When COVID-19 lockdowns swept the globe in 2020, businesses faced a stark reality: adapt or be made obsolete. For banks, survival meant embracing digital transformation at an unprecedented pace. Confronted with a sudden need to serve customers remotely and safeguard operations, banks were forced to accelerate investments in technology.
That digitization has become a defining force in banking performance worldwide. In the US, for instance, Moody’s Ratings’ Q2 2024 banking update highlights how large banks have improved operating leverage, a trend that suggests digital investment is yielding returns. Regional US banks, where net interest income remains a primary revenue driver, have also seen some relief from net interest margin compression, potentially reflecting the long-term impact of their digital strategies.
What began as a crisis-driven response has since evolved into a broader recalibration of banking’s relationship with technological innovation. Having built out digital infrastructure at speed, banks now had the proper scaffolding to leverage AI in ways previously considered still years—if not decades—into the future.
Today, AI is quickly becoming embedded in financial services. Moody’s Ratings 2025 global outlook for banks recently shifted from negative to stable, helped in part by an “improved application of technology”. The outlook also notes strong profitability for 2025, as banks “increase use of new technologies, including generative artificial intelligence”. Financial institutions that embraced digitalization during the pandemic are now poised to reap larger benefits, emerging leaner, more efficient, and better positioned for both sustained profitability and innovative growth.
A digital turning point
Banks had to tackle two critical challenges in 2020: maintaining business continuity and handling the explosion of digital data. With customers unable to visit branches, banks had to rapidly adopt cloud-based infrastructure, automation tools, and remote customer services. Overnight, years of digital inertia evaporated.
At first, these investments were seen as merely an expensive stopgap to keep the lights on. But as lockdowns continued, banks discovered that digitization offered more than survival. Automated workflows, cloud platforms, and integrated data systems not only improved productivity but also reduced costs. Financial institutions that had long been slow to adopt new technologies found themselves racing ahead, gaining valuable insights into both operational efficiency and risk management.
More significantly, these changes rewired how banks approached technology. Once seen as a necessary operational headache, automation and AI-powered analytics proved their worth in driving efficiency and business resilience. Banks that once hesitated to integrate AI-driven tools into their workflows found themselves more willing—if not eager—to explore its potential.
As banks transformed, so did customer interactions and expectations. According to Laurent Birade, Moody’s Banking Industry Practice lead, “The COVID-19 pandemic has significantly accelerated the adoption of digital payments and AI in the banking sector. According to the World Bank, the global increase in digital payments has been remarkable, with 76% of adults now having a bank or mobile money account, up from 68% in 2017. Additionally, banks have expanded their use of AI to enhance customer service, streamline operations, and improve risk management. AI-powered chatbots and virtual assistants are now common, handling a large portion of customer inquiries and transactions. These trends underscore the profound impact of the pandemic on the banking sector, driving a more digital, efficient, and customer-centric approach.”
Machine learning, in particular, became more than just an experimental concept; it became essential. Banks, under pressure to process vast amounts of digital transactions, analyze credit risk remotely, and detect fraud in real time, found that AI could provide solutions at a scale and speed that traditional systems could not match. AI-powered credit analysis became more commonplace, integrating real-time financial data, earnings reports, and peer comparisons at speeds unimaginable just years before.
At Moody’s, we have been developing and refining AI-driven solutions since 2018, gaining expertise in automating research, credit analysis, and risk monitoring. The first opportunity to leverage AI to drive efficiencies for our customers focused on applying machine-learning capabilities to the timely and manual process of financial spreading. Moody’s developed a powerful and effective solution that saw a modest but promising adoption starting in 2018 that continued leading up to the 2020 lockdowns in March. When the pandemic hit, however, and multiple unprecedented lockdowns were enforced across the globe, things changed.
Financial institutions suddenly found themselves without access to the analysts responsible for manually spreading financial data. With teams working remotely and workflows disrupted, banks had to reconsider digital solutions. As a result, the customer base leveraging Moody’s AI financial spreading solution almost doubled in 2020 alone, reaching a 57% growth in customers by end of 2021.
This rapid shift foreshadowed today’s broader embrace of GenAI in banking. The solutions that emerged during the pandemic didn’t just bridge short-term gaps; they reshaped banking’s long-term approach to automation and technology in general.
