Paper Type
ERF
Abstract
As artificial intelligence (AI) is increasingly used in high-stakes financial decision-making, concerns about transparency and trust remain critical. This research examines how AI explainability and model performance jointly influence users’ trust in a financial fraud detection context. Using a controlled 2×2 experimental vignette design, the study manipulates AI explainability (explainable vs. non-explainable) and accuracy (high vs. low) to assess their effects on trust in AI outputs. The findings will provide evidence on the performance–explainability trade-off and identify conditions under which explainable AI enhances trust, informing theory and practice in AI-enabled auditing and fraud detection.
Paper Number
1856
Recommended Citation
Gonzalez, Victoria; Amo, Laura; and Smith, Sanjukta Das, "The Performance-Explainability Trade-Off in AI-Assisted Accounting Fraud Detection" (2026). AMCIS 2026 Proceedings. 8.
https://aisel.aisnet.org/amcis2026/sig_odis/sig_odis/8
The Performance-Explainability Trade-Off in AI-Assisted Accounting Fraud Detection
As artificial intelligence (AI) is increasingly used in high-stakes financial decision-making, concerns about transparency and trust remain critical. This research examines how AI explainability and model performance jointly influence users’ trust in a financial fraud detection context. Using a controlled 2×2 experimental vignette design, the study manipulates AI explainability (explainable vs. non-explainable) and accuracy (high vs. low) to assess their effects on trust in AI outputs. The findings will provide evidence on the performance–explainability trade-off and identify conditions under which explainable AI enhances trust, informing theory and practice in AI-enabled auditing and fraud detection.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
SIGODIS