Generative AI and Machine Learning (ML) are transforming many areas of the financial industry, including credit risk management. While these AI technologies have the potential to improve decision-making and streamline operations, they also come with significant limitations and challenges. This article explores these limitations, focusing on the complexities and risks of using generative AI and ML in credit risk management.
In a nut shell, credit risk management is the process by which banks and financial institutions identify, assess, and mitigate the risk that a borrower (counter-party) might default on their loan obligations. It involves evaluating the borrower’s ability and willingness to repay their debts based on various factors, including financial history, credit scores, economic conditions, and qualitative judgments[1].
Credit risk assessment is a fundamental process in banking, used to evaluate the likelihood that a borrower will be able and willing to repay their financial obligations on time. Traditionally, this process involves a combination of quantitative analysisâsuch as examining financial statements and credit scoresâand qualitative judgment, that considers factors like management quality, borrowerâ reputation and character, industry outlook, borrowerâs competitiveness and business model and strategy.
Additionally, as part of deal structuring, judgments are made regarding risk mitigation measures such as loan covenants, guarantees and collateral. AI is significantly transforming this process by automating data analysis, enhancing predictive accuracy, and enabling real-time risk monitoring.
Economic Interdependencies
The overall economy plays a crucial role in credit risk management. For instance, during a recession, more borrowers are likely to default on their loans. Conversely, in a booming economy, banks may increase lending to capitalize on growth, which can expose them to greater risks during downturns.
| Strengths | Weaknesses |
| Generative AI models excel at analyzing large datasets, such as GDP trends or unemployment rates, providing comprehensive economic insights. Similarly, ML models can identify patterns and correlations in economic data, aiding in the prediction of credit risks. | Both generative AI and ML struggle with capturing the nuanced and interconnected effects of economic cycles on individual borrowers. For example, during the 2008 financial crisis, even sophisticated risk models failed to predict the cascading effects of subprime mortgage defaults. This underscores their limitations in volatile economic periods and their reliance on comprehensive data and scenario analysis. |
Qualitative Judgments
Generative AI is powerful in processing quantitative data but falls short when it comes to making qualitative assessments[2]. Factors such as a borrower’s reputation, management quality, or industry outlook often require human intuition and experience.
| Strengths | Weaknesses |
| ML models can analyze historical data to identify trends and make predictions. Generative AI can generate scenarios based on data patterns, providing potential outcomes for credit assessments. | Both technologies fall short in capturing qualitative insights. For instance, a startup with limited financial history might score poorly in a purely data-driven assessment but could still represent a low-risk borrower due to an innovative product and a strong leadership team. These insights are difficult for AI to fully capture, making human judgment indispensable in credit risk evaluations. |
Model Bias and Fairness
AI models, including generative AI and ML, are susceptible to biases inherent in their training data[3]. Historical lending data, for example, may reflect discriminatory practices, such as redlining, where loans were unfairly denied to specific demographics. If not properly addressed, AI models could perpetuate or even amplify these biases.
| Strengths | Weaknesses |
| ML and generative AI can process vast amounts of data quickly, potentially identifying and correcting biases if trained appropriately. | Bias can be inadvertently introduced by data scientists during the model development process. Choices such as how data is preprocessed, the features selected for the model, or the assumptions embedded in algorithms can encode bias. This highlights the importance of ensuring that data scientists are trained to recognize and mitigate their own biases during model design and implementation. Regular independent model validation and audits should not only focus on statistical performance measures but also aim to understand the nature and purpose of the model, as well as the development and parameterization processes, to detect potential biases. |
Data Quality and Privacy
AI’s effectiveness depends heavily on the quality of its training data. Incomplete or biased data leads to flawed predictions. For example, if an AI model lacks data on recent market conditions or emerging risks, its forecasts may be inaccurate or outdated.
| Strengths | Weaknesses |
| Both ML and generative AI can handle large datasets and identify patterns that may not be immediately obvious to human analysts. | Both technologies are vulnerable to issues of data quality. Moreover, financial data is highly sensitive, making privacy a critical concern. Adhering to regulations like GDPR requires robust data anonymization and encryption practices. Balancing data accuracy and privacy is a complex yet essential task for AI deployment in the financial sector. |
Explainability and Interpretability
Generative AI models, especially those using deep learning, often function as “black boxes,” producing predictions without clear explanations. This lack of transparency raises trust issues, particularly when justifying decisions to stakeholders or meeting regulatory requirements.
