Article ID: 0a68da490d70cc6d326142265baf73ad9830d272b291e386b12e80f306c18575
Source ID: regulatory:risk.net
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URL: https://www.risk.net/awards/7963743/best-use-of-ai-moody%E2%80%99s
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Best use of AI: Moody’s Moody’s Banking Decision Intelligence solution uses artificial intelligence to bring together credit, liquidity, capital, profitability and other insights, helping banks make faster decisions without sacrificing transparency, governance or regulatory defensibility Banks are awash with data from all sorts of different systems. Credit risk and underwriting, profitability models, capital planning tools, liquidity platforms and portfolio analytics engines generate vast amounts of information every day. Yet many important decisions still depend on analysts manually pulling together insights from across these systems before reaching a conclusion. Moody’s believes that process has become unsustainable. Moody’s Banking Decision Intelligence solution, winner of the 2026 Risk Technology Award for Best use of AI, was developed to address a challenge facing many financial institutions: how to synthesise fragmented data, models and analytics into a coherent decision-making framework without compromising governance or explainability. Instead of introducing another dashboard or point solution, Moody’s Banking Decision Intelligence acts as an enterprise-wide intelligence layer that sits across a bank’s existing infrastructure. It connects lending, credit, current expected credit loss (CECL), asset-liability management, liquidity, capital, profitability and portfolio management systems, enabling users to ask business questions in natural language and receive a thoughtful synthesis, supported by transparent analytics and auditable calculations. Banking decisions rarely sit neatly within a single function. A relationship manager considering whether to reprice a loan may need to understand not only the profitability implications, but also the impact on capital consumption, funding costs and portfolio risk. Similarly, a treasury team monitoring liquidity exposure may benefit from understanding how those risks connect to lending activity, deposit behaviour or broader balance sheet dynamics. Traditionally, those insights would have required information from multiple teams and systems. Moody’s seeks to bridge those gaps. The solution integrates a bank’s internal data with Moody’s analytical engines and models, assembling the relevant information automatically before synthesising contextualised responses with options and analysis on their trade-offs. Every synthesis is accompanied by supporting assumptions, calculations and data lineage, providing a level of transparency designed to satisfy both internal governance requirements and regulatory expectations. “You need to triangulate that data within the context of other data,” says Charlene Bian, Moody’s head of risk and finance solutions. “You need to benchmark it, you need to compare it, you need to be able to fully wrap your head around it before you can act on it confidently.” While many organisations are exploring AI-enabled decision support, concerns remain around model governance, auditability and the use of opaque black box systems. Moody’s approach is to position AI as an orchestration layer rather than a replacement for established risk models. This deliberate approach ensures that the validated analytical engines underpinning the analysis remain unchanged. The AI is used to interpret user intent, identify the relevant analytical components, co-ordinate outputs across different domains and translate the results into plain-language synthesis that can be used to support decision-making. “Instead of giving you just a probability of default [PD] number, we explain what that PD means to you, such as whether it’s higher or lower than previous years, or how it compares to that of your peers,” Bian says, by way of example. The platform prepares metrics to be “more decision ready” by connecting them to other relevant information that enriches the context to support a confident decision. You don’t want your balance sheet interpreted by eight different teams in eight different ways. You want them to use the same foundational data, and to be able to draw multiple perspectives from that single source Charlene Bian The foundation for this decision intelligence is a domain-focused multi-agent architecture, sitting on top of Moody’s trusted data and engines. It offers both conversational access and flexibility for a headless deployment and integration with other systems. As a result, relationship managers, finance teams, treasury functions and risk professionals can query Banking Decision Intelligence directly and receive contextualised synthesis that is grounded in existing analytics and institutional data. Additionally, the solution can be flexibly deployed into workflows, enabling banks’ technology and AI teams to build reusable agents and automated decision workflows within a controlled governance framework, embedding decision intelligence directly into their own processes while maintaining oversight, observability and appropriate controls. Within the solution, Moody’s developed specialised domain agents that combine its own expertise with enriched industry knowledge across credit analytics, balance sheet management, portfolio analytics and impairment modelling. A central orchestration layer interprets user requests, co-ordinates the relevant agents and combines their outputs into a single response. This architecture improves scalability while ensuring the resulting synthesis remains traceable and explainable. The platform now supports a growing range of banking use cases, including loan committee preparation, relationship profitability analysis, concentration risk monitoring, risk review, capital and liquidity stress-testing, CECL reporting and analysis, asset-liability committee decision support and strategic planning across banks’ business, risk and finance functions. The firm reports engagement with more than 30 banks across global, regional and specialist segments, with several pilot programmes under way. Feedback has highlighted the appeal of connecting previously siloed data and analytics into a unified decision-making environment capable of supporting executive-level decisions. “Imagine you are a CEO of a bank – you don’t want your balance sheet interpreted by eight different teams in eight different ways,” says Bian. “You want them to use the same foundational data, and to be able to draw multiple perspectives from that single source.” Going forward, as margin pressures, funding costs and regulatory scrutiny continue to increase, banks will need tools that can synthesise complex information, quantify trade-offs and identify potential actions while keeping humans firmly in control. In line with that shift, Moody’s has created a connective layer across the modern bank, helping institutions move from fragmented analysis towards faster, more informed and fully traceable decision-making. “The beauty of it is,” Bian concludes, “once you figure out how to connect the foundational data and capabilities, they can – and should – be used to answer questions from many different perspectives.” Sponsored content Copyright Infopro Digital Limited. All rights reserved. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy. If you would like to purchase additional rights please email info@risk.net Copyright Infopro Digital Limited. All rights reserved. You may share this content using our article tools. 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