Banking trends 2026: What's shaping the industry

For several years, resilience has defined global banking. Strong capital positions, ample liquidity and diversified revenue streams allowed banks to absorb shocks like inflation, rate hikes, geopolitical disruption and uneven growth.

In today’s environment, resilience alone is not enough. The drivers and dynamics impacting banks are mercurial, volatility is persistent and the only certainty is change. Banks are being pushed to move from resilience delivered by sheer scale to decisive precision across the balance sheet. Capital allocation, liquidity management and asset-liability decisions are becoming more granular, more frequent and more interconnected. What was once managed through periodic review is now expected to operate as a continuous, scenario-driven discipline.

Resilience remains essential, but precision will ultimately determine performance.

“It used to be garbage in, garbage out. With AI, it’s garbage in, garbage scaled.” SAS CMO Jenn Chase described the data quality, management and governance challenges that banks are facing perfectly. The adage rings true, but what has changed is the scale of the consequences. Data quality issues aren’t causing isolated errors: They’re propagating problems, inconsistency, uncertainty and risk. This isn’t the AI at scale that banks have bet on.

Banks are investing in and deploying AI faster than they’re fixing the foundation that sits beneath it. They’re also realizing that AI initiatives aren’t stalling because of underperforming models. They’re stalling because the data feeding the models cannot be trusted, reused or explained. Inaccurate data produces inconsistent decisions. Missing lineage makes outcomes hard to defend. Poor transformation logic introduces hidden bias. And poor access controls can create risk or slow execution. Democratization without governance amplifies confusion instead of insights. This is the definition of garbage in, garbage scaled.

The pressure to act and transform data foundations is mounting, and banks must refocus their eyes and their efforts, not on the model, but on what sits before and beneath the model and establish data quality standards and governance that ensure safe and effective use across the enterprise.

Investment in AI has surged across banking since 2024. Experimentation expanded, pilots multiplied and expectations grew. What has become clear since is that this progress is not even. Initiatives are delivering isolated wins but are failing to scale, integrate or earn lasting trust. With so much investment and expectation, banks are facing an inflection point where AI maturity is no longer optional, and the question is shifting from “Can this model work?” to “Can this system be trusted to operate at the center of the bank?”

Attaining maturity demands more than impressive models: It requires governed data, enterprise decisioning, clear ownership and an orchestration layer that ensures intelligence moves coherently across the organization. Without this discipline, AI behaves like an ensemble without a conductor. Individual instruments perform, but the result lacks harmony, timing and control. Banks that are seeing demonstrable impact and value have converged on a clear path forward – they recognize that intelligence and orchestration rely on a strong foundation of governance and that this effective governance layer accelerates speed, efficiency, scale and drives innovation that delivers harmonized performance.

JPMorgan experienced a 6% productivity lift, with strong gains in operations and servicing functions where decisions are repeated at scale, with 40% – 50% growth expected.

- SeekingAlpha

Banks are approaching a point where legacy technology no longer merely slows progress: It actively works against them. Systems built for stability and scale in a previous era are poorly suited for real-time decisioning, enterprise orchestration and AI-driven execution. What once provided reliability now introduces drag, increases and weakens resilience. The risk is more than inconvenience and incremental inefficiency; it’s losing the ability to evolve at the speed the operating environment demands.

McKinsey’s Global Banking Annual Review 2025 spells it out clearly: “Banks must align modernization, cloud, analytics and AI to capture the next growth curve, yet many report that legacy platforms and accumulated technical debt are limiting returns.”  Achievement is constrained by the gravitational pull of fragmented, brittle platforms that are expensive to maintain, difficult to integrate and increasingly fragile under modern workloads.

Platform rationalization and modernization are the path forward. Banks must reduce their technology estates, modernize data and analytics environments and move decisioning and orchestration onto cloud-native platforms to regain speed, resilience and the ability to operationalize intelligence when and where it matters.

Fraud, financial crime and cybersecurity are no longer separate threat vectors; they are converging into a single, fast-moving threat ecosystem where criminals pivot between social engineering, account takeover, malware and mule networks as if operating across one continuous front. AI is accelerating this convergence, making attacks more adaptive and easier to scale. Once episodic incidents, these attacks are now persistent pressure points across customers, channels and operations. Europol’s EU Serious and Organized Crime Threat Assessment 2025 highlights how AI-enabled techniques, like voice cloning and live deepfakes, are amplifying fraud, identity theft and extortion.

