AI in BSM: Applications, Frameworks & Insights | Mirai Whitepapers

Artificial Intelligence has moved well beyond research labs and pilot projects; it is now actively reshaping how financial institutions manage risk, allocate capital, and plan for the future. This transformation is particularly significant in Balance Sheet Management (BSM) and Asset and Liability Management (ALM), where traditional methods relied on inflexible models and labor-intensive processes.

This whitepaper, authored by risk technology experts at Mirai RiskTech, presents a structured exploration of how AI, and particularly Machine Learning, can be deployed strategically across the core functions of BSM and ALM. Drawing on real-world use cases and incorporating a practical classification framework, this document empowers risk leaders, treasury executives, data scientists, and model risk professionals with the insights they need to evaluate AI readiness and prioritize high-impact applications.

The paper introduces a dual-axis AI classification framework that maps AI systems by both cognitive capability (Artificial Narrow Intelligence, AGI, and ASI) and functional paradigm (Generative, Predictive, Explainable, and Agentic AI). In the context of Balance Sheet Management, four key AI categories stand out as vital: 

1. Generative AI, essential for automating compliance and generating synthetic data.

2. Predictive AI is crucial for accurately forecasting deposit behavior and conducting insightful scenario analyses. 

3. Explainable AI (XAI) is imperative for meeting stringent model governance and regulatory standards, such as Basel IV and SR 11-7. 

4. Dimensionality Reduction, which simplifies complex financial datasets and enhances model scalability. 

At the core of these transformative capabilities lies Machine Learning (ML). Rather than a single technique, ML functions as a toolbox of statistical and algorithmic methods that enables financial institutions to detect non-linear patterns, adapt to market dynamics, and generate probabilistic forecasts. By leveraging ML, organizations can surpass conventional models in key ALM areas such as loan prepayment modeling, deposit outflow prediction, and interest rate surface compression.

 Looking ahead, Digital Twins and Reinforcement Learning represent the next frontier, enabling institutions to simulate balance sheet dynamics in real time and optimize strategies before committing to real-world decisions.

A critical finding underpins the entire discussion: fewer than one in four organizations currently meet the data quality standards required to scale AI effectively. Cloud-native architecture, robust data governance, and cross-functional collaboration are therefore prerequisites for any institution serious about AI adoption in BSM.

Key Findings on AI and ALM

1. AI adoption is accelerating, but BSM lags behind. 78% of organizations worldwide use AI in at least one business function (Stanford HAI 2025), yet adoption in core ALM functions remains nascent, creating a first-mover advantage for institutions that act now.
   
   
2. Data quality is the primary bottleneck. Fewer than 1 in 4 organizations report high data quality, making data infrastructure investment a prerequisite for any serious AI deployment in BSM.
   
   
3. Machine Learning outperforms traditional models in behavioral forecasting. ML captures non-linear relationships and recalibrates automatically. A simple model updated monthly consistently outperforms a sophisticated one updated every 2–3 years.
   
   
4. Four AI types are immediately applicable to BSM. Generative, Predictive, Explainable, and Dimensionality Reduction AI each address distinct BSM challenges — from automating compliance reporting to improving deposit outflow modeling.
   
   
5. Digital Twins and Reinforcement Learning define the next frontier. These technologies can simulate balance sheet dynamics and optimize strategies in real time, moving ALM from static reporting to intelligent, self-improving decision support.