fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems

Hidden Technical Debts for Fair Machine Learning in Financial Services

The recent advancements in machine learning (ML) have demonstrated the potential for providing a powerful solution to build complex prediction systems in a short time. However, in highly regulated industries, such as the financial technology (Fintech), people have raised concerns about the risk of ML systems discriminating against specific protected groups or individuals. To address these concerns, researchers have introduced various mathematical fairness metrics and bias mitigation algorithms. This paper discusses hidden technical debts and challenges of building fair ML systems in a producti…

Detection of fraudulent users in P2P financial market

Financial fraud detection is one of the core technological assets of Fintech companies. It saves tens of millions of money fro m Chinese Fintech companies since the bad loan rate is more than 10%. HC Financial Service Group is the 3rd largest company in the Chinese P2P financial market. In this paper we illustrate how we tackle the fraud detection problem at HC Financial. We utilize two powerful workhorses in the machine learning field - random forest and gradient boosting decision tree to detect fraudulent users . We demonstrate that by carefully select features and tune model parameters , we…

TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial

With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tackle this problem, we introduce the TitAnt, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech companies in the world. The system is able to predict online real-time transaction fraud in mere milliseconds. We present the problem definition, feature extraction, detection methods, implementation and deployment of the system, as well as empirical effectiveness. Extensive experiments…

优化金融科技营销：Logistic Regression与XGBoost的比较研究

Optimizing Fintech Marketing: A Comparative Study of Logistic Regression and XGBoost

本研究旨在预测客户对直邮活动的响应及违约风险，通过应用logistic regression和XGBoost等机器学习技术分析消费者行为，发现XGBoost在各项指标上表现更优，能更有效地处理复杂数据结构并提升信用风险评估能力。

通过对比学习与领域自适应的包容性金融科技贷款

Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation

针对金融科技贷款筛选中的代表性偏差问题，提出基于Transformer的序列模型，结合对比学习和领域自适应，在真实小额贷款数据上提升包容性，并将准确率和利润分别提高7.10%和8.95%。

一种用于解决信用违约预测问题的机器学习工作流

A machine learning workflow to address credit default prediction

针对信用违约预测需求，提出一套含证据权重编码预处理、多类模型训练、集成技术与多目标遗传算法超参优化的机器学习工作流，兼顾准确率与财务因素，助力更可靠的信用风险评估。