The Importance of Explainable AI (XAI) in Finance

For many financial institutions all over the world, AI currently represents a double-edged sword. On the one hand, advanced AI models have shown performance metrics that are hard to beat with legacy software, let alone manual processing. This is true for many different key banking processes, from advanced fraud detection, to smart automation and instant identity verification.

However, these models also face a major hurdle. Namely, with finance being one of the most heavily regulated industries in the world, many have displayed concern regarding the use of AI for decision making processes. To these ends, regulations such as the EU’s AI Act have put controls in place in regard to this new technology.

A key point in these regulations is explainability; that is to say, the ability for humans to understand how and why an AI model makes any given decision and to inform relevant parties of this information upon request (GDPR). 

Let's have a closer look at Explainable AI (XAI), its benefits, challenges, and potential solutions.

What is Explainable AI (XAI)?

Fundamentally, Explainable AI is a set of methods and techniques that aim to make AI decision-making transparent and understandable to humans. For example, in a loan decision, an XAI model might explicitly state that 40% of the decision weight came from the applicant’s debt-to-income ratio and 30% from credit history. So, those two are the main factors.

We can juxtapose this to an AI model that just states "your loan was denied", with no other context or reasoning. Or, for example, let’s say that you use GenAI to find information online. You will likely be able to see which sources the model used. However, understanding why it picked those specific sources and/or blog sections is a lot more opaque.

This is known as the black box problem and it can be especially damaging in the financial space. If an AI model denies your loan request but neither you nor even the bank are quite sure why, it can cause a loss of customer trust or even non-compliance (regarding regulations such as the US Equal Credit Opportunity Act).

In this same context, an XAI would have a clear and auditable path. So, the relationship manager or compliance officer would have some way of tracing back why your loan request was denied.

Which problems does Explainable AI solve?

In the context of the financial sector, and especially when it comes to decision making processes, black box AI models can cause issues with auditability, performance, and customer trust.

Auditability and compliance

Financial regulators require that decisions (especially high-stakes ones) can be reviewed and justified. With strict rules around consumer protection, anti-discrimination and fairness, banks must often explain how credit or risk decisions were made. XAI ensures there is a clear, auditable path for every decision.

For example, if a regulator questions a declined mortgage, XAI tools can produce a breakdown of factors (income, collateral, credit history) that influenced the model. And this audit trail is essential for future-proofing key processes.

Example: a recent Bank of England survey found that 81% of UK financial firms using AI already employ some form of explainability method (such as feature importance or SHAP values).

This shows many banks are proactively building audit trails into their AI. As more jurisdictions adopt AI-specific rules, having XAI will be essential for demonstrating compliance.

Performance and bias mitigation

Explainable models give engineers insight into how processes run, which helps improve overall quality. By seeing exactly which inputs influence a decision, data scientists can identify bottlenecks or data quality issues early on.

Critically in finance, this also uncovers hidden biases. For instance, if a credit risk model was trained on historical data, it might inadvertently penalise certain geographic areas or demographic groups. An XAI approach can reveal if “living in postal code X” is disproportionately lowering scores. Once identified, the team can adjust the model or data to correct unfair biases before any harm occurs.

In fact, industry reports emphasise that explainability is key to spotting such biases and aligning models with fairness requirements. By making an AI model’s logic transparent, XAI helps maintain high performance and ethical standards simultaneously.

Customer trust and transparency

According to recent polls, 84% of Europeans believe that in a business setting, AI requires careful management in order to maintain transparency. And when it comes to life changing processes, such as taking out a loan, many would likely feel uneasy with a black box AI having the final say. 

However, an explainable AI does alleviate some of these issues. Namely, since the decision-making process can be displayed, relationship managers can confidently inform their clients why their loan application was accepted or denied.

For example, they could tell a loan applicant that their request was declined because their debt ratio exceeded the bank’s threshold, based on the model’s rule. Providing such explanations helps customers feel the process was fair. In practice, this builds trust: clients are more likely to accept outcomes if they understand the rationale. In an environment where regulatory scrutiny and public expectations on AI are rising, offering transparent, explainable decisions can be a competitive advantage.

How to make AI models explainable?

Explainable AI models are made in two distinct ways. Firstly, AI models can be created with explainability in mind from the start. This typically means that the AI model is more linear and doesn’t utilise complex features such as deep learning. Secondly, existing AI models can be made explainable via certain features or approaches. These models can be more complex but the accuracy of the explanation generated can also be lower than in the former case.

Inherently explainable AI models

When creating an XAI, the design philosophy is typically to provide clear workflows. This can be achieved by having more linear decision paths, decision trees, or the introduction of an advanced rule engine.

For example, when performing loan origination, an XAI may have explicit rules for how to calculate risk level; such as “high-risk country domicile = high risk” or “domestic PEP = high risk”. While this is an oversimplification, it’s easy to see how such models can quickly be looked at to check why a given decision has been made.

