The paper presents a deep learning credit risk early warning system that fuses heterogeneous data sources, such as transaction behaviors and social networks, using deep neural networks and attention mechanisms. By extracting multidimensional features, the system establishes an early identification mechanism for corporate and individual credit risks. Testing shows that this approach improves the accuracy and timeliness of risk warnings compared to traditional rule‑based engines.
arXiv:2609.25542v1 Announce Type: new Abstract: Corporate default prediction is a core problem in financial risk management, yet traditional credit models rely heavily on financial statements that ar...
arXiv:2605. 23955v3 Announce Type: replace Abstract: Deploying machine learning in regulated financial environments -- credit risk, fraud detection, and anti-money laundering -- exposes critical vulnerabilities in algorithmic reproducibility.
arXiv:2608.20343v1 Announce Type: new Abstract: This study develops and evaluates a bankruptcy prediction framework that integrates consensus-based feature selection, hybrid resampling, stacking ense...
arXiv:2607. 19350v1 Announce Type: new Abstract: Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.
The paper introduces DTD‑VAE, a Variational Autoencoder that disentangles temporal dependencies to better predict credit risk. It uses an autoregressive feature inference module to capture temporal patterns among latent variables and an element‑wise gating mechanism in the generative module to assign independent weights to each latent dimension, especially those relevant to credit risk. Experiments on six real‑world datasets show the model outperforms existing methods, improving ROC‑AUC by 3.2%–4.86% and Accuracy Ratio by 6.41%–9.71%.
arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.
arXiv:2607. 14416v1 Announce Type: new Abstract: The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults.
arXiv:2609.09945v1 Announce Type: cross Abstract: Machine learning-based credit scoring is increasingly central to Peer-to-Peer (P2P) lending, yet its resilience to adversarial manipulation, where ap...
arXiv:2604.08649v2 Announce Type: replace-cross Abstract: Modern financial systems generate vast quantities of transactional and event-level data that encode rich economic signals. This paper present...
The paper introduces the Integrated Multivariate Segmentation Tree (IMST), a new framework that combines financial data and textual information for credit evaluation of small- and medium-sized enterprises. IMST transforms text into numerical matrices via matrix factorization, selects key financial features with Lasso regression, and builds a multivariate segmentation tree using Gini or entropy with weakest-link pruning. Experiments on 1,428 Chinese SMEs show an 88.9% accuracy, outperforming baseline decision trees, SVMs, and neural networks while offering better interpretability and computational efficiency.
FINESSE is an agent‑based simulation framework that generates synthetic, structured datasets of multiple interdependent financial event streams, such as transactions, payments, account status changes, and policy interventions. Each stream has its own action space, schema, and variable types, and the streams are coupled through agents’ evolving latent states, allowing temporally rich interactions. The accompanying FINESSE‑Bench dataset supports four tasks—balance forecasting, transaction fraud detection, missed payment prediction, and next event prediction—and baseline results are provided using various time‑series and event‑sequence methods.
The paper proposes a hybrid generative adversarial network (GAN) that combines a bi-directional LSTM and a CNN (Bi‑LSTM‑CNN) to generate synthetic financial data aligned with real market data. By preserving stock trend features, the model predicts future stock price movements across multiple markets (TSX, SHCOMP, S&P 500). Experiments show that this hybrid approach outperforms existing machine‑learning prototypes, and the study highlights gaps between investors and technical researchers.