Enter GenAI
Building on the digital foundations laid during the pandemic, GenAI has emerged as the next frontier in financial services. Unlike earlier automation tools designed to simply streamline repetitive tasks, GenAI introduces adaptability and creativity. AI models can generate tailored financial insights, automate complex research processes, and synthesize vast datasets in real time, allowing banks to move from static reporting to proactive decision-making.
Presently, with the GenAI powered solutions offered through Moody’s, customers are fundamentally transforming how financial institutions operate, driving unprecedented gains in efficiency, insight generation, and decision-making capabilities.
- In-depth credit analysis automation: An analyst who once spent hours compiling financial statements for a client credit review can now receive an AI-generated summary in seconds. Instead of manually sifting through earnings reports and company filings, the system synthesizes key figures, identifies risk factors, and even compares the client’s performance to industry peers—all in real time.
- Credit memo creation: Generating a comprehensive credit memo, a task that can take hours or even days of compiling data from disparate sources including financial statements, emails, websites and various other documentation can be now be fully automated. Through GenAI powered technology credit analysts can generate a fully-written credit memo that pulls data from multiple sources to present a full view of risk, in their own bank's template.
- Portfolio monitoring automation: Monitoring performance on the loan and entity level, too, are experiencing a shift. Before AI-powered monitoring, a fund manager tracking dozens of assets would rely on periodic updates and manually review financial news for potential risks. Now, AI-driven alerts flag changes in credit ratings, major financial disclosures, and even shifts in investor sentiment, helping the manager be ahead of the curve. A once-passive monitoring process has become a proactive strategy, with AI surfacing insights before they become crises.
- M&A automated insights: Investment banking is undergoing a similar transformation. Traditionally, preparing an M&A pitch deck involved days of research, integrating market data, and drafting narratives. Now, GenAI tools automatically generate investment memos, identifying potential deals, analyzing valuation trends, and structuring pitch materials based on real-time market conditions. A process that once took days can now be completed in hours.
Across the board, financial institutions are no longer just integrating AI into existing processes—they are reimagining how those processes function. GenAI is not replacing human expertise but augmenting it, allowing professionals to focus on high-value decision-making while AI handles the data-heavy lifting.
The drivers of adoption
The same forces that pushed banks to digitize in 2020 are now fueling their adoption of GenAI. The pandemic made clear that technological agility was essential for both growth and survival, and those lessons remain at the forefront of today’s banking strategies. GenAI builds upon this digital infrastructure, offering new opportunities for efficiency, innovation, and risk management.
Three core drivers underpin banks’ growing commitment to AI:
Efficiency gains and cost reduction:
The pandemic demonstrated how automation and cloud technology could streamline operations, reduce overhead, and improve service delivery. GenAI enhances these efficiencies further, automating complex tasks like data integration, financial modeling, and risk analysis, freeing up professionals to focus on strategic initiatives.
Competitive edge:
Early adopters of digital services gained a significant advantage during the lockdowns by maintaining strong customer engagement and operational continuity. GenAI is now becoming the next competitive differentiator, allowing banks to offer tailored services, improve turnaround times, and gain better insights from vast data sources.
Enhanced risk management:
In a volatile world, banks need to anticipate risks more effectively. The digital upgrades made during COVID improved monitoring and response capabilities. GenAI advances this by providing real-time, data-driven insights, allowing institutions to detect emerging risks and opportunities before they escalate.
A sector transformed
The COVID crisis demonstrated that adaptability is no longer optional in banking. Institutions that responded swiftly to the lockdowns have since reaped the rewards, building digital ecosystems that support both growth and stability. GenAI is now propelling these gains forward, redefining how banks operate and serve their customers.
Solutions like Moody’s Research Assistant and Moody’s Lending Suite Automated Spreading, Automated Credit Memo and Loan Monitoring exemplify this transformation. By automating data analysis, generating real-time insights, and enhancing customer interactions, AI tools help banks to optimize efficiency and deliver more personalized services. Customers benefit from faster, more intuitive interactions, while professionals focus on strategy rather than administration.
Ultimately, the race to adopt AI is not just about staying current with technology—it is about shaping the future of finance. The banks that move decisively will likely dominate the landscape of tomorrow. For those that hesitate, the risks may prove insurmountable.
Interested in learning more about what GenAI can do for you?