The biggest financial services challenges in 2026 include artificial intelligence (AI)-driven fraud, regulatory change, margin pressure, credit and market risk, legacy systems, fragmented data, AI governance, nonbank competition, changing customer expectations, and talent shortages.
These challenges affect institutions differently, but they share a common problem for finance leaders: assumptions can change faster than budgets and reporting cycles. A shift in funding costs, fraud losses, or credit quality can alter profitability before the next scheduled forecast.
Understanding these risks is the first step. The harder task is connecting them to reliable financial plans, measurable costs, and timely management decisions.
In this guide, you’ll explore the 10 biggest financial services challenges in 2026, from AI-driven fraud and regulatory change to margin pressure, credit risk, legacy systems, fragmented data, and talent shortages. You’ll also see how finance teams can use rolling forecasts, scenario modeling, and profitability analysis to respond to changing assumptions.
The US banking industry entered the second half of 2026 with healthy aggregate earnings, strong capital levels, and improving credit metrics. The challenge for individual institutions is determining how much of that strength can be sustained as funding conditions, technology investments, and competition change.
According to the Federal Deposit Insurance Corporation's (FDIC) second-quarter 2026 banking results, 4,238 insured institutions reported $90.1 billion in aggregate quarterly net income, up 12% from the previous quarter. Industry net interest margin reached 3.32%, increasing by one basis point.
Those figures point to a resilient sector, but aggregate results can conceal considerable differences between banks. Deposit composition, loan portfolios, fee income, operating costs, and exposure to specific markets all influence individual performance.
Deloitte's 2026 Banking and Capital Markets Outlook identifies several pressures that remain relevant despite strong capital positions:
Industry-wide results provide context, but management decisions need institution-specific forecasts. A bank with growing fee income may still face pressure in its lending business. Another may maintain healthy margins while absorbing rising technology or customer-acquisition costs.
The financial services industry trends that matter most are therefore the ones that change an institution's earnings assumptions, risk exposure, or investment priorities.
Each challenge can affect revenue, expenses, capital allocation, or financial reporting. The priorities below can help finance teams focus their analysis, with the most important challenges varying by institution.
|
# |
Challenge |
Main 2026 driver |
First finance-team priority |
|
1 |
AI-driven fraud and cybercrime |
Scalable impersonation, scams, and cyber threats |
Model fraud-loss and response-cost scenarios |
|
2 |
Regulatory change and reporting burden |
Changing supervisory and reporting expectations |
Map reporting-data ownership and controls |
|
3 |
Margin pressure |
Changes in rates, funding costs, and asset yields |
Refresh rate and volume assumptions |
|
4 |
Credit and market risk |
Portfolio concentrations and market volatility |
Stress-test credit losses and earnings |
|
5 |
Legacy core systems |
Aging infrastructure and complex integrations |
Prioritize financially material dependencies |
|
6 |
Fragmented, low-quality data |
Inconsistent definitions and disconnected systems |
Establish governed planning dimensions |
|
7 |
Putting AI into production safely |
Model performance, data privacy, and accountability |
Define financial review and approval controls |
|
8 |
Fintech, nonbank, and stablecoin competition |
New lending, payment, and distribution models |
Evaluate product and channel profitability |
|
9 |
Customer retention and digital expectations |
Demand for convenient, responsive service |
Link service costs to customer economics |
|
10 |
Digital and AI talent shortages |
Competition for technical and analytical capabilities |
Identify skills gaps and workforce costs |
Financial institutions cannot address every industry risk through the same operating process. Cybersecurity teams manage threats, compliance teams interpret regulations, and risk specialists assess exposures. Finance must understand how these activities affect earnings, budgets, and resource allocation.
The following challenges explain where the pressure arises and what finance teams can do within their responsibilities.
Cybercriminals can use AI-generated content, impersonation, and increasingly sophisticated social-engineering techniques to make fraudulent communications more convincing. Financial institutions also face phishing, account-takeover attempts, payment fraud, ransomware, and attacks involving third-party providers.
The Federal Bureau of Investigation's (FBI) 2025 Internet Crime Report, released in April 2026, recorded nearly $21 billion in reported cyber-enabled crime losses across the United States.
That figure covers reported losses across victims and sectors. It should not be interpreted as losses incurred by financial institutions alone.
For banks and financial services providers, a successful attack can generate multiple financial consequences. Direct losses may be accompanied by customer reimbursements, investigation expenses, legal costs, technology remediation, and additional support requirements.
A budget based only on the previous year's fraud losses may therefore underestimate the cost of a changing threat environment.
Finance teams should work with fraud, security, and risk owners to establish realistic loss and cost assumptions. Useful measures include actual fraud losses, reimbursement expenses, investigation costs, recovery amounts, and investments in preventive controls.
