For more than a century, Moody’s has been at the forefront of financial analytics, providing the data and insights that underpin decision-making across global markets. As artificial intelligence reshapes industries at an unprecedented pace, generative AI(GenAI) is rapidly gaining traction in financial services – a sector often perceived as cautious due to its highly regulated nature.
In December 2023, Moody’s launched Research Assistant, a pioneering AI-driven search and analytical tool, designed to enhance how financial professionals access and analyze data. The product has since become the most rapidly adopted solution in Moody’s history, reflecting the financial sector’s growing recognition of AI’s potential to drive efficiency and generate insights.
According to Cristina Pieretti, General Manager of Digital Insights at Moody’s Analytics, this development represents not just a technological shift, but a fundamental rethinking of how financial institutions engage with information. Speaking on Microsoft Azure's Leading the Shift podcast, Pieretti outlined the strategic considerations behind Research Assistant, the challenges of integrating AI into a regulated industry, and what its rapid adoption signals for the broader financial landscape.
A calculated leap into GenAI
Moody’s has long employed machine learning to refine quantitative models and data collection methods, but the rise of generative AI presented both an opportunity and a challenge.
“Recognizing its potential for disruption, we wanted to understand what it meant for us as a company, how we could operate more efficiently, how it could transform our offerings, and how it would impact our customers,” said Pieretti.
Rather than taking a wait-and-see approach, Moody’s opted to drive the transformation internally, with CEO Rob Fauber and President of Moody’s Analytics Stephen Tulenko prioritizing AI adoption. “It’s always better to be the disruptor than to be the disrupted one,” Pieretti noted.
The firm took a measured but proactive approach: AI was to enhance expertise, not replace it. This philosophy shaped the development of Research Assistant, ensuring that it served as a tool to improve productivity while maintaining the accuracy, reliability, and regulatory compliance that financial professionals require.
From concept to market: Building AI for finance
Many organizations approach AI adoption incrementally, moving cautiously through lengthy approval cycles before committing resources. Moody’s, however, took a different route.
“We started very small—it wasn’t even a full squad. It was just a few developers working from a laptop, testing what was possible,” Pieretti explained.
This approach, more characteristic of a technology start-up than a century-old financial firm, enabled Research Assistant to move from concept to market in record time. Rather than building a theoretical model and seeking validation after the fact, the team began with experimentation, scaling resources only after clear value was demonstrated.
According to Pieretti, “Sometimes as leaders, we have to take a stance on uncertainty. We had to move quickly, but we also had to ensure we weren’t just chasing hype. We needed to create something truly useful for our customers.”
The key lesson, according to Pieretti, is that the most effective AI strategies do not start with grand business cases but with small, practical applications that can be refined and expanded.
Navigating risk in a regulated industry
The financial sector is highly sensitive to risk, and for good reason. Generative AI, with its potential for hallucinations, unreliable outputs, and regulatory complexities, presents unique challenges. Unlike consumer AI applications, where minor inaccuracies are tolerated, financial decision-making depends on verifiable, high-quality data.
Moody’s tackled this issue head-on by designing Research Assistant with trust and transparency at its core.
“From the beginning, we knew that accuracy and traceability were non-negotiable,” Pieretti said. “All responses had to be grounded in Moody’s proprietary data. That means if Research Assistant doesn’t have a trusted source for an answer, it won’t fabricate one. It will simply say, ‘I don’t know.’”
This deliberate constraint ensures that:
- AI-generated insights are exclusively sourced from Moody’s proprietary data and verified third-party sources.
- Every AI-generated response is fully auditable. Users can trace answers back to their underlying sources, ensuring complete transparency.
- Data security and regulatory compliance were embedded from the outset. The system is designed to function within the stringent governance frameworks that financial institutions require.
This disciplined approach differentiates Moody’s AI strategy from that of more speculative AI adopters, demonstrating that AI can be deployed at scale in regulated industries without compromising trust or accuracy.
A transformative impact on financial workflows
Research Assistant is not simply an AI-powered search tool. Rather, it represents a shift in how financial professionals interact with data, enhancing efficiency in previously labor-intensive processes.
According to Pieretti, three key features define its impact:
- AI-powered search: Users can rapidly surface insights, research, and financial data.
- Advanced querying (text-to-code): Natural language queries are automatically translated into structured database searches, allowing complex analytics without specialized coding expertise.
- AI-driven process automation: asks such as financial modeling, investment research, and compliance reporting can be partially or fully automated, significantly reducing time spent on repetitive work.
“Many of our clients want to do more with less. AI can help them achieve that by automating the most time-consuming parts of their workflows.” The tool has already been adopted by over 100 financial institutions, and thousands of users now rely on it daily, an indication of how generative AI is becoming an essential component of modern financial workflows.
What this signals for the future of AI in finance
The rapid adoption of Research Assistant is a microcosm of a broader industry transformation.
“We’ve seen financial institutions of all sizes, from global banks to smaller firms, embrace AI faster than expected,” Pieretti said. “But there are still challenges, particularly around risk frameworks and governance. Institutions that are prepared to address those concerns proactively will gain a real competitive advantage.”
Looking ahead, Moody’s sees AI playing an increasing role in:
- Enhanced automation of financial analysis – Reducing the manual burden of risk assessment and investment modeling.
- AI-assisted compliance solutions – Helping institutions navigate evolving regulatory frameworks through automated reporting and monitoring.
- Greater personalization of insights – AI that adapts to individual users, providing tailored recommendations and workflow optimizations.
The fundamental question is no longer whether AI will play a role in financial services—it is how quickly firms will integrate it, and how effectively they will manage the risks.
Final Reflections: The imperative to act
Moody’s decision to embrace AI rather than resist it offers a valuable lesson to the broader financial sector. The institutions that hesitate in adopting AI-driven solutions risk falling behind not only in efficiency but in capability and competitiveness.
For Moody’s, AI’s role in finance is not to replace human expertise but to amplify it, offering professionals greater speed, deeper insights, and enhanced decision-making support. Research Assistant is a clear demonstration of this principle in action.
According to Cristina Pieretti, the key to success is a balance between innovation and governance. “We need to be at the cutting edge, but we also need to ensure we’re doing things the right way. That’s how we build trust and long-term success.”
For financial institutions, the message is clear: the AI shift is already happening, and those who embrace it thoughtfully will define the future of the industry.