Banks today face a complex, rapidly evolving landscape defined by economic volatility, shifting regulation, and fast-moving technology. These elements are driving significant changes across the banking industry and presenting both challenges and opportunities.
Credit defaults have remained in check throughout 2025, and many asset classes (especially CRE) have shown resilience. Nevertheless, emerging sources of volatility like concentrated leverage and fluctuating tariff regimes call for strong early warning indicators. Holistic and real-time gauges of risk are needed to avoid unexpected losses and confidently allocate capital.
The disintermediation of traditional banking continues apace, with private credit funds and neo-banks taking share from conventional players. Even as banks work to stave off competition, they are also learning to co-exist with their disruptors by providing them with funding and even participating in deals. The tools to measure and assess risks must now support these complex and expanding relationships.
Banks have long worked to balance new technology adoption alongside regulatory scrutiny and evolving customer expectations. Artificial intelligence (AI) represents the latest phase of this pattern, offering tremendous potential for greater efficiency and improved customer experience as long as operational and regulatory risks are well-managed.
At Moody’s, we see the current environment as reflective of an ongoing pattern of interconnected and evolving risks. While these risks represent an evolution of what bankers have been dealing with for decades, the new generation of industry leaders is faced with heightened expectations of their ability to respond proactively and without disrupting the normal flow of business.
A bank’s risk appetite, workflows, and commercial strategy must be resilient enough to address today’s conditions and adaptable enough to quickly redeploy capital and absorb risks that have yet to fully emerge.
To provide perspective on the risks and opportunities facing banks as we enter 2026, I have asked Moody’s Industry Practice Leads, all former banking practitioners, to reflect on the major trends that shaped banking over the past year and to explain which developments mattered most and how they might play out in the coming year.
My hope is that by understanding these developments clearly and crafting responses that are flexible enough to navigate risks and seize opportunities, banks can thrive no matter what 2026 may have in store.
— Andrew Bockelman, General Manager, Head of Banking, Moody’s
Banks ramped up innovation investment – but struggled to turn AI potential into reality
In 2025, banks leaned on technology and innovation to act as an enabler for driving resilience, efficiency, and automation across workflows. AI, including GenAI and agentic tech, showed clear potential – but fragmented processes, legacy systems, and unstructured data kept many institutions from scaling.
The real shift comes in 2026. AI moves from pilots to practice. Success will hinge on strong data foundations, robust governance, and the right talent and culture. Banks that start with “why,” focus on high-value use cases, and invest in these enablers will turn AI promise into performance. For example, banks that effectively integrate AI into their lending workflows will be able to leverage their borrower data more efficiently, unlock speed and efficiency and ultimately grow their loan capacity and profitability.
The growth of private credit greatly impacted credit dynamics, lending, and the wider financial system
Private credit surged in 2025, fueled by lighter regulation and attractive borrowing terms. While this boom introduced more flexibility for borrowers and new partnership opportunities for banks, it also shifted lending dynamics – and with it came greater opacity, weaker underwriting and reduced visibility into systemic exposures.
In 2026, as private credit becomes increasingly interconnected with the regulated world of banking – particularly as private equity funds become major investors in bank credit risk transfers – banks will need to invest in robust capital planning and AI early warnings systems to build resilience and capture second-order effects.
Banks turned to predictive intelligence to tackle rising fraud
Fraud stood out as one of banking’s biggest challenges in 2025, amplified by rising sanction enforcement. With the UK’s new Failure to Prevent Fraud (FtPF) introducing unlimited fines, banks are shifting fast—moving from static compliance to proactive risk management.
This includes embedding unified intelligence and AI into due diligence and entity verification, enhancing screening for high-risk jurisdictions, and using real-time anomaly detection to identify legacy, long-tail fraud. This proactive mindset will shape how banks approach not only fraud, but broader operational risk and resilience strategies in 2026 – helping them to stay ahead of emerging threats, avoid costly settlements and reputational damage.
We saw proactive risk management transform the credit decision process
In the backdrop of 2025’s volatile environment, banks are making a decisive shift from reacting to risk to anticipating it. Institutions are integrating real-time, forward-looking analytics across their portfolios – using stress testing, scenario analysis, early warning systems, and model lifecycle management to inform decisions before risks materialize. This evolution is reshaping risk leadership, where the CRO is becoming a strategic partner: steering portfolio optimization, guiding underwriting, and turning proactive risk management into a true competitive edge.
In 2026, a technology framework – AI models, real-time data and cloud analytics – will be the backbone of agile lending, with predictive analytics and real-time monitoring reshaping underwriting and pricing.
Banks should invest in their stress-testing, scenario analysis and early warning capabilities – alongside the data and models required to support these functions – to spot risk and opportunity faster. Banks that employ consistent monitoring tools to identify first instances of stress or credit deterioration will be both quicker and smarter with their remediation actions. Appropriate model lifecycle management – from model risk, validation and governance – will also be crucial, and technology will be a key enabler of this – supporting model centralization, prioritization and automated data lineage.
