With AI, Financial Institutions Can Look Backward To Move Forward

Protect Customers by Learning How They Operate

AI and machine learning allow financial institutions to detect the subtle anomalies hidden within enormous volumes of legitimate activity.

Financial organizations manage extremely complex attack surfaces. Adversaries know where the vulnerabilities are:

• Customer endpoints without strong controls
• Employees using cloud platforms and collaboration tools
• Third-party vendors with varying degrees of cyber maturity
• High-risk workflows such as wire transfers, loan approvals and claims processing

And, of course, users remain a primary vector. Whether it’s a credit union teller opening emailed documents from members, an investment manager reviewing prospectuses or an insurance adjuster receiving claims imagery, financial employees must engage constantly with digital content to do their jobs.

Adversaries exploit this reality. Phishing sites, fraudulent ads, spoofed invoices, look-alike domains, deepfake voice calls — these tactics succeed because users must interact with digital content to keep business moving.

This is where AI becomes a powerful enabler.

READ MORE: Develop a cyber resilience strategy that allows your organization to bounce back quickly.

Use AI To Produce Ongoing Cyber Awareness for Financial Employees

Security leaders in financial institutions know that the human layer is as critical as the technical one. Fraud, social engineering and credential theft all thrive when users lack timely, contextual security knowledge.

However, cybersecurity teams cannot manually produce customized alerts, advisories and awareness materials for thousands of employees across dozens of business units. AI can help bridge that gap.

AI can:

• Translate complex threat intelligence into plain language
• Tailor messaging for specific job functions (e.g., mortgage officers vs. investment advisers)
• Generate awareness content quickly when new fraud patterns emerge
• Help security teams scale their influence without adding head count

If the only time a banking employee sees cybersecurity messaging is during an annual compliance training, the institution is vulnerable. AI-assisted awareness programs can shift this dynamic by providing frequent, relevant, proactive information about what adversaries are doing now.

This is essential in a sector where fraud tactics evolve weekly and regulatory expectations around customer protection are increasing.

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Analyze Patterns With AI to Thwart Malicious Financial Activity

AI enables institutions to look backward at patterns, behaviors and historical fraud indicators so their teams can better move forward.

In financial services environments, AI-driven analysis helps detect:

• Deviations from normal transaction patterns
• Account behaviors inconsistent with customer history
• Unusual administrative activity by privileged users
• Malware-driven automated transactions
• Insider-threat indicators within trading, underwriting or claims workflows

Financial networks are exceptionally large and diverse. They might operate:

• Tens of thousands of endpoints
• Multiple payment rails
• Cloud-based core systems
• External fintech integrations
• A network of branches or adviser offices

With this complexity, humans alone cannot sift through every log or alert. But machines can learn what “normal” looks like and elevate only what is truly abnormal.

Instead of analyzing 15,000 compliant devices, AI highlights the one behaving differently. Instead of chasing thousands of false positives, analysts see only what requires human judgment. Instead of discovering fraud after losses occur, institutions can detect early indicators and intervene sooner.

In short: AI frees up analysts to focus on the threats that matter most.