Modeling multiple loss scenarios can help management understand the financial impact of an adverse event. Finance can also compare approved security investments against actual spending and evaluate the financial benefits of alternative control investments.
Fraud prevention remains the responsibility of specialized security and operational teams. Finance's role is to make the financial exposure visible and support funding decisions.
Financial institutions must comply with reporting, governance, disclosure, and supervisory obligations that vary by jurisdiction, institution type, and business activity.
The Federal Reserve's June 2026 Supervision and Regulation Report covers banking conditions, regulatory developments, and supervisory priorities. Its findings reinforce the need for institutions to maintain processes that can respond to supervisory expectations and changes in their risk profiles.
Reporting becomes difficult when the underlying information is spread across general ledgers, treasury systems, operational applications, and risk databases.
Consider a bank preparing an internal management report and a regulatory submission. Both may use similar financial information, but differences in reporting definitions, entity hierarchies, or adjustment rules can create reconciliation work.
A finance team may spend more time identifying the source of those differences than interpreting the financial results.
Finance leaders should document the sources, ownership, definitions, and review procedures for material reporting data.
Common account, product, cost-center, and legal-entity structures can improve consistency across budgets, forecasts, and management reports. Well-defined reconciliation procedures also make it easier to explain changes between reporting periods.
A structured financial reporting process can reduce manual preparation and improve management visibility.
However, management reporting should not be treated as a substitute for regulatory reporting. Compliance specialists must continue to oversee regulatory interpretation, filings, and the controls required for their accuracy.
Interest-rate movements affect banks through several channels:
Net interest income measures the difference between interest earned on interest-bearing assets and interest paid on funding sources.
Even when net interest income increases, profitability can weaken in a particular product or segment if funding costs, credit losses, or operating expenses rise faster than revenue.
The FDIC reported an industry net interest margin of 3.32% in the second quarter of 2026, a slight increase from the previous quarter. That improvement does not mean every bank experienced the same margin trend.
A bank with a large share of rate-sensitive deposits may face different pressures from one funded primarily through lower-cost deposits. Loan repricing schedules and the mix of earning assets also matter.
Finance should model interest-rate changes alongside deposit balances, funding costs, asset yields, and changes in portfolio composition.
The analysis should separate pricing, volume, and mix effects wherever the underlying data supports. This helps management determine whether a margin change reflects rate movements, customer behavior, or a change in the products generating income.
Regular budget forecasting allows finance teams to refresh expected earnings as material assumptions change rather than waiting for the next annual planning cycle.
Product-level analysis can then identify which lending, deposit, or fee-generating activities contribute the most to profitability.
Credit losses, asset valuations, and market conditions can alter earnings and capital requirements even when the broader banking sector remains financially resilient.
The Federal Reserve's May 2026 Financial Stability Report identified vulnerabilities involving asset valuations, household and business borrowing, financial-sector leverage, and funding conditions.
The report also noted areas requiring continued attention, including some private-credit borrowers and relatively elevated delinquency rates in certain consumer-credit categories.
The financial implications depend on an institution's portfolio.
For example, a lender with concentrated commercial real estate exposure may need to model refinancing difficulties, collateral-value changes, and potential losses. A firm with market-sensitive investments may be more exposed to valuation changes or shifts in liquidity conditions.
Environmental, social, and governance (ESG) considerations can also affect financial exposure where they create material credit, operational, legal, or market risks. Climate-related collateral exposure, for instance, may be relevant to particular loan portfolios, but it should not be assumed to have the same significance for every institution.
Finance teams should translate approved risk assumptions into expected financial outcomes.
Relevant inputs can include delinquency rates, portfolio growth, loss assumptions, recoveries, interest-rate sensitivity, and market-dependent revenue.
Scenario planning helps finance compare plausible outcomes. A downside case could test higher credit losses alongside weaker loan growth and rising funding costs.
The resulting forecasts should be reviewed with risk and treasury teams. Finance should not independently establish formal credit-risk assumptions that belong to those functions.
Years of acquisitions, product expansion, and technology changes have left many financial institutions operating multiple systems that were never designed to work together.
Older core-banking platforms can be expensive to modify and difficult to integrate with newer applications. In some institutions, finance teams still depend on manual exports and spreadsheet reconciliations to combine information across products, entities, and reporting periods.
The Office of the Comptroller of the Currency's (OCC) Spring 2026 Semiannual Risk Perspective identifies operational and compliance risks alongside credit and market risks.
For finance teams, legacy technology creates two related problems:
Replacing a core system may improve operational flexibility, but the project can also introduce integration costs, migration risks, parallel-running expenses, and additional vendor dependencies.
Finance should identify which technology constraints have the greatest impact on reporting accuracy, preparation time, and management decisions.
