained machine learning classifiers help proactive fraud detection, improving stakeholder trust and robustness against illicit transactions. However, the design of machine learning based fraud detection algorithms has been challenging and slow due the massively unbalanced nature of fraud data and the…
An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks
The Fields Institute for Research in Mathematical Sciences（数学科学菲尔兹研究所）；Pennsylvania State University（宾夕法尼亚州立大学）；Data Science Research Center, Duke Kunshan University（昆山杜克大学数据科学研究中心）；Zu Chongzhi Center for Mathematics and Computational Sciences, Duke Kunshan University（昆山杜克大学祖冲之数学与计算科学中心）
Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference
University of Rochester（罗切斯特大学）；University of Minnesota（明尼苏达大学）；Northeastern University（东北大学）；University of California（加利福尼亚大学）；Cornell University（康奈尔大学）
Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review
Georgia Institute of Technology, College of Computing（佐治亚理工学院计算机学院）；Harvard University, School of Engineering and Applied Sciences（哈佛大学工程与应用科学学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University, The Fu Foundation School of Engineering and Applied Science（哥伦比亚大学傅以文工程与应用科学学院）
Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions
School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST)（国家科技大学电气工程与计算机科学学院）；Department of Computer Science, Bahria University（巴里亚大学计算机科学系）；College of Computer and Information Sciences (CCIS), Prince Sultan University（苏丹王子大学计算机与信息科学学院）
A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions
Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach
Independent Researcher（独立研究者）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Southern Methodist University（南卫理公会大学）；Shanghai University of Finance and Economics（上海财经大学）
Imbalanced data set is a problem often found and well-studied in financial industry. In this paper, we reviewed and compared some popular methodologies handling data imbalance. We then applied the under-sampling/over-sampling methodologies to several modeling algorithms on UCI and Keel data sets. The performance was analyzed for class-imbalance methods, modeling algorithms and grid search criteria comparison.
Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring
Risk Management Division, China Development Bank（国家开发银行风险管理部）；School of Finance, Central University of Finance and Economics（中央财经大学金融学院）；GF Holdings (Hong Kong) Corporation Limited（广发控股（香港）有限公司）；University of Cambridge（剑桥大学）
Dist Loss: Enhancing Regression in Few-Shot Region through Distribution Distance Constraint
Institute of Medical Technology, Health Science Center of Peking University（北京大学健康科学中心医学技术研究所）；National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control
Department of Applied Statistics & Operations Research (ASOR), Bowling Green State University, Bowling Green, OH, USA（应用统计与运筹学系，布罗金格林州立大学）；Data Science Department, Actual Reality Technologies, OH, USA（数据科学系，Actual Reality Technologies）
Center for Research on Intelligent Perception and Computing（智能感知与计算研究中心）；National Laboratory of Pattern Recognition（模式识别国家实验室）；Institute of Automation, Chinese Academy of Science（中国科学院自动化研究所）
Corporate credit rating reflects the level of corporate credit and plays a crucial role in modern financial risk control. But real-world credit rating data usually shows long-tail distributions, which means heavy class imbalanced problem challenging the corporate credit rating system greatly. To tackle that, inspried by the recent advances of pre-train techniques in self-supervised representation learning, we propose a novel framework named Contrastive Pre-training for Corporate Credit Rating (CP4CCR), which utilizes the self-surpervision for getting over class imbalance. Specifically, we prop…
Deep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification
Faculty of Engineering, University of Ruhuna（鲁胡纳大学工程学院）；College of Engineering, Computing and Cybernetics, The Australian National University（澳大利亚国立大学工程、计算与控制学院）
Beyond Fixed Patches: Enhancing GPTs for Financial Prediction with Adaptive Segmentation and Learnable Wavelets
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；School of Mathematical Sciences, Tongji University（同济大学数学科学学院）；Seek Data Group, Emoney Inc.（Seek Data集团，Emoney公司）
Learning from positive and unlabeled (PU) data is a setting where the learner only has access to positive and unlabeled samples while having no information on negative examples. Such PU setting is of great importance in various tasks such as medical diagnosis, social network analysis, financial markets analysis, and knowledge base completion, which also tend to be intrinsically imbalanced, i.e., where most examples are actually negatives. Most existing approaches for PU learning, however, only consider artificially balanced datasets and it is unclear how well they perform in the realistic scen…
