SPONSORED CONTENT PRESENTED BY nCino
The one constant in the financial industry landscape will always be change. When it comes to credit decisioning, financial organizations who are striving to be innovative, forward-thinking, and truly progressive are no longer content with standard practices and processes. By aiming to evolve from purely transaction-based operations to more curated experience-driven services, these institutions can honor their dedication and commitment to providing their clients with personalized, efficient, and highly secure financial services.
The key instrument aiding in this transformation is the strategic application of artificial intelligence (AI) and machine learning (ML).
Transforming Credit Decision-Making with AI and ML
Artificial Intelligence (AI) and Machine Learning (ML), although often used synonymously, are distinctly different, and hold the potential to revolutionize the way financial institutions make credit decisions.
While AI denotes the ability of a machine to emulate human intelligence by learning from experiences, adapting to new data, and executing tasks—some basic, others extremely complex—that usually necessitate human brainpower, ML is a part of AI that utilizes algorithms to let systems learn from data, progressively improve, and make knowledgeable decisions without being explicitly programmed.
Artificial Intelligence and Machine Learning, when combined with the capabilities of cloud computing, arm financial institutions with insights that allow them to offer customized services to their clients, streamline operations, and create significant enhancements in efficiency, risk management, customer experience, and decision-making abilities.
Redefining Credit Decisioning with Innovation and Transparency
Traditionally, the practice of lending was straightforward: the likelihood of being approved for a loan was dependent solely on a borrower’s credit score. If a credit score was high, so in turn were the chances of being approved for a loan. Introducing AI into borrowing and lending models, however, allow for the basis of a loan to depend on many real-time data factors, rather than on a credit score alone. Allowing for more accurate insights into a borrower’s data, credit history, and circumstances in turn allows for a more evolved and improved solution. Leveraging AI in financial decision-making is no longer just about automating and simplifying workflows. Instead, it’s also crucial to maintain transparency, accountability, and human relevance to these processes.
While AI tools are powerful, they’re not without flaws. Given the high stakes that characterize the financial industry, it’s crucial to be aware of the possible shortcomings within this technology as we progress towards an AI-driven revolution.
Ethical considerations cannot be overlooked when dealing with AI. Mark Douchette, Data and AI Leader, highlights the importance of being cognizant of some red flags. He explains, “Models built on historical data will make the same historical mistakes based on biases that existed then. As we build these models, we must take precautions with the same set to ensure it’s fair and equal. That can be tough, but it’s necessary.”
Predictive AI in Credit Decisioning
In the current technological landscape, artificial intelligence is significantly reshaping traditional financial services such as credit decisioning and assessing risk. This transformation marks a shift from traditional statistical models to more innovative, AI-powered approaches and represents a quantum leap in the industry. AI, particularly predictive AI, essentially represents a substantial progression over traditional models and signifies a transformative shift in how data is analyzed and utilized.
Lending tools that utilize Predictive AI allow financial institutions access to real-time data and metrics to provide more clarity, along with additional factors that inform the overall decision. Rather than relying on, for example, a borrower’s outdated financial records and statements, financial institutions can instead analyze recent and relevant transactions and data to gain a more proactive, rather than reactive, approach to assessing a decision’s financial risk.
Sharper precision, improved accuracy, and a proactive approach allows lenders to promote financial inclusivity by enabling them to offer more credit extensions based on a better understanding of the risk associated with individuals and businesses alike. Leveraging AI and ML in these processes allows for algorithms that can assess and understand borrowing behaviors. This enhanced speed in credit decisioning allows financial institutions to adopt a proactive approach to risk assessment and credit decisioning.
Why Explainable AI is So Important
The incorporation of AI and machine learning has significantly transformed the way financial institutions assess and manage risk, ushering in increased efficiency and innovation. However, it’s still important for lenders to be able to explain their decisions and the results and insights gained from the analyzed data. Not only do lenders need to be able to understand why a decision was made, they also need to be able to clearly and effectively communicate the decision to their clients. Producing results without explanations of how or why isn’t necessarily helpful or effective for lenders when it comes to communicating a decision back to the borrower.
While revolutionary to the financial industry landscape, it’s important to note that some AI technologies, such as generative AI (Gen AI), are considered rather enigmatic due to their complexity. Gen AI has the ability to swiftly draw from billions of data points to accomplish a specific task. While it is theoretically possible to understand this process, the sheer scale and speed involved often make it an intimidating prospect. This complexity is the main reason why AI sometimes functions as a “black box”, providing outcomes without clearly indicating the reasoning behind them.
This ongoing transformation emphasizes the cruciality of explainability—the ability to coherently communicate the decision-making process behind AI and comprehend the underlying mechanics of the model. Essentially, this pivotal shift symbolizes the dawn of a groundbreaking and innovative era within the financial services industry. As such, the need to understand the inner workings of AI systems has grown immensely.