While simpler models may trade off some predictive accuracy, they offer clear audit trails and easier debugging. Banks might use these inherently explainable models for core decisioning processes (like KYC or credit checks) where transparency is mandated.

Ad-hoc explainability

Alternatively, existing AI models can be “made explainable” via certain methods. The most common methodologies used are:

• Local Interpretable Model-agnostic Explanations (LIME): Changing input data slightly and recording how this affects the output.
• Shapley Additive Explanations (SHAP): Assigning “weight” to individual features in reference to how they impact the end result.
• Permutation Feature Importance: Shuffling one feature at a time and recording differences in model accuracy and decision-making.
• Layer-wise Relevance Propagation (LRP): For image processing, LRP can be used to see which parts of an image were focused on, helping identify the key features the AI model sought out.
• Mechanistic Interpretability: Reverse-engineering model internals into human-understandable circuits or concepts by dissecting neurons, probing activations, or tracing causal pathways.

These methods enable banks to “explain” an otherwise black-box model by attributing outputs to inputs. In practice, a team might train a sophisticated AI fraud detector, then use SHAP to generate a report showing exactly why each flagged transaction was deemed high risk. This report could be reviewed by a compliance officer. The more complex the model, the harder the explanations can be, but these techniques make it tractable. Many banks combine both approaches: use some inherently transparent models where possible, and apply post-hoc tools to the rest.

Atfinity’s AI-powered rule engine

Lastly, we'll take a look at what Explainable AI can look like, using the Atfinity Rule Engine as an example. In this case, it’s an inherently explainable model that was tailor-made to help banks automate key processes. It features an external ruleset coded in an intermediary language called RuLa, where the rules can be inferred from the end-result.

In practice, that means that you don’t have to manually create linear workflows or decision trees but rather just define the rules of a given process while the engine handles the rest. By doing it this way, our software can dynamically adapt to user input, much like most modern AI models.

However, since it’s all based on a viewable external ruleset, there is full transparency regarding how any given decision is made. 

We chose to approach explainability in this way because it does away with some of the challenges typically present.

Challenges of implementing explainable AI

AI is still a new development in finance. Explainable AI even more so. Therefore, there are many challenges and concerns that decision makers must take into account when opting for XAI. With that being said, depending on the business in question, their needs, the AI model they use and their approach to explainability, things tend to change.

However, these are generally the main challenges of implementing XAI.

Model complexity

If explainability is approached ad-hoc and the AI model in question utilises deep learning, it can be very difficult to implement explainability methods in a robust manner. This is due to the fact that deep learning models use a lot of data and many different parameters, making it difficult to pinpoint exactly which factors affected the decision in what way.

For example, if a model uses 500 borrower attributes, simply tweaking one value may not clearly show its importance. This makes ad-hoc explanations less precise. In complex finance use cases (like predicting market trends from multifaceted data), practitioners must work harder to interpret results. Often, banks mitigate this by using simpler surrogate models for explanation or focusing XAI efforts on the most critical variables.

Monitoring

As just touched on, evaluating the accuracy of explainability methods can be very difficult when dealing with deep learning models. Namely, even if our analysis shows that certain parameters are “getting a lot of attention”, how can we know whether they actually significantly affected the end decision?

Furthermore, there is no standardised measure for explainability. Therefore, it can be difficult to properly gauge whether a given XAI is robust enough to fulfil jurisdictional, business or individual requirements.

For this reason, firms are increasingly involving dedicated model governance and compliance teams to vet AI tools before and after deployment.

Accuracy and performance

Keeping an AI model simple and linear can directly lead to it being a lot more explainable. This also means that the AI model utilises less data and/or less complex algorithms and therefore can be less accurate.

For example, when performing Enhanced Due Diligence, a linear model might only consider necessary parameters, such as PEP and sanction lists, transaction histories, and country of origin. An advanced model on the other hand may perform deep adverse media screening tests, offering much deeper insights into potential risks that wouldn't be considered otherwise. For example, whether the person is currently being accused of financial crimes.

This presents a very notable challenge in finance for processes such as fraud detection or risk assessments, as both accuracy and explainability are crucial. However, a potential solution can be a hybrid approach, where high-performance “black-box” models are used where needed, but are surrounded with tools that interpret their outputs.

Legal and ethical considerations

Both legislation and public opinion regarding AI are still in flux. And while explainable AI was conceived as a method to both stay compliant with relevant regulations and build customer trust, neither is guaranteed.

As we’ve established, the world of XAI is still very wide and varied. Therefore, depending on the model and its complexity, as well as the explainability method used, both legal and ethical concerns may still be present.

Conclusion

Explainable AI can revolutionise finance in more ways than one. By harnessing the immense computational power of modern AI models and ensuring transparency, financial institutions can provide unparalleled services while also staying compliant and building customer trust.

However, there are still many uncertainties. Due to the many differences between AI models, explainability methods, use cases, and jurisdictional regulations, few things are set in stone. However, it’s a safe assumption that the financial world will keep traveling down this road, refining all the details along the way.