Enter: interpretable AI. Interpretable AI refers to artificial intelligence systems that are designed to provide clear and understandable explanations for their decisions and actions. Unlike "black box" AI models, which often deliver results without revealing the underlying reasoning, interpretable AI aims to make the decision-making process transparent and comprehensible to humans, which is vital in highly regulated industries such as financial services.
When it comes to processes like loan approvals, credit scoring, or customer interactions, interpretability plays an integral role in creating transparency, reducing bias, ensuring compliance, and enhancing overall model performance.
It’s one thing to understand the theory behind interpretability; it’s another thing to see it in action. That’s why we’ve put together this list of real-life examples to show the significance of interpretable AI and explore how it can impact various stakeholders at financial institutions, from customers to employees to regulators.
Example 1: Reducing Bias and Ensuring Fair Lending Practices
A bank employs an AI model to automate its loan approval process. Although the model is highly accurate in predicting loan defaults, it inadvertently penalizes certain demographics because it was trained on historical data that contains biases.
Interpretability Importance:
By making the AI model interpretable, the bank can understand how and why the model makes its predictions. This allows the bank to identify and correct biases inherent in the model, ensuring that loan approval decisions are fair and do not discriminate against any group unlawfully. This practice not only complies with regulations but also creates trust and transparency with customers.
Example 2: Meeting Regulatory Compliance and Audit Requirements
A fintech startup uses a complex AI model for credit scoring, which considers hundreds of variables. While the model performs excellently, its decisions are not transparent, making it difficult for the startup to explain the decision-making process to regulatory bodies.
Interpretability Importance:
Regulatory requirements, such as those outlined by the Fair Credit Reporting Act (FCRA) in the US or the General Data Protection Regulation (GDPR) in Europe, mandate that any decisions made by AI which affect consumers' rights or credit accessibility must be explainable. An interpretable AI model aids the fintech startup in providing the necessary documentation and rationale behind each credit decisioning process, thereby adhering to compliance and facilitating audit trails.
Example 3: Enhancing Customer Experience and Trust
A credit card company uses an AI model to adjust credit limits. A customer, whose credit limit was unexpectedly reduced, seeks an explanation. However, the complexity of the AI model used makes it challenging for customer service to provide a clear and understandable explanation.
Interpretability Importance:
Interpretable AI models equip the credit card company with the ability to offer transparent explanations to customers about how credit decisions are made, including any factors that led to a decrease in the credit limit. This level of transparency not only enhances customer experience by addressing concerns promptly but also strengthens trust, as customers feel treated fairly and with respect.
A financial institution relies on an AI model for mortgage approvals. Over time, the model's accuracy decreases, and the institution faces challenges in identifying the cause due to the model's opaque nature.
Interpretability Importance:
An interpretable model allows for the ongoing monitoring and analysis of which features are most influential in decision-making and how they’re being weighed. This detailed insight enables financial institutions to fine-tune the model for better performance, adjust to changing market conditions, and reduce instances of false positives or negatives in credit decisioning.
As AI continues to revolutionize the financial services landscape, the role of interpretable AI is more important than ever. Its potential to enhance transparency, mitigate bias, ensure regulatory compliance, and improve model performance are undeniable— and, as highlighted in these real-world examples, it impacts all stakeholders.
At nCino, we're not just observers of this transformative trend—we're active participants, playing our part in driving the AI revolution. Our innovative tool, Banking Advisor, powered by nIQ, harnesses the power of Generative AI, providing intuitive and interpretable models that boost accuracy and foster trust. Explore Banking Advisor today and see how this new tool is ushering in the future of the modern banker experience.
