According to Deloitte Research Center 2026 is likely to deliver broadly stable bank profitability in Europe but the central strategic battleground for banks will be how quickly and safely they embed AI, data and automation into front-line and back-office processes (1). AI is simultaneously a major growth lever (productivity, new revenue streams, improved client engagement) and a governance/risk challenge (model risk, fraud, over-investment). A

t the same time, geopolitical fragmentation and a stickier, more volatile inflation regime change the risk landscape for lending and commodity/energy exposures — increasing demand from banks for realtime, data-driven risk tooling. Deloitte states that these dynamics create a near-term imperative: deploy AI in high-ROI, low-friction pockets (compliance, automation, client engagement, credit-monitoring) while strengthening governance, explainability and operational controls (1). 

 

Macro & revenue outlook, and what it means for banks  

Morningstar judges 2026 a “Goldilocks” year: weaker-than-2024 NII (Net Interest Income) but stabilising NIMs (Net Interest Margin) and positive loan growth will likely produce moderate net-interest-income gains, while non-interest income should remain supportive though less outsized than in 2025 (2-3). That backdrop means banks cannot rely on cyclical tailwinds alone to lift ROE — the marginal gains in profitability will come from structural efficiency and fee diversification rather than macro repricing (2). Practically, retail banks should prioritise automation of origination and servicing to protect margins, while private banks should focus on converting advisory capability into fee-generating digital products. 


AI: where banks must invest in 2026  

J.P. Morgan describes AI as a defining frontier for 2026 and quantifies the scale — from hyperscaler capex to sovereign AI programs — underscoring the depth of investment and competition for infrastructure (2). For banks, this translates into three pragmatic investment priorities: 

1. Operational automation

Deployment of NLP-driven document intake, automated KYC/KYB, and straight-through processing for loan origination to reduce unit costs and processing times. 

2. Risk & compliance

Integration of AI for anomaly detection, AML pattern recognition and model-assisted provisioning to detect stress earlier and reduce manual false positives. 

3. Client-facing intelligence

Implementation of hybrid-human AI for next-best-action, personalised product recommendations, and scaled wealth insights for private clients. 

However, J.P. Morgan and Morningstar both caution against indiscriminate AI spend: banks should favour explainable models, staged rollouts, and a strict ROI lens to avoid “AI over-investment” and governance shortfalls. That means prioritising plug-and-play, vendor solutions or co-built modules that offer auditability and rapid time-to-value (2-3). 

 

Fragmentation, energy & trade risks — the demand signal for risk-tech  

J.P. Morgan’s “think fragmentation” thesis shows how onshoring, tariffs and energy-security concerns will reorder sectoral credit risk and increase the frequency of localized shocks. For banks, this implies more frequent sectoral stress events (eg. autos, shipping, energy-intensive industries) and the need for granular, near-real-time portfolio analytics that incorporate trade flows, commodity prices and regional policy changes (2).

Banks should therefore accelerate adoption of risk platforms that: 

  • ingest cross-border trade and supplier-chain signals, 
  • perform scenario-driven sector stress tests, and 
  • produce actionable limits/alerts for relationship managers and credit committees. 

FinTechs that specialise in linking macro/trade data with borrower-level signals will be natural partners for banks seeking to operationalise fragmentation-risk monitoring. 

 

Client experience & revenue diversification: AI-enabled advisory and personalisation

According to Deloitte Research Center, with non-interest income likely to be more muted than in 2025, banks must monetise customer relationships more effectively (1). For retail banks, this could mean using AI to drive targeted, permissioned cross-sell and to implement personalised pricing and product nudges for deposits, cards and consumer loans. For private banks, AI-enabled portfolio analytics and meeting-preparation tools let advisors serve more clients with higher-quality insight (hybrid robo + human models). Crucially, both segments need “explainable personalisation” which favours vendors providing audit trails, model documentation and human-in-the-loop workflows. 

M&A and strategic tech procurement  

Although M&A momentum has picked up domestically in several EU markets, large cross-border bank deals remain difficult given political and regulatory barriers. Morningstar expects banks to pursue scale and fee-income diversification, but in practice banks will increasingly acquire technology or enter strategic partnerships to obtain those capabilities faster and with less political friction (3). For bank CFOs and heads of strategy, 2026 could read as an opportunity to pursue targeted technology M&A (asset managers, payments, data platforms) and to carve out budgets for partnerships that accelerate AI adoption. Structured partnerships are likely to be favoured over outright cross-border takeovers. 

Governance, operational risk & model safety  

Both reports emphasise that AI’s upside is matched by governance risks: model opacity, data bias, misuse for fraud, and operational-control failures (1-2-3). Banks' recommendation, therefore, is to treat model risk management (MRM) and AI governance as first-class programs in 2026: standardise model documentation, invest in explainability toolkits, perform continuous monitoring, and build incident-response playbooks for AI-related operational incidents (1). FinTech vendors should be evaluated on their ability to provide reproducible, auditable models and to integrate into banks’ MRM workflows. 

 

Conclusion: 2026 is the year of execution

2026 will be a year of execution: macro conditions are broadly supportive but not sufficient to drive step-change ROE. Banks that win will be those that deploy AI where it measurably improves margins, risk detection, and client monetisation — while building robust model governance and prioritising capital-efficient partnerships. The two reports agree: AI is the central strategic battleground; fragmentation and inflation raise the premium on real-time risk intelligence and resilient infrastructure. 

 

Sources: 

  1. Deloitte. (2025b, November 12). 2026 banking and Capital Markets Outlook. Deloitte Insights. https://www.deloitte.com/us/en/insights/industry/financial-services/financial-services-industry-outlooks/banking-industry-outlook.html  

  2. J.P. Morgan — Outlook 2026: Promise and Pressure (Position for the AI revolution; Think fragmentation; Prepare for inflation’s structural shift). 

    JPMorganOutlook2026PromiseandPr… 

  3. Morningstar DBRS. Home. (n.d.). https://dbrs.morningstar.com/research/467824/2026-european-banking-sector-outlook-neutral-a-goldilocks-year-ahead#:~:text=Morningstar%20DBRS%20published%20a%20new,to%20improving%20earnings%20in%202026.  
Maria Ceruti
Maria Ceruti
Maria Ceruti joined Gambit Financial Solutions as a Product Marketing Specialist, where she played a pivotal role in building the company’s marketing ecosystem from the ground up. Her responsibilities spanned product positioning, communications, content strategy, and event coordination. She successfully integrated ESG narratives and compliance themes into the company’s messaging, enhancing Gambit’s visibility in both the financial and fintech sectors. Her contributions bridged product innovation with thought leadership, reinforcing the brand’s credibility and relevance.