As AI takes on a larger role in finance workflows, finance teams need greater visibility into how AI-supported outputs and recommendations are generated.
By making AI outputs easier to understand and evaluate, explainable AI (XAI) can help finance teams build trust, support regulatory compliance, and maintain human oversight.
In this guide, you’ll learn the meaning of explainable AI in finance, the techniques involved, and what it can look like in accounts payable workflows.
Key Takeaways
- Explainable AI (XAI) can help make AI-driven outputs easier for humans to understand, evaluate, and audit across finance workflows.
- Clear explanations of how AI models make decisions are critical for regulatory compliance and for establishing trust with financial stakeholders and customers.
- XAI in accounts payable (AP) automation can help finance teams optimize workflows while maintaining transparency and control.
- To implement explainable AI, finance leaders should look for AI solutions that provide audience-relevant, traceable outputs, along with predefined rules and contestability features to support human oversight.
What is explainable AI (XAI) in finance?
Explainable AI (XAI) refers to techniques and systems designed to make AI outputs and the factors influencing them easier for humans to understand and evaluate.
Unlike traditional “black-box” AI, where algorithms ingest data and produce outputs with hidden calculations, XAI reveals the why and how behind decisions without necessarily exposing a model’s internal math.
It allows humans to understand and audit results, ultimately making them more trustworthy.
The distinction between the two is critical in AI for finance, where processes must be transparent and auditable to meet regulatory requirements. Read more about the specific role of AI in accounts payable (AP) in Tipalti’s dedicated blog post.
Here’s a simple way to see the difference. An AI model flags a supplier invoice as high risk and holds it for review:
- Black-box output. Invoice #1048 from supplier X flagged for manual review.
- XAI output. Invoice #1048 from supplier X flagged for manual review. Reason: The invoice unit price ($100) is 25% higher than the PO price ($80), exceeding the configured 5% tolerance. Review the source documents before resolving the exception.
The XAI output provides the accounts payable team with actionable information. A payables manager can use the triggers to verify line items and contact the supplier to clarify totals. The payee can then adjust the invoice, while the company retains a complete record of the explanation in the audit trail.
Note: Explainability, auditability, and transparency are related but distinct terms. While an audit trail shows what happened, explainability helps someone understand why an output occurred. Transparency can cover broader visibility into the data, system use, and limitations behind it.
Explainability is becoming increasingly essential in accounts payable (AP) and mass payout automation workflows, but it’s already firmly established in many areas of finance, including:
- Anti-money laundering (AML) and fraud detection, to trace which patterns or transactions triggered an alert (e.g., unexpected velocity or proximity to sanctions lists).
- Credit underwriting to show explicit reasons why applications are approved or denied. It helps lenders comply with fair lending regulations and provide actionable feedback to applicants.
- Insurance pricing to show which risk factors drive up premiums and to rule out demographic biases.
- Investment management to explain which indicators drive trades or allocations, ensuring alignment and risk control.
By shifting from simple predictive outputs to transparent, evidence-backed decision factors, explainable AI finance applications turn a potential risk factor into a trusted assistant that improves efficiency and compliance across your financial workflows.
That said, explainability can never replace human judgment. Rather, it can provide clear, data-backed information that enables finance teams to approve or challenge AI-supported outcomes.
Why does explainable AI in finance matter?
Explainable AI is crucial to ensuring outcomes are clear and auditable for every stakeholder.
In finance, any unexplained margin of error is a significant operational and compliance risk. If a finance leader cannot trace an algorithm’s conclusion, they cannot defend decisions to auditors, customers, or regulatory authorities.
By addressing the black-box problem, XAI may benefit finance departments in 3 ways.
1) Regulatory compliance across global jurisdictions
To meet global regulatory requirements, finance teams must ensure machine learning models meet an evolving patchwork of frameworks.
An XAI research report by the CFA Institute highlights several regulatory frameworks relevant to explainability and AI governance in finance:
- The EU AI Act. The Act classifies certain AI uses as high risk, including systems used to evaluate the creditworthiness or credit score of natural persons and systems used for risk assessment and pricing in life and health insurance. High-risk systems are subject to requirements around areas such as documentation, transparency and human oversight.
- GDPR and automated decision-making. GDPR provides protections related to certain decisions based solely on automated processing that produce legal or similarly significant effects. Depending on the circumstances, individuals may have rights to information about automated decision-making and the logic involved, as well as human intervention and the ability to contest a decision.
- US Equal Credit Opportunity Act (ECOA). In credit decisions, lenders must provide specific reasons when taking certain adverse actions. When AI or complex models contribute to these decisions, lenders still need to provide reasons that accurately reflect the factors behind the adverse action.
