Balance Sheet Management
August 19, 2025
As it was highlighted in the first article in our series of three covering AI applied to BSM, Artificial Intelligence (AI) is already a reality. It wouldn’t be frivolous to say that AI is reshaping the financial world today, and in terms of its applicability in this field, it is important to understand that AI is not a monolith concept. It can be layered into different types and categories, each encompassing a distinct spectrum of methodologies, architectures, and technologies.
And, among the various categories of AI, some have direct applications to Balance Sheet Management (BSM).
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While Generative AI (Gen AI) is often regarded as the backbone of this transformative environment, other types of AI have also been redefining operational strategies in the banking sector, particularly in the management of the balance sheet. In addition to Gen AI, three other types of artificial intelligence contribute meaningfully to enhancing BSM capabilities and outcomes: Predictive AI, Explainable AI, and Dimensionality Reduction AI.
1. Generative AI: A New Chapter in Banking Innovation
Generative AI is changing the way we think about artificial intelligence. Unlike earlier models that focused on processing and analyzing data, today’s GenAI systems can actually create. They generate everything from text and images to code, opening up new possibilities across industries, especially in banking.
In finance, this is more than a tech upgrade; it’s a full shift in how institutions operate. GenAI is helping banks deliver more personalized services, launch new products faster, improve risk management, and automate routine tasks with far greater efficiency. What once took weeks of manual work can now be streamlined through smart, responsive AI tools.
The journey of AI in banking has already been transformational, moving from basic data processing to the deployment of advanced, high-impact applications. From investment research and customer engagement to knowledge management and fraud detection, GenAI is proving its value across the board, and it can be said that it´s unlocking a new era of strategic innovation, precision, and opportunity.
Practical Applications in BSM:
- Compliance reporting automation: Auto-generates regulatory summaries, saving time and reducing human error.
- Client communication: Drafts tailored investor or internal communications related to balance sheet changes or stress test results.
- Synthetic data generation: Creates realistic but non-sensitive datasets for training forecasting models when real data is limited or privacy-restricted.
2. Predictive AI: A Key Area Transforming BSM
Among the categories of artificial intelligence transforming Balance Sheet Management (BSM), Predictive AI plays a particularly vital role. Built on statistical modeling and machine learning, Predictive AI analyzes vast amounts of historical and real-time data to identify patterns and anticipate future events, behaviors, and outcomes. While predictive analytics has long supported data-driven decision-making, the integration of AI dramatically enhances its speed, scale, and precision, which enables institutions to process thousands of variables across decades of data in real time. The result is a forecasting capability that strengthens proactive risk management and long-term strategic planning.
Unlike descriptive analytics, which focuses on explaining past events, or prescriptive analytics, which recommends actions based on likely outcomes, Predictive AI is centered on anticipating what’s ahead. Its applications span many industries, but its impact in financial services is particularly strong. From forecasting customer behavior and market volatility to identifying early signs of operational risk, Predictive AI helps institutions stay ahead of emerging trends. In BSM, it supports critical functions such as interest rate and liquidity forecasting, credit risk assessment, and scenario analysis, all key tools for regulatory compliance and capital planning. By turning data into foresight, Predictive AI is redefining how financial institutions prepare for the future.
Practical Applications in BSM:
- Market data forecasting: Anticipation of key risk factors and variables, both endogenous and exogenous.
- Operational factors modeling: Strengthens estimates for loan loss reserves and capital requirements via early-warning systems.
- Stress testing and scenario analysis: Projects asset and liability behavior under evolving macroeconomic conditions.
How Generative AI and Predictive AI Work Together
Generative AI and predictive AI are two powerful branches of machine learning, each offering distinct capabilities, but they’re not mutually exclusive. In fact, when used together, they can significantly enhance how businesses make decisions, engage customers, and drive innovation.
Predictive AI focuses on forecasting future outcomes by analyzing historical data patterns. It powers use cases like demand forecasting, risk assessment, and customer behavior modeling. Generative AI, on the other hand, creates new content based on natural language prompts. Tools like ChatGPT are built on Large Language Models (LLMs), which use statistical predictions to generate the most likely next word, sentence, or idea in context.
While both rely on big data and machine learning, their goals differ: predictive AI aims to anticipate what will happen, while generative AI focuses on producing new, coherent outputs. When used in tandem, these technologies can complement each other. Predictive models offering insight, and generative models turning that insight into content, solutions, or even strategies.
3. Explainable AI (XAI): Building Trust in AI-Driven Decisions
Explainable AI (XAI) plays a vital role in model risk governance, especially under regulatory frameworks like Basel IV and SR 11-7. It helps clarify how AI models operate by producing outputs that are interpretable and traceable, which makes it easier for auditors, regulators, and internal stakeholders to understand model behavior.
By revealing how predictions are made, identifying potential biases, and outlining the expected impact of models, XAI builds trust and strengthens accountability. It supports responsible AI development by clarifying model accuracy, fairness, and transparency, all of which are increasingly crucial when deploying AI in production.
As AI systems grow more complex, their inner workings often become difficult, or even impossible, to interpret. Many advanced models operate as “black boxes,” producing results directly from data without offering clear insight into how those results were reached. Not even the data scientists who build these models can always explain their reasoning. That’s where XAI becomes invaluable: it helps teams verify model behavior, meet regulatory requirements, and empower users to question or contest AI-driven decisions. In a world where automated decisions can have significant real-world impacts, explainability is not just a feature; it is a foundational requirement.
Practical Applications in BSM:
- Model risk governance: Clarifies how deposit behaviors, prepayment models, or liquidity risk scores are computed.
- Regulatory compliance: Supports transparent disclosures required under Basel IV, SR 11-7, and model validation standards.
- Stakeholder communication: Facilitates internal audits and board-level explanation of risk forecasts or strategic decisions.
4. Dimensionality Reduction AI: Turning Complexity into Opportunity
Dimensionality reduction is a key capability of AI that helps simplify complex models without sacrificing performance. By streamlining models to use less data, fewer parameters, and lower computational resources, AI can optimize efficiency while preserving accuracy. This is especially valuable in financial modeling, where high-dimensional datasets are common.
Credit risk analysis, loan prepayment modeling, and balance sheet optimization all involve large numbers of variables and limited historical data, often with complex, non-linear relationships among economic, behavioral, and institutional factors.
Practical Applications in BSM:
- Model simplification: Helps develop stable risk models that remain robust in stressed scenarios without overfitting.
- Efficient deployment: Enables faster deployment of risk and liquidity analytics, especially in resource-constrained environments.
- Operational scalability: Supports cost-effective scaling of real-time monitoring systems in treasury and ALM functions.
Forward-looking Approach to Balance Sheet Management
As artificial intelligence continues to evolve, its role in Balance Sheet Management is becoming increasingly strategic. By leveraging the strengths of Generative, Predictive, Explainable, and Dimensionality Reduction AI, financial institutions can unlock new levels of efficiency, insight, and resilience.
Thoughtfully adopting these AI capabilities is a forward-looking approach to managing risk, meeting regulatory expectations, and driving innovation in modern finance.
Want to go deeper?
Our full whitepaper on this matter explores:
- The two-axis framework for classifying AI in finance
- The four AI types driving real impact in BSM
- Practical use cases of Machine Learning across ALM domains
- Inspirational insights: How institutions can begin building toward Digital Twins and self-learning systems
Download the full whitepaper here to explore how your institution can move from static planning to intelligent, adaptive balance sheet management.
