Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack

2025-07-07

AI Summary

Financially distressed firms possess strong incentives to manipulate financial statements—concealing operational deterioration, inflating profits, and evading fraud detection—yet existing adversarial attack methods fail to jointly optimize conflicting reverse-correlation objectives (e.g., increasing reported profit while decreasing fraud risk scores). This paper proposes Maximum Violation Multi-Objective (MVMO) attack, a novel adversarial framework that integrates accounting standards and legal constraints to define a realistic threat model, and employs dynamic multi-objective optimization to simultaneously perturb financial metrics and fraud risk scores. Experiments demonstrate that MVMO successfully inflates reported earnings by 100–200% while reducing fraud scores by 15% in ~50% of cases, achieving a 20× higher success rate than conventional methods. To our knowledge, this is the first work to formalize financial statement manipulation as an adversarial machine learning problem, offering novel insights for audit defense mechanisms and regulatory technology (RegTech).

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Adversarial Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Bad actors, primarily distressed firms, have the incentive and desire to manipulate their financial reports to hide their distress and derive personal gains. As attackers, these firms are motivated by potentially millions of dollars and the availability of many publicly disclosed and used financial modeling frameworks. Existing attack methods do not work on this data due to anti-correlated objectives that must both be satisfied for the attacker to succeed. We introduce Maximum Violated Multi-Objective (MVMO) attacks that adapt the attacker's search direction to find $20 imes$ more satisfying attacks compared to standard attacks. The result is that in $approx50%$ of cases, a company could inflate their earnings by 100-200%, while simultaneously reducing their fraud scores by 15%. By working with lawyers and professional accountants, we ensure our threat model is realistic to how such frauds are performed in practice.

Problem

• Detect adversarial attacks on financial reporting systems
• Address anti-correlated objectives in financial fraud
• Improve fraud detection with MVMO attack method

Innovation

• MVMO attacks optimize search direction
• Inflate earnings while reducing fraud scores
• Collaboration with legal and accounting experts