Note: In practice, the EU AI Act’s high-risk classification specifically covers credit scoring and insurance pricing, not AP or fraud detection. GDPR also gives individuals the right to human review, not necessarily a full technical explanation.
In the UK, regulators are also considering how existing regulatory frameworks apply to AI. The Financial Conduct Authority (FCA), for example, has emphasized the importance of areas such as accountability, governance, controls, and consumer outcomes when firms use AI.
If you operate across jurisdictions, check which AI rules apply to each use case, since obligations depend on where and how a system is used.
2) Ethical fairness and bias mitigation
Machine learning models learn from historical data. If your data contains human biases or system inefficiencies, a black-box model can automate and amplify them without explanation.
This results in “proxy variables” that unintentionally discriminate based on factors such as demographics or geographic location.
In insurance pricing, for example, this might show up as wrongly penalizing applicants for combinations such as zip code or account age. But it can also be a problem in AP, with new global suppliers labeled as high risk for using a non-standard invoice layout, or having an overseas bank.
XAI surfaces these hidden biases by explaining the outcome behind each flag. It helps keep machine predictions fair and auditable throughout your payment cycle.
3) Institutional and customer trust
By pinpointing behavioral anomalies, XAI helps finance professionals distinguish real threats from false alarms at high volume. It enables them to maintain trust without compromising security.
The ability to trust AI models is a crucial factor inside and outside of your company.
- Internal trust. Leaders and audit teams may not approve full financial automation using a black-box approach, which increases regulatory risk.
- External trust. Suppliers and customers expect fast, clear decisions on why a payment is delayed or held. Ambiguity can lead to complaints or churn.
By grounding daily decisions—such as invoice approvals or suspect transfer flagging—in clear, traceable logic, XAI makes machine learning model decisions easier for stakeholders to interpret.
How explainable AI works: 4 common XAI techniques
AI systems typically use 4 common explanation techniques to help humans understand XAI decisions: SHAP, LIME, counterfactual explanations, and partial dependence plots.
The right method depends on the model and decision, and some finance use cases may benefit from combining multiple techniques.
A finance team doesn’t need to write the algorithms to extract explanations, but it can be useful to understand them when evaluating AI tools and presenting audit trails.
Here’s a closer look at each technique, including where it’s best used, and its pros and cons.
1) SHAP
SHAP (Shapley Additive Explanations) borrows from game theory, calculating the precise attribution each feature or variable makes towards the model’s output.
It differs slightly depending on whether the approach is based on local or global interpretations.
As a ScienceDirect XAI paper describes, SHAP works at two levels:
- Local interpretability. SHAP assigns each feature a value for every individual prediction, showing how much each factor pushed that result up or down
- Global interpretability. Combined across many predictions, SHAP values show which factors most influence the model’s outputs, and in which direction.
Say your AI flags an invoice. Local interpretability indicates that the invoice was flagged due to a $4,000 variance in line-item pricing. Global interpretability shows finance leaders that price variances are the single biggest driver of processing delays.
Both approaches are valuable for improving workflows and eliminating bottlenecks.
| Best for | Risk scoring, general ledger (GL) coding, fraud detection, and credit underwriting. |
| Pros | Mathematically consistent. Provides macro- and micro-level views of decision breakdowns. |
| Cons | Can be computationally expensive on large or complex datasets. Outputs need to be translated into plain language for non-technical stakeholders. |
2) LIME
While SHAP can explain both single predictions and overall model behavior, LIME (Local Interpretable Model-Agnostic Explanations) zooms in on a single specific decision to explain the prediction. It creates an interpretable surrogate model, like a decision tree or linear regression, that mirrors the complex model’s behavior for a single data point.
For example, an AI system flags a supplier payment of $40,000 as high risk. LIME produces a local decision tree around that transaction.
By isolating the variables that drive the single flag, it shows that a new currency payout and out-of-hours submission triggered the alert.
Finance teams can use these results to understand and solve issues without unpicking the complex algorithm behind them.
| Best for | Individual transaction reviews, vendor risk alerts, and real-time approval flags. |
| Pros | Fast explanations. Works on any machine learning architecture, including models too complex for SHAP to run efficiently. |
| Cons | Explanations are strictly local. Minor data point variations can yield different explanations for identical transactions. |
3) Counterfactual explanations
Rather than explain what drove an AI model’s decision, counterfactual explanations ask: what minimum change is required to produce a different result?
For example, if an invoice fails due to a line-item variance, a counterfactual explanation might show: “If the line-item rate matched the purchase order (PO) rate within a 5% tolerance window, the invoice would have auto-approved.”
