Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack
Journal:
arXiv
Published Date:
Jul 7, 2025
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 $20\times$ 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.
