AI is transforming how financial institutions detect fraud and money laundering but with growing regulatory scrutiny, a critical question is surfacing: Can you explain the decisions your AI is making?
As scrutiny intensifies, especially in anti-money laundering (AML) and fraud detection, financial institutions are under intense pressure to ensure transparency, accountability, and traceability in every automated action.
While many vendors boast about their “AI-powered” platforms, few can deliver the level of explainability regulators demand. It’s no longer enough to say you use AI; you must prove how it works, why it makes the decisions it makes, and that it aligns with policy and risk controls.
In this blog, I explore why explainability is becoming a non-negotiable standard in regulated environments and what to look for in a truly compliant AI solution.
The Problem with Black-Box Systems
In 2024 alone, multiple banks faced regulatory inquiries after failing to explain how automated systems flagged suspicious activity.
As AI models grow in complexity, they become harder to interpret. While early-stage models such as decision trees offered some transparency, more advanced algorithms often function as “black boxes,” where the reasoning behind a prediction is obscured. In many use cases, that doesn’t really matter, because if a model predicts what it’s supposed to predict with increasing accuracy, the business impact is generally positive, even if no one fully understands how it works. But in regulated environments such as fraud detection and anti-money laundering (AML), explainability isn’t optional.
While some vendors offer advanced AI-powered tools, they may still operate with limited transparency, making it difficult for financial institutions to understand or justify the system’s decisions. That’s a problem when those decisions have serious legal or financial consequences.
Imagine an individual being flagged for fraud by an algorithm. Without a clear, auditable explanation of how that determination was made, financial institutions can’t defend the action to regulators, or to the accused. Worse yet, if a model’s decisions are later found to be influenced by sensitive attributes such as race or gender, the fallout could be severe.
In high-stakes domains, accuracy isn’t enough. You must be able to demonstrate that your AI model is also fair, transparent, and compliant.
Explainability vs. Transparency: Both are Essential - But Not the Same
While often used interchangeably, transparency and explainability represent two distinct requirements in AI governance. This is especially true in regulated industries.
Transparency refers to the ability to see how a model is built and maintained: its structure, input features, update cadence, and overall behavior. Some vendors provide visibility into these components through dashboards and documentation, so customers have a high-level understanding of how the system works.
Explainability is more granular. It refers to the ability to trace why a specific decision or prediction was made. In the context of fraud detection or AML, this means knowing what triggered the alert, which data points influenced the decision, and whether the outcome was biased or misinformed.
The distinction between transparency and explainability matters. A vendor may offer transparency through user-friendly interfaces or configurable risk thresholds, but that doesn’t mean their model is explainable.
For example, a bank might see that a transaction was flagged and certain risk rules were applied, but may still be unable to identify the logic that the AI used to identify a high-risk customer.
True explainability means that every prediction is traceable, defensible, and free from hidden bias, and it’s a critical capability when regulatory compliance and customer trust are at stake.
Regulatory Expectations Around Explainable AI
Regulators around the globe are honing in on the use of AI in financial decision-making. As a result, explainability is quickly becoming non-negotiable.
In the U.S., agencies such as the OCC and FINCEN have issued guidance emphasizing the importance of model governance, fairness, and auditability in AI-driven systems. On a global scale, the Financial Action Task Force (FATF) and the European Banking Authority (EBA) have echoed these concerns, calling for responsible AI practices that prioritize transparency and accountability.
Regulatory bodies expect financial institutions to demonstrate auditable logic, traceable decision-making, and consistent outputs across all AI-assisted processes. They must be able to explain clearly to regulators how a flagged transaction or customer profile triggered an alert and justify that outcome using clear, non-discriminatory reasoning.
What Real Explainability Looks Like
To support compliance and analyst workflows, explainability must be built into the detections lifecycle, not bolted on after the fact. Here are some core features to look for in a truly explainable system:
- Human-readable rules and annotations: Every alert should be accompanied by plain-language logic explaining the key risk factors, thresholds, or behaviors that triggered it. This way, analysts and auditors can quickly understand and validate the reasoning behind a decision.
- Alert traceability: Teams need to be able to drill down into each alert to analyze the data inputs and understand which model or rule triggered a response, as well as the logic that led to the outcome. This insight speeds up investigations and supports regulatory reporting.
- Version control and audit logs: Any changes to detection logic, thresholds, or model behavior should be recorded in a standardized way, creating reliable logs that help institutions prove due diligence and pass compliance reviews.
- Configurable thresholds and parameters: Explainable systems should enable teams to adjust sensitivity levels, scenario models, and escalation rules based on their unique risk profile or regulatory environment.
Without these capabilities, even the most accurate AI systems remain indefensible. Explainability isn’t a feature, it’s a prerequisite for trust and compliance.
Choosing the Right Partner for Explainability
As regulatory expectations intensify, financial institutions must ensure their technology partners meet evolving standards for explainability. To that end, evaluations of AI-powered fraud and AML solutions should put explainability front and center.
Financial institutions that buy into flashy AI claims without asking hard questions about visibility, traceability, and human oversight put themselves at serious risk.
Make sure you ask the right questions, including:
- Does the solution provide clear, human-readable reasoning for each decision?
- Do you have control over detection logic, thresholds, and changes?
- Can you trace every alert back to its source logic for audit and regulatory reviews.
True compliance means more than using AI – it requires AI that supports human understanding, allows for investigation and documentation, and empowers teams to defend and adjust decisions as needed.
Compliance Starts with Clarity
If a system can’t explain itself, it can’t protect your organization. The right platform will treat explainability not as an afterthought but as a core design principle. It should provide transparent, configurable tools that empower your team to meet regulatory demands and continuously improve model performance over time.
As regulators raise the bar, explainability must rise with it. Institutions that embrace transparency and control in their AI systems will remain compliant while building trust with customers and regulators alike.