Chris Gufford, Executive Director – Commercial Lending at nCino, explains the similarity in explainable AI to the transparency required in traditional banking models: “Both center on clear communication of inputs and outputs. Within the model development cycle and data interpretation, explainability is essential for maintaining trust and understanding. At its heart, explainability is about achieving this transparency, regardless of the advanced nature of the AI or the mathematical complexity of the models.”
In the early stages, when users were predominantly interested in the precision of the predictions, the necessity for explainability seemed of lesser importance. However, as AI continues to evolve and infiltrate various sectors, including heavily regulated ones like financial services, the need to truly understand the inner workings of AI has increased significantly.
What differentiates AI models from conventional statistical models in the sphere of credit decisioning is their superior accuracy and proactive nature. Improved precision enables lenders to promote financial inclusivity by extending credit opportunities to a larger number of worthy businesses and individuals.
The need for explainability will only grow as predictive AI continues evolving. Explainability not only instills trust and accountability in AI systems but also improves regulatory compliance and facilitates ongoing model refinement and optimization. It empowers stakeholders to make informed decisions based on AI outputs and fosters a deeper understanding and acceptance of AI technology across various domains.
Companies like nCino maintain explainability through an approach known as “human in the loop.” This concept involves integrating human expertise throughout the development, deployment, and execution of the AI model. Human experts, like domain specialists or risk analysts, can provide valuable insights into the data, model assumptions, and business context—all crucial elements for understanding and interpreting the model’s outputs. Additionally, this approach enables experts to monitor AI behavior and intervene if necessary to prevent and addresses any ethical concerns, such as bias or fairness.
“Financial institutions can ensure explainability in their AI models through several key practices,” says Gordon Campbell, CCO and Co-Founder of Rich Data Co (RDC). “First, they can adopt transparent and interpretable AI techniques, which provide clear insights into how the model arrives at its decisions. Additionally, employing techniques such as feature importance analysis or sensitivity analysis can help identify which features have the most significant impact on the model’s outputs, enhancing its explainability.”
Interpretable AI in Financial Services
While explainable AI focuses on the ability to communicate the reasoning behind decisions, interpretable AI seeks to make the inner workings of AI models understandable to humans. Ideally, lenders should be able achieve a balance where AI models are used thoughtfully with full consideration of both the lender and the borrower. Regardless of what methodology a financial institution uses for interpretability, selecting a method that is interpretable and understandable is paramount. Interpretability in AI models, especially those used for credit decisioning, is not just a technical requirement but a necessity to support fairness, regulatory compliance, and to build trust with both customers and regulatory bodies.
“The goal of explainable AI is not to remove or bypass the human interaction, it’s to enhance and augment the human and support their decision,” says Peter Fabbri, Master Product Manager – AI Solutions at nCino. “It gets the decision in front of the banker and their customer sooner. That additional lead time means more time to react and ultimately build a stronger credit portfolio.”
Beginning Your Credit Decisioning Transformation
While it may seem daunting or overwhelming to begin infusing AI into processes and workflows, the key to leveraging AI is starting small, concentrating on specific use cases, and focusing on particular data elements.
“Starting small is viable, given the strategy we’ve developed,” says Fabbri. “Over the long term, AI will revolutionize the banking industry. We’ve seen impressive advancements in AI technology recently allowing us to finally realize our visions. The time is now to leverage these transformative tools.”
By starting with targeted, small-scale AI-driven projects, you allow not only for better data quality management, but also for easier impact measurement and results. These improvements will eventually pave the way for you to expand AI implementation for a broader, more sophisticated AI strategy.
“When leveraging AI in lending decisions, financial institutions must take a balanced approach,” advises Gufford. “The wise strategy is to implement AI models while still maintaining traditional methods, essentially operating these systems simultaneously to measure and discern the variations in results. Such a strategy allows banks to assert and ensure the integrity of the AI system, providing confidence to both the institutions and the regulators. It’s the most responsible way to deploy. Banks might start by applying this approach to smaller loans before moving on to larger-scale applications.”
Conclusion
Navigating this transformative era where innovation and efficiency converge requires embracing the changes that AI brings to financial institutions. With a balanced approach that ensures fairness and equality, industry leaders are shaping the future of credit decisioning in a way that is more inclusive, intuitive, and impactful than ever. However, it remains paramount to place explainability and interpretability at the heart of AI models to prioritize transparency, accountability, and human relevance.
The integration of artificial intelligence (AI) into the credit decision-making protocol can empower financial institutions to carve out a more inclusive, intuitive, and impactful finance sector. The journey towards a successful AI implementation starts with initiating specific AI-centric projects, cultivating a culture of innovation, and synergizing with seasoned AI partners.
To further delve into the transformative potential of AI, its integration into the financial sector, and the impact of AI explainability and transparency for financial institutions, download our comprehensive white paper now.
