As highlighted in the first article in our series of three on 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 monolithic 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.
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:
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:
Mirai AI applies Predictive AI at the contract level, modeling deposit behavior, loan prepayments, and liquidity dynamics using ML models trained on institutional balance sheet data, giving ALM teams forecasting granularity that static models cannot provide.
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.
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:
This is a core design principle of Mirai AI: every model output is traceable to its inputs and assumptions, ensuring that risk teams, auditors, and regulators can follow the analytical reasoning behind each forecast or recommendation without treating the system as a black box.
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:
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.
Mirai AI applies all four approaches in an integrated production environment:
Predictive AI for deposit behavior and prepayment modeling,
Explainable AI for regulatory transparency and model governance,
Generative AI for natural language analytics and ALCO reporting, and
Dimensionality Reduction AI for efficient, scalable deployment within the Mirai platform.
It is not a standalone AI layer but an integral part of the balance sheet management workflow.
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Our full whitepaper on this matter explores:
Download the full whitepaper here to explore how your institution can move from static planning to intelligent, adaptive balance sheet management.
Why does Balance Sheet Management require more than one type of AI?
Because the challenges of BSM are not uniform. Forecasting deposit behavior requires Predictive AI trained on historical patterns. Explaining a risk model to a regulator requires Explainable AI. Auto-generating an ALCO report requires Generative AI. Simplifying a high-dimensional credit model for deployment requires Dimensionality Reduction AI. Each type addresses a distinct problem, and the institutions that combine them effectively gain capabilities that no single AI approach can deliver on its own.
What makes Generative AI different from other types of AI used in finance?
Most AI in finance has historically focused on analyzing or predicting outcomes from existing data. Generative AI goes further by producing new content, such as regulatory summaries, investor communications, synthetic datasets, or natural-language responses to balance-sheet queries. In BSM, this shifts AI from a background analytical tool to an active participant in how information is prepared, communicated, and acted upon across the organization.
How does Predictive AI improve on traditional forecasting methods in ALM?
Traditional ALM forecasting relies on fixed assumptions and manual model updates, which struggle to keep pace with rapidly changing market conditions. Predictive AI processes large volumes of historical and real-time data simultaneously, identifies non-linear patterns across thousands of variables, and updates continuously as new information arrives. The result is more granular forecasting of deposit behavior, loan prepayments, and liquidity dynamics, with faster calibration and deeper visibility into the behavioral drivers behind each projection.
What is the difference between Explainable AI and standard AI transparency disclosures
Standard transparency disclosures describe what a model does at a high level. Explainable AI goes deeper: it reveals how a specific prediction was reached, which input variables drove the outcome, and where the model may carry bias or uncertainty. In a regulatory context, this distinction matters because supervisors under Basel IV and SR 11-7 expect institutions to demonstrate not just that their models work, but that they understand why they work and can defend that reasoning to auditors and risk committees.
Why is Dimensionality Reduction AI particularly relevant for banks with large balance sheets? Large balance sheets generate enormous volumes of data across interest rate curves, customer segments, product types, and macroeconomic indicators. Without dimensionality reduction, models built on this data become computationally expensive, prone to overfitting, and difficult to deploy at scale. Dimensionality Reduction AI simplifies these models by identifying the variables that carry the most predictive power and discarding redundant ones, preserving accuracy while enabling faster, more cost-effective deployment across treasury and ALM functions.
How do Generative AI and Predictive AI complement each other in BSM?
Predictive AI generates the analytical insight: what deposit outflows are likely under a rate shock, how prepayment rates will evolve, where liquidity buffers may come under pressure. Generative AI turns that insight into action: drafting the ALCO commentary, structuring the stress-test narrative, or answering a natural-language query about the portfolio. Together, they cover the full cycle from analytical production to organizational communication, where much of BSM's current operational cost sits.
How does Mirai AI apply these four types of AI in practice?
Mirai AI integrates all four approaches within Mirai: Predictive AI for contract-level modeling of deposit behavior and prepayment rates, Explainable AI to ensure every model output is traceable and audit-ready, Generative AI for natural language balance sheet queries and ALCO reporting, and Dimensionality Reduction AI for scalable, efficient deployment across treasury and ALM workflows. Rather than operating as a standalone layer, Mirai AI is embedded directly in the balance sheet management fabric, so analytical outputs connect immediately to reporting, governance, and decision-making processes.