Escaping the Black Box: Why You Need Explainable AI in Finance

AI is showing up everywhere in finance. According to Planful’s 2025 survey, 60% of finance leaders use AI daily, and more than half of global teams already rely on AI-powered FP&A tools.

But here’s the problem: too many of those tools are black boxes. They give you an answer, but they hide the logic that led to it. And in finance, if you can’t explain a number, you can’t defend it, share it, or trust it. That lack of transparency erodes confidence, slows decisions, and pushes teams back to spreadsheets.

Explainable AI (XAI) changes that. It takes the mystery out of machine learning, showing how results were generated so finance teams can validate, adjust, and act with confidence.

You can also explore more by watching Planful AI Labs, a video series that dives into practical strategies and cutting-edge AI advancements to help finance teams elevate their planning and analysis.

Learn more: Explainability in AI: Why It Matters for Finance

​​Finance is built on trust and traceability. Every number in a forecast or report needs to be backed up, not just dropped in from a tool you can’t interrogate.

AI becomes a black box when you can’t see how it reached its conclusions. You enter data, and it returns an output, but what happened in between is unclear. The logic is hidden, which makes it difficult to trust, explain, or act on.

• Trust breaks down when teams can’t see the drivers behind forecasts.
• Decisions get delayed as stakeholders debate or redo numbers manually.
• Reporting suffers because finance can’t provide a clear context for executives or auditors.
• Adoption stalls as people default back to spreadsheets, undoing the gains AI was meant to provide.

The good news? There’s a fix. Explainable AI gives you the clarity, control, and trust you need to use AI with confidence.

Explainable AI is the difference between an output and an insight. For finance and accounting teams, that clarity is more than just a “nice-to-have.” It’s foundational to how finance works because it turns “what happened” into “why it happened,” giving leaders the confidence to act.

Explainability solves the biggest problems with black box systems by making AI’s decisions visible, traceable, and editable.

Here’s what explainability looks like in action:

• You can see how the model reached its conclusion. Drill into the logic behind any forecast, variance, or output to understand the “why,” not just the “what.”
• You can inspect and adjust the assumptions. Inputs like growth drivers or seasonal trends aren’t hidden; they’re editable. This allows you to run new scenarios or correct errors easily.
• You can follow the thinking step by step. With techniques like Chain-of-Thought reasoning, the model walks through its reasoning, just like a finance professional would.

Explainability is the foundation for trust when it comes to AI in finance. Without it, AI turns into a black box, and teams are left second-guessing results instead of acting on them.

How to spot AI that can’t be explained

Not all tools are created equal. Practical, hands-on features that provide visibility, control, and trust are what separate truly valuable tools from “black box” systems.

If you’re evaluating AI solutions for finance and accounting, watch for these red flags:

• “Magic answers” with no visible process. If a tool gives you a forecast but won’t show how it got there — or let you drill down to the source dimensions behind the data, calculations, and assumptions — you can’t validate or defend the outcome.
• Vendors who won’t show what’s under the hood. If a provider won’t walk you through how their models work, or avoids questions about logic and assumptions, that’s a red flag. True explainability requires openness, not obscurity.
• Locked-in assumptions you can’t change. AI tools must adapt to changing business realities. If the logic is rigid or the inputs are hardcoded, the model becomes a liability, not an asset.
• LLMs that generate unverifiable outputs. Large Language Models (LLMs) can be helpful for summaries or natural language queries. Still, if they’re used to generate forecasts or financial insights without clear data attribution or traceable calculations, the risk of errors (or hallucinations) is high.
• LLMs without financial acumen. LLMs are trained broadly on general text, not the specialized logic of Finance and Accounting. Without domain expertise, outputs can ignore financial principles, misinterpret terminology, or miss critical context — leading to inaccurate or misleading insights.

Here’s what to look in an AI solution for finance and accounting

It’s not surprising that finance leaders are already prioritizing explainability. In Planful’s 2025 survey, The Next Era of Finance, more than half of teams using AI-powered FP&A tools reported gains in forecast accuracy, strategic focus, and data transparency.

A best-in-class AI solution for finance and accounting will have:

• Transparent logic and data sources so you can drill into exactly how each number was generated.
• Configurable assumptions to test growth rates, seasonality, or cost drivers in real time.
• Human-in-the-loop workflows so finance can stay in the driver’s seat.
• Audit trails and data lineage to satisfy compliance and governance needs.
• Tailored outputs by role so executives see high-level trends while analysts get the details.
• Communicable insights that appear in familiar formats, such as tables, charts, and dashboards.

Explainable AI is the difference between tools that slow you down and tools that help finance lead with confidence.

At Planful, we didn’t add AI as an afterthought. We built it to augment how finance teams work. Explainability isn’t something we added on later; it’s the foundation for each solution.

Planful AI is embedded directly into finance workflows. It’s trained on finance-specific logic, aligned to real-world processes, and built to support human judgment, not replace it.

Whether you’re forecasting, answering a “why” question, analyzing trends, or preparing for a board meeting, Planful AI provides intelligence that’s fast, relevant, and ready to act on.

Here’s how it works:

• Analyst Assistant: Go beyond static responses. Receive instant answers as tables, charts, or summaries so insights are easy to understand and even easier to share.
• Help Assistant: Ask an in-app assistant to answer questions about the Planful system, including explanations of Planful’s capabilities.
• Signals: Automatically flag anomalies, categorize risks, and prioritize what matters, with customizable alerts and thresholds.
• Projections: Build intelligent forecasts using driver-based templates, guardrails, and scenario planning that’s quick to adapt and easy to trust.

Because Planful AI is built for finance and accounting, every output is explainable, auditable, and grounded in the context your business needs. And with role-based permissions and all data kept securely within Planful, you can trust that transparency never comes at the expense of control.

Learn more: Meet Planful AI

• Explainable AI eliminates the “black box” by making forecasts and insights transparent and defensible.
• Explainability bridges the gap between AI outputs and business decisions, giving leaders the clarity to act with confidence.
• Planful AI was designed with explainability at its core: It provides step-by-step reasoning and data lineage to ensure every output is clear, actionable, and defensible.

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How does explainable AI improve audit readiness for finance teams?

Explainable AI provides a complete data lineage and audit trail for every forecast, variance, and scenario analysis. This makes it easier for finance teams to validate results, answer auditor queries, and ensure compliance with internal controls and external regulations.

How can explainable AI accelerate financial planning cycles?

By making calculations transparent, explainable AI reduces time spent on data validation and rework. The ability to make assumptions editable means finance teams can quickly adjust scenarios, respond to stakeholder questions, and finalize plans more efficiently.

How does human-in-the-loop AI enhance explainable AI in finance?

Human-in-the-loop AI ensures that finance professionals remain actively involved in reviewing, validating, and adjusting AI outputs. Explainable AI provides the transparency needed to understand every assumption and calculation. At the same time, human-in-the-loop workflows enable teams to apply their expertise, make real-time adjustments, and ensure that decisions align with business strategy and compliance requirements.

How does Planful AI ensure explainability?

Planful AI uses finance-specific models, validates every output with Planful’s calculation engine, and shows step-by-step reasoning so every number can be trusted and defended.