The financial industry is undergoing big changes driven by advancements in the predictive capacity of statistical models. AI and ML development enhances data analysis, automates processes, enables new services, and improves customer experience. However, due to security and regulatory constraints, only a few areas of financial service software development can fully capitalize on these breakthroughs. Transparency, explainability, and strict control over data usage are essential—qualities that AI models, particularly complex ones like deep learning systems, often lack. Explainable AI in finance bridges the gap between the drive to lead in technological innovation and the necessity of maintaining integrity.
Essentially, explainable AI ensures that financial institutions (FIs) can trust and validate model outputs, reducing the likelihood of errors, biases, or unethical outcomes in sensitive applications like credit scoring, fraud detection, and risk assessment. Let’s take a closer look at explainable AI in finance—how it operates, why it’s crucial, and the steps needed to implement it effectively.
Why explainable AI is a necessity for your project success
AI and ML models learn through identifying patterns in data, but they don’t understand what those patterns mean—they simply find relationships, even nonsensical. Real-world data, being inherently noisy, often contains mathematical connections between unrelated variables—these are called spurious correlations. These are mathematical connections between variables that have no plausible causal relationship.
Spurious correlations can lead to models producing harmful or counterproductive outcomes. For instance, a loan approval model that, due to patterns in its training data, concludes that applicants applying on Tuesdays after lunch should always be rejected. While this pattern might exist in the data, it is a clearly nonsensical rule. If deployed, such a model would not only fail to serve its intended purpose but could also result in significant financial losses and reputational harm for the organization.
Stakeholders must be able to trust and understand why a model reaches a particular decision. This transparency is not only a best practice but is also increasingly required for compliance with regulations and maintaining public trust.
Find out more about implementing AI in credit risk management
Explainable AI in banking is a necessity for truly benefiting from predictive AI technology. Here’s how explainable AI tools help utilize AI’s full capacity while mitigating its shortfalls:
Protecting from bias
Many FIs initially believe that the best way to avoid bias in their AI model is to simply omit legally protected personal information such as gender and race. The logic is that if the model doesn’t have access to this information, it won’t be able to discriminate based on it. This approach is dangerous and can lead to major fallouts for two reasons: AI will infer potentially discriminatory information from other data, and your team will not know this.
For example, in credit scoring and approval, AI can use data about where customers shop to create a proxy variable for gender and race. Technically, these models do not explicitly discriminate based on gender; rather, they may discriminate against categories of spending commonly associated with specific genders or use zip codes as a substitute for race. Identifying such bias without direct access to the data is like looking for a needle in a haystack. There are countless variables that could serve as proxies.
This is why all potentially discriminatory variables should be included in the training data set, and the model should be routinely checked for bias. A simple correlation between a variable and model outputs can show if the model’s output correlates with gender, race, or other protected information. Implementing explainable AI in finance can protect the organization against major reputational risks.
Revealing feature importance
To learn how a model makes its decisions, it is important to understand which parameters it deems the most important—feature importance. One common approach is to conduct sensitivity analyses, where individual input values are systematically varied to see how the prediction changes. For a credit-scoring model, this might mean adjusting the debt-to-income ratio or lowering the reported monthly salary and observing any changes in the approval probability.
In fraud detection scenarios, feature importance might highlight that the time of day a transaction occurs, the device used, or the frequency of recent transactions are key drivers in predicting fraudulent activity. For example, a sensitivity analysis can be run by systematically varying the location of the transaction. If the model ascribes the highest fraud probability to transactions made from an unfamiliar country, this is the highest-weighed variable.
Another method involves using model-agnostic tools such as SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-Agnostic Explanations) to show how each feature contributes to an individual prediction. For example, a SHAP analysis might reveal that in a loan decision scenario, a slightly higher credit utilization rate increases the likelihood of rejection by a certain margin, while stable employment history has a strong positive influence on acceptance.
In all these “experiments” of explainable AI in finance, the goal is to reconstruct the model’s inner workings, thereby building confidence in and understanding of the model’s conclusions.
Read more: AI in Fintech: Use cases and best practices
Allowing decision traceability
One of the reasons standard AI models are impossible to explain is that there are no intermediary steps between the inputs and the final decision. An important practice of explainable AI is breaking the decision-making process down into elements. Instead of analyzing the case as a whole, a model would individually evaluate several parameters before transferring its outputs to a rule-based, transparent model.
In credit application processing, for example, an explainable model would evaluate annual income, outstanding debt, and employment stability one by one. The model might determine that the applicant has a high credit score and stable employment but that their outstanding debt is higher than typical for someone at their income level. Each of these judgments is recorded in a machine-readable format to be transferred to a transparent, rule-based model. The reasoning becomes traceable and justifiable. Instead of a vague refusal, the bank’s decision-maker can now point to a specific factor—excessive debt relative to income—and explain the outcome to the applicant or use it to inform a decision to request more financial details.
This approach leverages the strengths of AI and ML models while minimizing their weaknesses. AI excels at processing and analyzing vast amounts of unstructured data, such as transactions, unstandardized documents, and behavioral patterns. Using AI to process the data and estimate a single parameter is an ideal application of its capabilities, leaving minimal space for uncertainty.
Other statistical tools
In the upcoming EU guidelines for AI in banking, robustness will be considered alongside explainability. What is robustness, and how is it achieved? Robustness is the model’s ability to maintain accuracy when faced with new or unexpected inputs. In practice, this ensures that decisions remain consistent, reliable, and justifiable as market conditions shift or anomalies occur.
For example, monotonicity constraints are a technique that locks the direction of the correlations in the model. So, an increase in one input variable always leads to either an increase or decrease, and signs never change. These constraints help the model stick to that logic instead of making erratic predictions based on noise or quirks in the training data.
The tools that help data scientists achieve robustness are complex and highly abstract. What’s important is that not all AI models are equally robust, and there are methodologies under the umbrella of explainable AI that can improve them.
Partner with N-iX to partake in industry trends intelligently
Partnering with N-iX for your explainable AI in banking projects ensures you’ll work with a team that is deeply experienced in delivering AI and ML solutions tailored to the finance industry. N-iX offers AI consulting services that provide tailored solutions to automate complex processes and extract actionable insights from data, helping businesses achieve measurable improvements in efficiency and revenue growth.
With over 30 successfully delivered AI projects and a dedicated team of over 200 data experts, N-iX combines deep technical expertise with an understanding of industry regulations to create secure, efficient, and innovative solutions.
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