Start with the workflows that require repeated manual reconciliation or produce delayed financial information. Estimate their operating cost and the investment required to improve them.
For major modernization programs, finance should maintain a business case that separates implementation costs, recurring expenses, expected benefits, and the timing of those benefits.
The analysis should also track whether improvements actually occur after implementation. A modernized system has limited financial value if the organization continues to maintain the same manual reporting processes around it.
Financial institutions collect information across lending, deposits, payments, risk, treasury, customer management, and accounting systems.
Problems arise when those systems apply different definitions to the same financial measure.
One business unit may classify revenue by product, another by customer segment, and a third by legal entity. Finance must reconcile those views before it can reliably compare profitability or explain variances.
The Basel Committee on Banking Supervision's January 2026 publication on effective risk data aggregation and reporting identifies data lineage, fragmented ownership, and legacy infrastructure as continuing challenges for banks.
Data lineage refers to the ability to trace information from its original source through the transformations that produce a reported figure.
Without that traceability, a discrepancy can require lengthy investigation. Finance may know two reports disagree without knowing which system, mapping, or adjustment caused the difference.
Finance should establish consistent dimensions for accounts, cost centers, products, customers, and legal entities.
Material financial measures need documented sources, calculation logic, and reconciliation procedures. Teams should also identify who approves changes to reporting definitions and how those changes affect historical comparisons.
A common planning structure makes it easier to connect actuals, budgets, and forecasts.
It does not resolve every enterprise data problem. Data quality still depends on upstream systems, ownership, and governance. The immediate objective is to make the information used for financial decisions consistent and explainable.
Financial institutions are evaluating AI for fraud detection, customer support, document processing, financial analysis, and internal operations.
Moving these applications into production introduces practical questions about accuracy, accountability, data privacy, security, and human oversight.
The National Institute of Standards and Technology's (NIST) Generative AI Risk Management Profile identifies risks such as inaccurate generated information, privacy concerns, and harmful bias.
These risks matter when AI-generated outputs influence financial decisions.
An AI-assisted forecasting process, for example, could produce an apparently reasonable projection using incomplete historical data. If the underlying assumptions are not visible, finance may struggle to explain or challenge the result.
The same issue can arise with automated variance explanations that identify a pattern but fail to distinguish correlation from an actual business driver.
Finance should begin by identifying the workflows where AI influences assumptions, forecasts, reports, or financial recommendations.
For each use case, teams should establish the source data, accountable owner, validation method, approval process, and escalation procedure.
Human review remains particularly important when outputs affect budgets, capital allocation, financial reporting, or management decisions.
The appropriate starting point is a controlled, measurable use case. A team considering AI-supported finance automation should determine which manual task the technology will replace, how it will measure performance, and what happens when an output is incorrect.
Model-risk, compliance, and information-security teams must continue to oversee their respective controls.
Traditional financial institutions increasingly compete with fintech providers, nonbank lenders, payment platforms, and digital-asset businesses.
These competitors can change pricing expectations, payment economics, customer-acquisition costs, and the demand for particular banking services.
Stablecoins have also become a more concrete regulatory and competitive consideration.
On July 18, 2025, the United States enacted the Guiding and Establishing National Innovation for U.S. Stablecoins Act, known as the GENIUS Act. The law establishes a federal regulatory framework for payment stablecoins.
Its implications for individual institutions depend on their business model, regulatory status, and participation in payment and digital-asset markets.
For finance teams, the challenge is evaluating the economics of new offerings against existing products.
A digital payment service may attract customers and transaction volume while generating lower fees per transaction. A new lending channel may grow originations but require higher acquisition spending or carry a different risk profile.
Revenue growth alone does not establish that a product is financially attractive.
Finance should compare product and channel profitability using consistent assumptions for revenue, funding costs, servicing expenses, technology costs, and risk-related losses.
Scenario models can test the effect of changing fees, adoption rates, transaction volumes, or customer-acquisition costs.
Management should also distinguish between investment-stage products and established offerings. A new service may reasonably operate below mature-product return targets during an approved growth period.
Customers expect financial services to be accessible, reliable, and responsive across digital and traditional channels.
These expectations are particularly visible in retail banking.
The American Bankers Association's 2025 Preferred Banking Methods research found that 54% of surveyed US adults preferred mobile apps as their primary banking method, while 22% preferred online banking.
The findings come from a survey of 4,403 adults conducted in October 2025. They describe consumer preferences and should not be generalized directly to institutional or commercial-banking customers.
Digital adoption creates opportunities to reduce certain transaction and service costs, but the economics are not automatic.
A financial institution may reduce branch transactions while increasing spending on applications, cybersecurity, cloud services, customer support, and third-party vendors.
