Agentic AI Threats in Banking: How Market Research Can Help Businesses Stay Ahead

Agentic AI is taking artificial intelligence in banking beyond generating responses or assisting employees. Increasingly autonomous AI systems can interpret objectives, plan actions, interact with multiple systems and execute tasks with limited human intervention.

For banks, this creates significant opportunities in customer service, fraud management, lending, compliance and operations—but it also introduces new questions around accountability, privacy, transparency, security and customer trust.

As banks evaluate where and how to deploy agentic AI, understanding how customers, employees and other stakeholders perceive these technologies becomes increasingly important. This is where market research can provide valuable evidence to complement technical, regulatory and operational assessments.

What is Agentic AI in Banking?

Agentic AI refers to artificial intelligence systems capable of working towards defined objectives with a greater degree of autonomy. Rather than simply generating a response to an individual prompt, an AI agent may interpret a goal, plan a sequence of actions, interact with different systems and complete multiple steps with limited human involvement.

This distinguishes agentic AI from traditional automation, which generally follows predefined rules, and generative AI, which primarily produces content or responses based on user inputs.

Within banking, potential applications could include:

• Customer service and financial assistance
• Fraud detection and risk monitoring
• Credit and lending processes
• Investment and wealth management support
• Compliance and regulatory workflows
• Internal operations and process automation

As these systems become capable of undertaking increasingly complex activities, banks may need to understand not only their technical capabilities but also how different stakeholder groups respond to greater AI autonomy.

Why is Agentic AI Becoming Important for Banks?

Banks operate in an environment characterised by growing transaction volumes, complex regulatory requirements and increasing expectations for convenient digital experiences. At the same time, they face pressure to improve productivity and control operating costs.

Adoption is already moving beyond experimentation. According to Deloitte, one in three financial institutions is allocating budgets specifically for agentic AI, while several major banks are already investing in agent-based systems and applications. 

Agentic AI may help automate multi-step tasks that previously required employees to move between different systems or manually coordinate processes. It may also allow banks to deliver faster responses and more personalized interactions using large volumes of customer and transactional data.

Competition is another factor. Fintech businesses and digitally native financial platforms continue to influence customer expectations around speed, availability and ease of use. As AI capabilities advance, banks are therefore exploring how autonomous systems could support both customer-facing and internal activities.

However, greater autonomy also means that errors, unintended behavior and weak controls may have wider consequences.

Read Also: How Market Research Strengthens Scenario Planning and Strategic Decision Making

What are the Threats of Agentic AI to the Banking Industry?

Agentic AI can introduce a new layer of risk as banking systems become more autonomous and interconnected. According to BioCatch’s survey of 1,440 fraud-management, AML, risk and compliance leaders at banks across 25 countries, 84% identified AI agents as the industry’s greatest exploitable vulnerability over the next year. The key challenges span decision-making, data security, compliance, customer trust, operational resilience and workforce readiness. 

1. Loss of Control Over Autonomous Decisions

One of the central concerns surrounding agentic AI is the extent to which systems should be allowed to act independently.

If an AI agent performs an incorrect action within lending, payments, fraud management or another sensitive process, the consequences could affect customers and banking operations. Financial institutions therefore need clear boundaries around where human review and intervention remain necessary.

2. Data Privacy and Security Risks

Banking AI systems may need access to substantial amounts of personal, transactional and financial information to perform effectively.

Greater connectivity between AI agents and internal systems may increase concerns around unauthorised access, data leakage and cyberattacks. Through market research in the banking industry, organizations can study how customers perceive AI-related data use and what levels of transparency they expect.

3. Fraud and AI-Enabled Financial Crime

AI can support fraud prevention, but similar technologies may also be used by criminals. The wider cyber threat landscape is already substantial: Kaspersky detected 1,338,357 banking trojan attacks between November 2024 and October 2025 

Automated phishing, impersonation, synthetic identities and sophisticated social engineering could increase the complexity of financial fraud. Banks may consequently face an ongoing challenge as both defensive and malicious AI systems become more capable.

4. Bias and Unfair Financial Decisions

AI models may reproduce or amplify patterns contained within their training data or underlying decision frameworks.

This becomes particularly important when systems influence credit assessments, lending processes or customer segmentation. Decisions perceived as inconsistent or unfair can damage customer confidence and may also create regulatory concerns.

5. Regulatory and Compliance Challenges

Financial services operate within tightly regulated environments, making accountability particularly important.

Agentic AI can complicate questions around who is responsible for an automated action, how decisions can be audited and whether regulatory requirements have been followed throughout an autonomous workflow. These issues may become more significant as regulations around AI continue to develop. Regulators are also acknowledging the difficulty of keeping pace with rapid AI development. In July 2026, CNBC reported that European policymakers and central bankers had warned that traditional rulemaking processes were struggling to match the speed of advances in AI, particularly as agentic systems evolve.

6. Lack of Transparency and Explainability

Customers may expect an explanation when an application is declined, a payment is blocked or another important financial decision is made.

Highly complex AI systems can make it difficult to trace exactly how an outcome was reached. Banks therefore need to consider how autonomous processes can remain understandable to both customers and internal teams.

7. Customer Trust and Acceptance

Customers are unlikely to view every AI use case in the same way.

