Online transaction fraud presents substantial challenges to businesses and
consumers, risking significant financial losses. Conventional rule-based
systems struggle to keep pace with evolving fraud tactics, leading to high
false positive rates and missed detections. Machine learning techniques offer a
promising solution by leveraging historical data to identify fraudulent
patterns. This article explores using the personalised PageRank (PPR) algorithm
to capture the social dynamics of fraud by analysing relationships between
financial accounts. The primary objective is to compare the performance of
traditional features with the addition of PPR in fraud detection models.
Results indicate that integrating PPR enhances the model's predictive power,
surpassing the baseline model. Additionally, the PPR feature provides unique
and valuable information, evidenced by its high feature importance score.
Feature stability analysis confirms consistent feature distributions across
training and test datasets.

通过分析金融账户之间的关系，本研究探讨了使用个性化 PageRank 算法来捕捉欺诈的社会动态。结果表明，将个性化 PageRank 算法融入欺诈检测模型可以提高预测能力，并且该算法的重要性得分较高，特征稳定性分析确认训练和测试数据集之间的特征分布一致。

基于网络特征的欺诈检测潜力

On the Potential of Network-Based Features for Fraud Detection

Devising procedures for downstream task-oriented generative model selections
is an unresolved problem of practical importance. Existing studies focused on
the utility of a single family of generative models. They provided limited
insights on how synthetic data practitioners select the best family generative
models for synthetic training tasks given a specific combination of machine
learning model class and performance metric. In this paper, we approach the
downstream task-oriented generative model selections problem in the case of
training fraud detection models and investigate the best practice given
different combinations of model interpretability and model performance
constraints. Our investigation supports that, while both Neural
Network(NN)-based and Bayesian Network(BN)-based generative models are both
good to complete synthetic training task under loose model interpretability
constrain, the BN-based generative models is better than NN-based when
synthetic training fraud detection model under strict model interpretability
constrain. Our results provides practical guidance for machine learning
practitioner who is interested in replacing their training dataset from real to
synthetic, and shed lights on more general downstream task-oriented generative
model selection problems.

在选择适合特定的机器学习模型类和性能度量组合的合成训练任务的最佳生成模型的过程中，我们的研究发现，当模型解释性要求苛刻时，基于贝叶斯网络的生成模型优于基于神经网络的生成模型。我们的结果为有意将训练数据从真实数据集替换为合成数据集的机器学习从业者提供了实际指导，并为更一般的下游任务导向的生成模型选择问题提供了启示。

针对合成数据的下游任务导向生成模型选择 —— 用于欺诈检测模型的训练

Downstream Task-Oriented Generative Model Selections on Synthetic Data  Training for Fraud Detection Models

Exploring generative model training for synthetic tabular data, specifically
in sequential contexts such as credit card transaction data, presents
significant challenges. This paper addresses these challenges, focusing on
attaining both high fidelity to actual data and optimal utility for machine
learning tasks. We introduce five pre-processing schemas to enhance the
training of the Conditional Probabilistic Auto-Regressive Model (CPAR),
demonstrating incremental improvements in the synthetic data's fidelity and
utility. Upon achieving satisfactory fidelity levels, our attention shifts to
training fraud detection models tailored for time-series data, evaluating the
utility of the synthetic data. Our findings offer valuable insights and
practical guidelines for synthetic data practitioners in the finance sector,
transitioning from real to synthetic datasets for training purposes, and
illuminating broader methodologies for synthesizing credit card transaction
time series.

本研究探讨了用于合成表格数据的生成模型训练，特别是在连续上下文中，如信用卡交易数据，它面临着重大挑战。本文解决了这些挑战，重点在于实现数据的高保真度和机器学习任务的最佳效用。我们引入了五个预处理方案来增强条件概率自回归模型 (CPAR) 的训练，展示了合成数据保真度和效用的渐进改进。在达到令人满意的保真度水平后，我们将注意力转向针对时间序列数据进行定制的欺诈检测模型的训练，评估合成数据的效用。我们的发现为金融领域的合成数据实践者提供了有价值的见解和实用指南，从真实数据过渡到合成数据用于训练，并阐明了合成信用卡交易时间序列的更广泛方法。