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.
arXiv:2607. 09955v1 Announce Type: cross Abstract: Predictive modeling is a core component of modern financial services, where a wide range of tasks are traditionally addressed using separate models trained on manually engineered tabular features.
arXiv:2609.34211v2 Announce Type: replace Abstract: In financial fraud detection, rich semantic context can provide important evidence for transaction behavior modeling and fraud reasoning. However,...
arXiv:2607. 09528v1 Announce Type: new Abstract: The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior.
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: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...
arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.
arXiv:2608. 14198v1 Announce Type: new Abstract: Banks analyse sequential financial transaction data to perform many tasks, including fraud prevention, credit risk assessment and offer personalization.
arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.
arXiv:2608. 19447v1 Announce Type: new Abstract: Shocks that spread through the web, such as cybersecurity breach disclosures, can abruptly disrupt financial time series and cause substantial abnormal losses.
PAWS is a new dataset for policy-driven agentic world simulation that covers 36 verified U.S. financial and economic policy episodes. It includes 12,727 policy-linked news records and 65,291 stakeholder actions, each linked to supporting news and represented by a multi-layer event frame with interaction mode, financial-action family, semantic attributes, and taxonomic mappings. The dataset aligns actions with daily market-return context and has been validated by AI and human reviewers, demonstrating high agreement on interaction mode and revealing challenges in detecting rare stakeholder actions.
arXiv:2607. 09641v1 Announce Type: cross Abstract: Financial anomaly detection suffers from extreme class imbalance, causing traditional single-objective algorithms to exhibit ``fraud collapse'', defaulting to the majority class and failing to balance anomaly interdiction with customer friction.
SR‑Fraud is a framework that uses a frozen, stateless LLM agent to score transactions in real time while an offline reflection agent proposes boundary hypotheses based on matured errors. The system then verifies these hypotheses deterministically before updating its knowledge state. On a production payment‑fraud benchmark, SR‑Fraud outperforms both static and periodically retrained CatBoost models and successfully detects an emerging fraud burst.