How AI and Cybersecurity Are Transforming Community Banks

Community banks have long been considered trusted pillars in local economies, offering personalized financial services and fostering strong customer relationships. However, in 2026, these institutions are navigating an evolving landscape shaped by:

• Technological innovation
• Increasing cyber risks
• Shifting regulatory demands

To remain competitive and resilient, improving community bank risk management strategies is necessary to address emerging challenges. According to the 2025 Banking Priorities Survey, key focus areas include:

• Artificial Intelligence (AI)
• Automation
• Modernizing core banking systems

These priorities reflect a growing recognition that legacy systems and traditional processes are no longer sufficient. Community banks are reevaluating their operations, leveraging AI to streamline compliance, automate repetitive tasks, and enhance customer experiences.

At the same time, the escalating risk of cyberattacks and increasing regulatory expectations are prompting community banks to develop more sophisticated risk management frameworks. As these pressures mount, banks must invest in AI-driven tools and enhanced cybersecurity measures to protect their customers and ensure regulatory alignment.

Read on to understand more about the major trends and technologies shaping community banking in 2026, as well as how smarter, AI-based solutions are streamlining risk management in community banks.

Key Strategic Priorities for Community Banks in 2026

Cybersecurity and Data Privacy in Banking

According to the 2025 Banking Priorities Survey, 28% of financial professionals identified cybersecurity and data privacy as their top concerns. As digital transactions become more prevalent, the potential for data breaches and cyberattacks increases, putting sensitive customer information and institutional reputations at risk.

The financial impact of cyber incidents also continues to rise across the board, with a reported cost of $5.56 billion per data breach or other cybersecurity incident for those organizations operating in the financial sector.

Community banks ramp up their risk management defenses with AI-powered risk assessments, real-time fraud monitoring, and advanced cybersecurity tools. These technologies enable proactive detection and mitigation of risks, helping institutions stay ahead of cybercriminals while ensuring compliance with increasingly stringent data privacy regulations.

The Shift to Core Modernization and Open Banking

Legacy systems are becoming obsolete as community banks prioritize modernizing their core banking systems. In 2025, 62% of small business banks were investing in core and ancillary products designed to support ongoing innovation and enhance operational efficiency (2025 Banking Priorities Survey). By moving away from outdated legacy applications, these banks are positioning themselves for improved scalability.

A key component of modernization is the embrace of open banking. 57% of community banks are integrating open APIs to create a more connected financial ecosystem, enabling seamless collaboration with fintech providers and third-party services. Additionally, 53% of banks plan to evaluate technology based on its ability to support open banking initiatives (2025 Banking Priorities Survey).

The momentum behind open banking is accelerating, with total open banking transactions projected to reach $330 billion by 2027, a significant increase from $57 billion in 2023. As transactions increase, community banks’ risk management frameworks must be enhanced to ensure customer data is protected, and partnerships remain secure.

Driving Operational Efficiency with Automation

Operational efficiency was a top strategic priority for 44% of community banks in 2025, signaling that this trend will continue into this year (2025 Banking Priorities Survey). Banks are under increasing pressure to:

• Optimize their operations
• Enhance customer experience
• Reduce costs

A significant driver of efficiency is the deployment of automation. 40% of community banks have identified deposit growth as one of their top three priorities. AI-powered insights can help them capture new accounts and tailor offerings to customers’ needs. Despite this focus on deposits, only 22% of community banks plan to prioritize loan growth, reflecting a shift in strategy (Source: The Financial Brand).

AI Integration in Banking

AI has firmly established itself as the driving force behind the transformation in community banking. As 33% of bankers identify AI as the most impactful technology trend of 2025, its role in fraud detection, AML compliance, and customer service has expanded rapidly.

With 91% of banks relying on AI to bolster fraud and AML efforts and 90% integrating AI for enhanced customer service, it’s clear that AI is no longer a competitive advantage; it’s a necessity. Beyond banking, AI is expected to deliver a 21% net increase to the U.S. GDP by 2030, underscoring its broad economic potential (2025 Banking Priorities Survey).

While AI offers powerful capabilities, implementing and managing this technology can be daunting for community banks’ risk management policies, especially those with limited resources and smaller teams. To stay ahead, scalable software for community banks can help them overcome unique challenges and meet budgetary constraints.

Enhancing AI Integration and Banking Modernization with Predict360

Community banks’ risk management frameworks must navigate rising cybersecurity risks, increasing regulatory demands, and the need for modernization. Having the right tools in place is critical. Predict360 Essentials is purpose-built for U.S. community banks, offering a scalable, AI-powered solution that simplifies risk and issue management without requiring additional headcount.

Predict360 Essentials empowers community banks to strengthen their risk management programs. It offers preconfigured risk assessments tailored to banking needs. The platform streamlines identification, assessment, and mitigation within your organization’s risk management framework.