Macabacus Report: 62% of Finance Firms Risk Client Trust as GenAI Errors Slip Through Guardrails

Macabacus has released a new report revealing a critical gap between the rapid adoption of generative AI in financial services and the implementation of necessary verification guardrails. With 87% of firms now using AI for model creation, the lack of robust review layers poses a significant risk to client trust and document accuracy across the sector.

What was announced

Macabacus released its "GenAI for Financial Services: Velocity and Verification" report, which highlights a growing disparity between the speed of AI integration and the maturity of institutional oversight. The findings are based on a survey of Macabacus’ 75,000 users alongside analysis of conversations with hundreds of clients and prospects. The data indicates that AI is no longer a peripheral tool but sits at the core of model creation and client presentation development.

Specifically, 87% of respondents reported using AI on a daily or weekly basis to generate financial models and client presentations. However, the report identifies a "verification gap," as only 23% of firms have implemented comprehensive guardrails. These guardrails are defined as a combination of approved tools, accuracy checks, brand compliance, and formal review workflows. This lack of oversight has led to tangible risks; 62% of respondents believe an AI-generated error has reached a client or internal decision-maker in the last 12 months. Furthermore, 46% admitted that an error has "probably" landed in a deliverable without being caught.

The report also uncovers a divergence in confidence levels across seniority. While 43% of analysts and associates feel AI has increased their confidence in their work, only 29% of VPs, directors, and managing directors share that sentiment. Senior leadership is also six percentage points more likely than junior staff to state that AI has made them less confident in the output. This suggests that while the workforce is moving toward automation, the decision-makers responsible for the final product remain skeptical of the unverified results.

"AI is now involved in the majority of models and documents that reach clients, with 87% of firms saying they use AI daily or weekly for this work. Deal teams should not slow down their use of AI. They need guardrails that let them move faster while maintaining accuracy and their clients' trust. That is what Macabacus is built for."

Paul Ross, Chief Marketing Officer, Macabacus

The companies involved

Macabacus operates as a leading Microsoft 365 productivity platform specifically designed for finance and professional services teams. The company provides a Microsoft-native environment that integrates directly with Excel, PowerPoint, and Word to assist users in the rapid creation and verification of financial models and deal documents. Its platform is currently utilized by approximately 75,000 users across 3,000 firms globally.

The firm’s client base spans several high-stakes sectors within the financial ecosystem, including investment banking, private equity, asset management, consulting, advisory, and corporate finance. Macabacus positions itself as a critical tool for environments where document accuracy is paramount, combining traditional productivity features with AI-powered review and automation capabilities. By focusing on the "M365" suite, the company serves as a specialized layer for professionals who require high-fidelity outputs for client-facing deliverables.

The platform’s functionality is centered on enabling organizations to execute complex financial tasks with greater speed and accuracy, addressing the specific workflow needs of deal teams who manage sensitive financial data and presentation materials. As a productivity platform, it aims to bridge the gap between the raw capabilities of Microsoft’s office suite and the rigorous, error-free requirements of the professional services industry. This focus on being Microsoft-native ensures that users can maintain their existing workflows while adding a layer of sophisticated automation and checking tools that are native to the software they use daily.

What this means

This report highlights a dangerous "competence paradox" in the fintech and investment banking sectors. While junior staff are embracing AI to accelerate output, the lack of institutional guardrails is creating a trust deficit with senior leadership. The fact that nearly two-thirds of professionals believe AI errors have already reached clients suggests that the industry’s current approach to generative AI is increasingly incompatible with the fiduciary duties of financial services. Firms are under immense pressure to automate, but without a standardized verification layer, they risk significant reputational damage. The industry must now pivot from simple AI adoption to the more difficult task of AI governance.