Someone may be comfortable using an AI assistant for a basic account enquiry but less comfortable with AI influencing a lending or investment decision. This makes customer trust and acceptance a key issue, as banks need to understand where customers welcome automation and where they still expect human involvement. Marketing research in the banking sector can help banks test AI-related messaging, product positioning and communication to understand what resonates with different customer groups. 

8. Operational and Third-Party Risks

AI adoption may increase reliance on external technology vendors, platforms and data providers.

Technical failures, integration problems or weaknesses within third-party systems can create additional operational risks. Banks therefore need visibility into both internal processes and the wider technology ecosystems supporting them. Banking market research can also help organizations assess stakeholder concerns around third-party dependence and operational resilience. 

9. Workforce and Skills Disruption

Agentic AI may change how employees perform everyday tasks rather than simply replacing individual activities.

Banking professionals may increasingly supervise AI workflows, review outputs or handle exceptions. Market research in finance and banking involving employees and other internal stakeholders can help organizations understand attitudes towards these changes, skills gaps and potential adoption barriers.

Why Market Research is Critical for the Banking Industry in the Age of Agentic AI

As agentic AI becomes more embedded in banking, financial institutions need to understand how customers and other stakeholders perceive these technologies. Banking and financial industry research can help businesses gather market data on customer attitudes, stakeholder expectations, adoption barriers and changing behaviors.

1. Understand Customer Attitudes Towards AI in Banking

Primary research can help financial institutions measure awareness, acceptance and concerns surrounding autonomous banking technologies.

Surveys, interviews and qualitative studies can explore which activities customers are willing to delegate to AI and where they continue to expect human involvement.

2. Identify Emerging Market and Customer Trends

Financial industry market research can capture changing customer behaviors, technology awareness and expectations as AI adoption develops.

Banking market research can also help examine stakeholder perceptions of new financial products and service models entering the market, creating a more current evidence base for internal teams.

3. Evaluate New AI-Powered Products Before Launch

Concept testing allows banks to collect feedback on proposed AI-enabled services before wider deployment.

Respondents can evaluate features, usefulness, messaging, ease of understanding and potential concerns. Both qualitative and quantitative approaches can help businesses capture reactions across relevant customer groups.

4. Assess Trust, Risk and Brand Perception

AI implementation can influence how customers view a financial organization.

Market research in the banking industry can help measure trust, comfort with automation, privacy concerns and perceived transparency. Tracking these factors over time can also help organizations understand whether stakeholder attitudes are changing as familiarity with AI grows.

5. Support Customer Segmentation

Attitudes towards autonomous banking may vary substantially by age, income, digital behavior, financial needs and previous experience with technology.

Market research in finance and banking can help collect data across different customer profiles, enabling banks to examine how acceptance and concerns differ between segments rather than assuming customers will respond uniformly.

6. Capture the Voice of Multiple Stakeholders

Agentic AI affects more than retail banking customers.

Studies may include employees, banking professionals, business customers, fintech stakeholders or other specialised audiences. Including multiple respondent groups can provide a broader picture of perceptions, expectations and implementation concerns.

7. Strengthen the Evidence Available to Internal Teams

Banking market research can provide structured primary data that organizations can use alongside operational, regulatory and technical information.

Rather than relying solely on assumptions about how people may respond to AI, banks can conduct targeted research to collect direct feedback from the audiences most affected by new technologies.

How Market Research Can Help Banks Navigate Agentic AI Risks

Different AI-related concerns can be explored through different research approaches.

These studies may combine quantitative surveys with qualitative interviews, focus groups or other methodologies depending on the audience, research question and required depth of feedback.

Read Also: Qualitative Market Research and Customer Journey Mapping for Sales

From AI Risk to Responsible Adoption

Avoiding AI entirely is unlikely to be realistic as automation becomes increasingly embedded within financial services. The more important question is where AI delivers meaningful value and where customers or employees may still expect stronger safeguards, transparency or human involvement.

Through market research in the banking industry, financial businesses can collect evidence around trust, adoption, customer experience and stakeholder expectations. This research can support internal product, customer experience and technology teams by providing a clearer understanding of how intended users respond to proposed AI applications.

The purpose is not to replace technical, regulatory or organizational judgement, but to ensure that the voice of relevant stakeholders is represented alongside these considerations.

Navigate the Future of Agentic AI with Better Banking Industry Research 

Agentic AI is creating new opportunities for banks, but also raising important questions around trust, privacy, transparency and customer acceptance. Banking market research can help financial organizations capture stakeholder perspectives as these technologies evolve.

Founded in 2009, Unimrkt Research conducts multi-industry research across 90+ countries and 22+ foreign languages, following ESOMAR norms and holding ISO 20252 and ISO 27001 certifications. Through Primary, Qualitative and Quantitative Research, Business Research and Research Support Functions, we support reliable data collection across diverse markets.

Looking to conduct reliable financial industry market research around AI, customer experience or changing banking behaviors? Connect with Unimrkt Research to discuss your research requirements and explore how our global research capabilities can support your next study. Contact us at +91-124-424-5210 or email sales@unimrkt.com. Alternatively, you can fill out our contact form, and our team will reach out to you shortly.

Frequently Asked Questions