It gives finance teams fast, actionable guidance to resolve the delay and process the payment.
Suppliers can also get a clear explanation of why a payment didn’t go through. Where GDPR applies to an automated decision about an individual, this kind of explanation can help provide “meaningful information” about the logic involved.
| Best for | Invoice approval exception handling, vendor onboarding compliance, and adverse action feedback. |
| Pros | Actionable and easy to understand. |
| Cons | Doesn’t provide a full picture of a model’s logic. |
4) Partial dependence plots
Partial Dependence Plots (PDPs) show the marginal effect that one or two factors have on the model’s predicted outcome.
They demonstrate whether the relationship between an input feature (e.g., invoice age or payment velocity) and the output score is linear, exponential, or multi-variable by holding all other variables constant.
For example, a PDP might show how a model’s predicted invoice risk score changes as invoice amounts increase, holding factors such as vendor history, payment method, and approval flow constant.
Finance controllers can use this technique to ensure a system’s logic aligns with internal risk policy. Teams can confirm risk scores rise predictably alongside invoice amounts and flag unnatural spikes or drops for investigation.
| Best for | Bias auditing, risk governance, and macro-level model validation. |
| Pros | Valuable for spotting model patterns or errors. Easy for auditors to interpret visually. |
| Cons | Doesn’t show why the model made a specific decision (e.g., an invoice was flagged or approved). Other methods are required when an auditor or supplier wants a pinpoint answer. |
Why agentic AI workflows need greater transparency
Traditional XAI techniques can help explain individual model predictions. However, techniques such as LIME and SHAP, on their own, don’t provide visibility into an entire multi-step agentic workflow.
Note: Generative AI describes systems that generate content and other outputs. Agentic AI drives systems that can pursue tasks through actions or workflows, while XAI concerns people’s ability to understand and evaluate AI-supported actions.
Rather than provide static risk scores or numerical classifications as outputs, AI agents built on large language models (LLMs) can parse unstructured documents, follow multi-step logic, and take actions across multiple tasks. For example, agents can work together to perform tasks such as matching POs and initiating approvals.
As AI moves from producing outputs to recommending or taking action across multi-step workflows, finance teams increasingly need visibility into the information that informs those actions.
In an agentic workflow, auditability means a reviewer can see what information each step used, what the agent did or recommended, and where a person reviewed or approved the result. However, it’s important to understand how agentic AI finance software makes decisions as you move from basic automation to full AI-assisted workflows. Our guide to AI agents in finance explains how agentic AI is supporting core finance processes.
What finance teams should look for in explainable AI
Any AI-driven finance solution that automates supplier onboarding and payment flows must meet operational, legal, and audit standards to avoid risk and strengthen financial control.
While there are currently no universal benchmarks for assessing the quality of AI explanations, there are elements you can look for to ensure that an XAI model prioritizes transparency and auditability.
Here are 7 core capabilities and why they matter to your finance team.
| XAI Capability | What It Means | Why It Matters |
|---|---|---|
| 1) Traceability | The system logs exact source data, rules, or factors behind every decision. | Controllers can trace automated outputs back to specific triggers. |
| 2) Robust audit trails | Automated decisions, confidence scores, and human interventions are logged for review. | Gives internal and external auditors a comprehensive paper trail to prove compliance. |
| 3) Audience-relevant output | Explanations are in plain language rather than code logs or mathematical weights. | Stakeholders get clear, actionable rationale. |
| 4) Contestability | Finance professionals can review, challenge, or override AI decisions. | Keeps humans in control of the final decision, with a clear record of why each override or approval was made. |
| 5) Consistency | The system supports consistent explanations for comparable inputs and outcomes. | Keeps similar actions (e.g., vendor invoices) aligned to the same logic. |
| 6) Predetermined rules | AI models apply rule-based guardrails if confidence falls below a set threshold. | Helps edge cases default to set business rules and approvals, avoiding guesswork. |
| 7) Regulatory alignment | Explanations are designed to support applicable regulatory requirements in your jurisdictions. | Methods can hold up under regulatory scrutiny. |
A solution that meets these criteria can improve financial efficiency while supporting compliance and audit-readiness.
Map your path to AI-powered accounts payable
Build confidence in AI with a practical checklist to prepare your AP team for agentic AI while maintaining oversight and control.
How explainable AI can apply to accounts payable
In accounts payable, explainability can help finance teams understand why an invoice was flagged or an approval route was suggested. In AI-supported invoice processing, useful explanations connect the output to relevant information, giving reviewers a basis for verifying it and deciding what to do next.