Customer retention adds another consideration. Losing an established customer relationship may affect deposits, recurring fees, lending opportunities, and acquisition costs.
Finance should connect customer-service investment to measurable operating and commercial outcomes.
Useful measures include service costs by channel, product profitability, customer-acquisition spending, retention assumptions, and the costs associated with maintaining digital services.
For major customer-experience investments, compare the expected financial benefits with implementation and ongoing operating expenses.
Finance should also distinguish between reduced processing costs and genuinely improved customer profitability. A cheaper transaction channel does not necessarily produce a more profitable customer relationship.
Financial institutions need employees who understand finance, data analysis, cybersecurity, automation, and risk governance.
Finding those capabilities can be difficult, particularly when organizations compete for specialists with experience across multiple disciplines.
The World Economic Forum's Future of Jobs Report 2025 found that 63% of surveyed employers identified skills gaps as a major barrier to business transformation.
The finding covers employers across industries and geographies rather than financial institutions alone. It nevertheless provides evidence of broader competition for technical capabilities.
Finance departments face an additional challenge. Employees increasingly need to understand the data and assumptions behind automated analysis, even when they are not responsible for building the technology.
A forecasting process maintained by one specialist can also become difficult to sustain if model logic is undocumented or knowledge is concentrated within a small team.
Finance leaders should identify the skills required for forecasting, reporting, data management, scenario analysis, and oversight of automated processes.
Workforce planning can help connect capability requirements to hiring costs, training budgets, staffing assumptions, and implementation timelines.
Documenting key models and reporting procedures can also reduce dependence on individual employees.
Training should focus on the decisions finance staff must make. Understanding how to challenge a credit-loss assumption or explain a forecast variance may be more immediately useful than learning to build an AI model.
The practical response to these financial services challenges is to build planning processes that can accommodate changing assumptions without requiring a complete rebuild each time.
Scenario planning tests alternative outcomes, while driver-based forecasting links financial projections to the underlying factors that influence performance.
In financial services, those drivers may include interest rates, deposit balances, loan volumes, credit losses, fee income, fraud costs, and operating expenses.
Four actions can make financial planning more responsive:
These actions improve the quality and timeliness of financial decisions. Their effectiveness depends on credible inputs, accountable owners, and a regular review process.
A forecast that updates automatically but uses outdated assumptions is still unreliable. Finance teams need both current data and the judgment to interpret it.
Financial services organizations need to connect recorded performance with their approved plans and forward-looking assumptions.
Actuals represent financial results from completed periods. Approved decision packages are documented spending or investment proposals authorized by management, such as technology programs, departmental budgets, hiring plans, or product initiatives.
Finance teams need to compare those commitments with actual spending and revised forecasts.
Limelight can support this work through connected budgeting, forecasting, financial modeling, and management reporting.
Its planning and forecasting capabilities allow finance teams to maintain budgets, update operating assumptions, develop scenarios, and bring planning inputs together across departments and entities.
Its financial reporting capabilities help teams compare actuals with budgets and forecasts, investigate variances, and review performance across reporting dimensions.
For a financial institution, these capabilities can support several practical activities:
Limelight is an FP&A platform. It does not replace specialized fraud-detection systems, cybersecurity controls, regulatory-reporting solutions, credit-risk engines, model-risk governance, or compliance-management programs.
Those systems and processes have separate responsibilities. FP&A helps finance understand their financial implications, evaluate investment decisions, and explain their effects on forecasts and profitability.
For institutions struggling with disconnected budgets, manual reporting, or slow forecast updates, the next step is to evaluate whether their planning processes can support the decisions management needs to make.
See how Limelight can help your finance team. Book a demo.
AI-driven fraud and cybercrime are among the biggest challenges because they can create direct losses, operating costs, customer-service demands, and reputational damage at the same time. Financial institutions must address these threats while maintaining reliable planning, reporting, and profitability analysis.
The main banking challenges include margin pressure, changing regulation, credit and market risk, fraud, legacy technology, fragmented data, digital competition, and talent constraints. These issues affect earnings forecasts, operating expenses, capital planning, product profitability, and management reporting.
AI increases risks by making fraud and impersonation attacks easier to scale while creating new concerns around model performance, data use, explainability, and governance. Firms need clear approval processes and human review when AI-supported outputs influence financial, risk, or customer decisions.
Key trends include greater reliance on fee income, pressure on net interest income growth, AI adoption, rising fraud exposure, digital competition, and continued investment in data and technology modernization. Finance teams need more frequent forecasting and clearer profitability reporting to manage these changes.
Finance teams can plan for volatility through rolling forecasts and scenarios that test rates, deposit volumes, funding costs, credit losses, fraud losses, and product demand. Using reconciled actuals and common planning assumptions helps management evaluate changes quickly and make more informed resource-allocation decisions.