Banks are absorbing real casualties, measured in financial losses and customer harm. UK Finance’s Annual Fraud Report 2025 details the scale and evolution of scams, showing how criminals exploit digital channels and force banks and ecosystem partners to continually strengthen prevention capabilities and intelligence sharing. On the cyber side, Verizon’s 2025 Data Breach Investigations Report notes that synthetically generated text in malicious emails has doubled over the past two years, signaling that social engineering is becoming more industrialized. Together, these signals reinforce a single reality. Banks are no longer defending against isolated threats: They are operating in a convergent multidimensional threat landscape.

Banks that are winning the battle are responding by moving toward adaptive, intelligence-driven defenses that operate continuously across domains and are fusing signals from fraud, AML, payments, identity and cyber telemetry to support real-time decisioning. In this AI-driven era of crime, accountability, governance and execution must align to ensure a coordinated response that is explainable and defensible. Banks must be able to sense, decide and respond faster than their adversaries.

Banks have spent years talking about customer centricity. Yet customer sentiment tells a different story. Uniform marketing messages are delivered without context, product offers miss the mark and engagement strategies rely on broad segments rather than individual signals. Banks possess rich insight into financial behaviors, life stages and changing needs, but those insights rarely translate into action. The gap isn't visibility; it's the will and capability to act on understanding in ways that matter. Missed signals and impersonal interactions leave customers with a quiet but damaging conclusion: I know you can see me, but do you really know me?

The breakdown surfaces first in acquisition and engagement, where intelligence should determine not just what a bank communicates but when, where and why. Instead, insights are often vague instead of clarifying, and they stop at the campaign level rather than persisting through every touchpoint across every channel. As customers move through the life cycle, experiences continually reset. Personalized marketing gives way to generic onboarding. Trusted digital interactions break down during fraud, servicing and branch experiences. What should feel like a continuous relationship becomes a series of disconnected transactional interactions. Advanced analytics and AI can help close this gap, but when insights are siloed or inconsistently applied, AI amplifies irrelevance rather than building trust.

Pressure to close this gap is mounting. Regulators are expanding scrutiny beyond individual touchpoints to examine fair treatment, suitability and outcomes across the full customer life cycle, raising the stakes for banks that cannot demonstrate consistent, evidence-based engagement. At the same time, customer expectations are being shaped by organizations that recognize intent and adapt in real time. As competition intensifies, deposits grow more mobile and switching friction falls, the imperative is clear: Customer centricity must move from marketing narrative to operational reality, where relationships built on understanding, recognition and trust become the genuine differentiator and not just a brand promise. 

Quantum computing is moving from imagination and theoretical promise to real-world exploration. While broad commercial availability remains on the horizon, financial institutions are already testing how quantum and hybrid quantum-classical approaches could reshape some of banking's most complex challenges. The real question is not just what quantum can accelerate, but what becomes possible when today's computational limits no longer apply. Quantum speed is significant, but the deeper opportunity lies in expanding the range of challenges banks can realistically explore, model and optimize.

Many of banking's hardest decisions sit at the edge of what current computing systems can handle. Portfolio optimization, scenario analysis, fraud pattern detection, liquidity management and network analysis all involve vast combinations of variables that grow exponentially as complexity increases. Quantum introduces a fundamentally different approach to these problems, enabling the simultaneous exploration of possibilities that would otherwise require enormous time and computational resources. This potential has made banks take notice and track quantum's evolution closely.

Banks exploring the edges of quantum capability are doing so with both curiosity and discipline. Early efforts focus on learning through pilots, partnerships and hybrid experimentation that blends classical analytics, AI and emerging quantum techniques. These explorations reflect a clear understanding that the future value of quantum will depend on banks having strong data foundations, advanced analytics and enterprise decisioning already in place. The institutions that invest in those foundations now will be best positioned to move quickly as quantum transitions from emerging possibility to practical reality.

$400 - $600 billion

Potential economic value from quantum computing in the finance industry by 2035.

- McKinsey & Company – Quantum Technology Monitor, June 2025