Banking Trends for 2026

As 2026 progresses, several simultaneous factors are forcing a reset of priorities. With margin tailwinds fading, banks are operating in a more constrained environment as other powerful forces such as Artificial Intelligence (AI), shifting consumer behavior and escalating fraud risk intensify. These dynamics are converging at the same moment legacy technology and operating models are nearing the end of their useful lives, compressing the room for error and magnifying the cost of slow execution. 

For bank leadership teams, the question is no longer whether to transform, but where to focus and how quickly intent can be translated into execution in a cost constrained environment. AI will be central to this shift, but it is not a silver bullet. The banks that will gain an advantage are those that move beyond surface-level AI deployment and instead industrialize AI within simplified technology estates, resilient operating models and disciplined decision-making. Rather than using AI as a surface layer for tactical improvements, the real prize lies in embedding AI into core processes and workflows so that it drives measurable productivity, faster decision making, stronger control and sustainable cost improvement. 

The dynamics below set out how the pressure of profitability normalization, reinforced by changing customer expectations, technology complexity and rising fraud, will shape bank priorities in 2026 and how institutions can turn constraint into competitive advantage.  

1. Profitability normalization intensifies the focus on cost optimization  

After several years of margin expansion driven by higher interest rates, the next phase of performance will be shaped less by rate cycles and more by balance sheet discipline, granular profitability insight and active portfolio management. With rates largely expected to stabilize or fall through 2026, the industry is shifting from interest margin driven growth to a far more cost-sensitive operating environment. 

In 2026, deposit competition, structural hedging strategies and repricing asymmetry across markets and product mixes move from marginal gains to core strategic levers as margin sensitivity increases. At the same time, finance functions are under increasing pressure to deliver faster, decision-grade insight, with profitability transparency by segment, product and channel rather than only at legal entity level. 

Whilst banks operated in near-zero rate environments after the financial crisis, today’s cycle is different. Banks are more complex, digital channels are non-negotiable and technology spend is unavoidable, resulting in a structurally higher cost base. At the same time, persistent inflation, faltering growth and AI-driven uncertainty continue to weigh on policymakers. In this context, cost optimization becomes even more of a strategic imperative. Many banks are still carrying significant architecture and infrastructure debt due to duplication created through M&A, over-engineered controls layered onto broken processes, and manual operations that were masked by strong top-line growth.  

As revenue compression increases, these inefficiencies directly erode returns. In 2026, meaningful cost reduction will not come from broad headcount cuts or isolated efficiency programs. It will come from simplification at source, rationalizing application estates, automating end-to-end processes and redesigning operating models so that controls, data and execution are aligned by design rather than managed through manual intervention. 

Banks that will outperform are those that will treat cost as a core design constraint, not a budgeting exercise, using technology, data and AI to remove friction from the operating model at source, rather than continually managing around it. With less upside from revenue growth, this shift from periodic efficiency drives to structural simplification becomes a competitive necessity. 

2. Banks must reshape products and services for Gen Z and Gen Alpha  

Consumer expectations are fragmenting, and traditional customer segmentation is evolving. Banks are increasingly having to shape products and services around cash-light, mobile-first behaviors and more fragmented credit utilization (i.e., mixing debit, wallets, BNPL and short-term credit alongside traditional lending). This shift is already material: according to Morgan Stanley more than a quarter of US consumers have used Buy Now, Pay Later as of mid-2025, reflecting growing comfort with embedded, short-term credit at the point of purchase. 

As muted economic fundamentals including persistent cost of living pressures drive weaker cashflow, consumers are justifying a higher appetite for easily accessed debt like BNPL, as they look for more flexible ways to manage day to day spending. This continued uncertainty from consumers in cashflow is visible in spend data: shoppers remained restrained through 2025, with December marking the eighth consecutive drop in UK consumer spending and reinforcing a 2026 mindset defined by value-seeking and AI-enabled ‘savvy shopping’ – where consumers rely on algorithmic price comparison, personalized offers, spend insights and automated decision support to optimize value. Generation Alpha will embody this, entering financial decision-making through non-bank platforms, creator economies and embedded finance, where spending behavior is increasingly shaped by immediacy, personalization and social influence rather than traditional financial planning.   

For banks, this creates both tension and opportunity. Customers increasingly expect real-time decisions, personalized pricing and credit, and seamless digital journeys. Despite this, many banks are still constrained by batch processing, fragmented data and channel-specific operating models, leading to slower innovation. As super-apps and telcos like WeChat, Alipay and Orange Money continue to serve billions of users and capture daily financial engagement outside banking channels, banks face increased vulnerability to attrition outside of the traditional financial ecosystem. 