| Strengths | Weaknesses |
| ML models, particularly simpler algorithms like decision trees, can be more interpretable than deep learning-based generative AI. This makes it easier to understand and explain decisions to stakeholders. | Complex ML and generative AI models can be difficult to interpret. For instance, a bank might use AI to assess a borrowerâs creditworthiness. If the AI rejects the application, there is a need for the relationship manager to understand the reasons and articulate them to internal management and, as appropriate, the borrower. Without explainability and transparency in credit decisioning, the bank stands to lose credibility, miss business opportunities, and potentially diminish the share value of the business. Regulators, on the other hand, are keen to ensure the bank has robust governance and controls in place to mitigate potential credit losses. |
Integration with Existing Systems
Integrating generative AI into existing credit risk management systems is complex and resource-intensive. Legacy systems often lack the flexibility to accommodate AI-driven tools, requiring significant upgrades or replacements.
| Strengths | Weaknesses |
| Both ML and generative AI can enhance existing systems by providing advanced analytics and automation capabilities. | Integration can be challenging due to technical and operational constraints. For example, integrating AI with a bank’s loan origination system might involve retraining staff, reconfiguring workflows, and ensuring compatibility with existing compliance frameworks. Without careful planning, these challenges could delay or undermine AI implementation. |
Collection and Recovery Processes
Generative AIâs role in collection and recovery processes for corporate borrowers remains limited. These processes often involve human-led negotiations, relationship management, and complex collateral valuations.
| Strengths | Weaknesses |
| AI can provide data-driven insights that support the collection and recovery processes. For instance, assessing the value of collateral, such as commercial real estate or equipment, requires expertise in market conditions and asset-specific factors. AI may assist by providing data-driven insights, but human intervention is necessary to account for fluctuating market conditions and legal complexities. | Generative AI struggles with nuanced and context-specific tasks. Similarly, legal assessments of guarantees and other risk mitigants involve interpreting contract terms, regulatory compliance, and enforceabilityâall areas where AI has limited capability. While AI can support these tasks, it cannot replace the nuanced judgment of legal and financial experts. |
Accountability for Credit Decisions
Accountability is a critical aspect of credit risk management, particularly when credit quality deteriorates. For example, during economic downturns, banks may need to increase provisions to cover potential losses from borrower defaults. These provisions can significantly impact earnings.
| Strengths | Weaknesses |
| AI models can provide early warning signals for deteriorating credit quality, allowing for proactive management and intervention. | When AI models drive credit decisions, questions arise about who is responsible for the outcomes. If an AI-driven decision leads to increased provisions, management must justify these decisions to stakeholders and regulators. For instance, a bank might face scrutiny if an AI model overestimated creditworthiness, resulting in higher-than-expected defaults. Ensuring clear accountability and a thorough understanding of the AI model’s decision-making process are essential for maintaining trust and regulatory compliance. |
Practical Use Case: AI and Credit Monitoring
A practical example of generative AI’s potential is its use in monitoring credit portfolios. AI can flag borrowers showing early signs of distress, such as declining cash flow or missed payments, allowing banks to intervene proactively. However, as seen during the COVID-19 pandemic, sudden and unprecedented events can render even advanced AI models ineffective without human oversight to interpret and adapt strategies.
Generative AI and Machine Learning hold significant promise for credit risk management, offering advanced analytics and automation capabilities. However, their limitationsâsuch as biases, lack of qualitative judgment, and challenges with accountabilityâunderscore the importance of balancing AIâs capabilities with human expertise. This balance is crucial for banks and financial institutions as they navigate the complexities of integrating AI into their organisations.
Should you wish to discuss any of issues raised in the note, please do not hesitate to contact: Laurie Antioch, Chief Finance & Strategy Officer.
[1] These factors often require subjective judgment and a deep understanding of the specific circumstances and details surrounding the lending decision.
[2] Particularly in areas such as contextual understanding and cognitive dissonance where the former is ensures decisions are informed and holistic, while the latter fosters self-reflection and bias mitigation. Other dimension of human judgement include: emotional intelligence and creative problem solving.
[3] The selection of an appropriate modelling technique is crucial for accurate and reliable analysis, requiring careful consideration of the specific context and data characteristics to minimize model risk.