Explaining an invoice discrepancy
During PO matching, including 3-way matching, an explanation could identify the discrepancy behind a flag:
“The invoiced unit price is $100, compared with $80 on the purchase order—a 25% variance that exceeds the configured 5% tolerance.”
With the discrepancy clearly identified, an AP reviewer can check the source documents and determine whether the invoice needs correction or an approved exception.
Explaining a suggested approval route
An explanation could also show the information behind an AI-suggested approver:
“The suggested approver manages the department associated with this invoice and has approved similar expenses.”
The reviewer can assess whether those factors support the recommendation and confirm or change the route, subject to the company’s approval policies.
Tipalti uses specialized agents across AP, including the Invoice Capture Agent, PO Matching Agent, Bill Approvers Agent, and Tax Form Scan Agent. Explore Tipalti’s finance AI agents to learn more about their roles and how they support finance teams.
How explainable AI can support audit trails and compliance in AP
AI predictions are only as good as their ability to hold up under audit or regulatory scrutiny. Explainable AI applications in finance help strengthen audit readiness by keeping automated actions clear and defensible across 3 key AP workflows:
| AP Workflow | AI Decision Points | Examples of What Explainability Could Provide |
|---|---|---|
| GL (general ledger) coding | Predicting expense categories, tax codes, and department allocations. | Explainability could provide context around the factors that influenced a suggested classification, such as relevant invoice data or coding patterns. |
| Fraud scoring | Assigns risk scores to incoming bills and payment requests in real-time. | Explainability could highlight factors that may have contributed to a risk score or flag, giving reviewers more context when assessing a transaction. |
| Sanctions and compliance screening | Cross-references payees against global watch lists. | Explainability could provide context around the factors that contributed to a potential match, helping reviewers assess the result. |
While traditional financial audits rely on testing a sample of transactions after the end of a period, XAI can make automated decisions easier to review in real time, not only at period end.
It gives finance controllers a year-round, defensible digital record to support compliance for auditors while reducing time-consuming manual audit prep.
XAI is the foundation for trusted finance automation
As machine learning models become a core part of your finance function, explainable AI for finance helps bridge the gap between agentic automation and operational trust.
Implementing XAI principles helps replace unverified outputs with explanations reviewers can check, supporting governance and compliance at scale.
It lets AI assistance amplify human insight, allowing reviewers to better optimize workflows, communicate outputs to stakeholders, and stay audit-ready.
Understanding explainability is the first step of successful agentic AI implementation. To learn how to turn AI into a reliable extension of your team without compromising internal controls, download Tipalti’s Finance Leader’s Guide to Operationalizing AI.
Explainable AI in finance FAQs
How can explainable AI build trust?
Explainable AI builds trust by making machine learning decisions transparent and verifiable. Seeing the evidence behind output decisions gives finance teams confidence to implement high-volume workflows without compromising control.
It also makes AI more transparent to stakeholders and customers, helping them understand outcomes and trust that decisions are based on logic rather than bias.
What’s the difference between explainable AI and interpretable AI?
Interpretable AI means the model is simple enough for humans to understand, such as a basic decision tree. Explainable AI uses separate techniques such as SHAP and LIME to unpack and explain the logic of complex “black-box” algorithms.
Both methods can be valuable in finance. Interpretable AI offers instant clarity in routine workflows, while XAI enables teams to deploy advanced AI models without sacrificing control or auditability.
How does the EU AI Act affect financial AI explainability?
The EU AI Act classifies certain financial AI applications as high risk, including systems used to evaluate the creditworthiness or credit score of natural persons and systems used for risk assessment and pricing in life and health insurance. These high-risk systems are subject to requirements that include transparency, documentation and human oversight.
The requirements depend on the AI system and its intended use, so the Act should not be read as classifying all AI used in finance as high risk.
How can finance teams balance model accuracy with regulatory accountability?
Pair highly accurate AI models with post-hoc XAI techniques like SHAP, LIME, or counterfactual explanations. Back these with rule-engine safety nets to improve financial efficiency while helping keep outputs auditable and aligned with regulatory requirements.
How does explainable AI work in credit decisions?
XAI breaks down how applicant data, including payment history, credit history, and debt-to-income ratios, contribute to an approval, denial, or a set interest rate. If an AI model contributes to a rejection, XAI helps the lender identify the specific reasons it must include in the adverse action notice.
What is explainable AI in investment management?
Investment models use XAI to clarify why algorithms recommend a trade, allocation, or portfolio adjustment. It explains the primary risk factors, market drivers, and economic data points, enabling portfolio managers to justify decisions to stakeholders and regulators.