The winners will be those that redesign products and decisioning around how customers actually behave, embedding real-time data, pricing and credit into seamless digital journeys rather than forcing new expectations onto legacy models. In 2026, banks that simplify operating models and move from batch-led processing to always-on, personalized engagement will be best positioned to retain trust, grow relevance with younger cohorts and compete effectively with non-bank platforms. 

3. Will 2026 be the year banks reduce human capital and grow with a much smaller, AI augmented, workforce?

Despite sustained investment in cloud, data platforms and AI tooling, many banks have more technology but less flexibility. Cloud, data platforms and AI tooling have often been layered onto unchanged architectures and ways of working, limiting value realization. With financial services AI spend projected to reach $97B by 2027, the message is clear: investment only translates into value when complexity is removed, and capabilities are embedded into workflows – from engineering and testing through to operational execution and decision support for frontline and risk teams. Without simplification, AI is more likely to increase cost and risk than reduce it. 

In 2026, the focus must shift from technology adoption to fundamental enterprise redesign, with technology as the catalyst. This means rationalizing application estates and standardiszing platforms, but also rethinking how work is organized, how decisions are made, and how teams are structured. As automation and AI take on more routine processing, monitoring and decision support, banks will need to redesign roles, reduce duplication across functions and reshape workforce composition to reflect a more AI-augmented operating model. Technology advancement will not only reshape systems, but also the structure and size of parts of the organization itself. 

Overall, we do not expect broad-ranging workforce reductions as a direct consequence of AI. Evidence to date suggests that AI adoption is more strongly associated with productivity gains and task redesign than immediate, large-scale job displacement. Research from the Federal Reserve indicates that workers using generative AI report meaningful time savings in routine tasks translating into measurable improvements in output per hour, showing top and bottom-line growth can be achieved simply with proper AI adoption, rather than headcount reductions. Similarly, analysis from the Brookings Institution finds that firms adopting AI often experience growth and reallocation of labor rather than net employment decline. As a result, the more immediate impact for banks in 2026 is likely to be doing more with the same people – automating routine processing, improving decision support and reallocating human effort toward higher-value, judgment-based activities. 

4. Fraud becomes the defining AI battleground

Fraud is no longer a perimeter issue, rather it is embedded across payments, onboarding, account servicing and customer interaction. According to the payment technology company Visa, the cost of fraud and cybercrime has grown over 30% year-over-year for the past six years. As digital volumes grow and fraud techniques become more sophisticated, manual review and rules-based controls are no longer sufficient, especially as human behavior remains one of the largest drivers of cyber risk, with 74% of CISOs citing human error as their top security concern.  

AI will be a differentiator in fraud management, but only where it is deployed responsibly and at scale. Leading banks are moving toward real-time, behavior-based risk scoring, adaptive models that learn as fraud patterns evolve and integrated fraud decisioning across channels and products to counter increasingly industrialized, fast-moving and coordinated fraud activity.  

We increasingly see fraud as a defining customer proposition rather than a back-office control function. Fraud and identity theft are among the most personal and emotionally charged risks customers face, and banks that demonstrably protect customers can turn security into a source of trust, advocacy and market differentiation. When AI is embedded end-to-end across fraud operating models, linking data, controls, customer experience and regulatory expectations, it also enables banks to intervene faster, reduce false positives, and protect customers without adding friction. This must also be paired with deliberate cultural and behavioral change. Sustainable fraud resilience also depends on aligning incentives, improving employee awareness and embedding secure behaviors as the natural way of working. Done well, this shifts fraud from a defensive necessity to an offensive capability, strengthening retention and enabling banks to win customers from competitors rather than simply avoiding trust erosion. 

5. How to win

In an agenda defined by lower margins, rising risk and compressed decision windows, execution becomes the differentiator.  

In 2026, advantage will belong to banks that prioritize simplification, embed AI into decision making, adapt to shifting customer expectations and leverage technology to turn operational processes into differentiators. The pace of change means that there will be no silver bullet to sustainable growth, but those who holistically transform strategies and cultures to truly benefit from technology advancements will create competitive advantage. 

The institutions that will differentiate themselves are those prepared to make hard structural choices early. In 2026, advantage will favor banks that act on four priorities:  

1. Remove structural cost by simplifying technology estates and eliminating duplication across functions. 
2. Transform strategy to become a true beneficiary of AI, whilst embedding a culture of agility and innovation throughout the organization. 
3. Redesign roles and team structures from first principles to reflect an AI enabled, real-time operating model.  
4. Align product design with the next generation of customers and leverage technology to find new ways of differentiating services. 

Those that move decisively on these fronts will improve time to market, resilience and returns while competitors remain constrained by legacy complexity.