AI enables sophisticated financial fraud at scale, but financial services organizations can fight back by using AI-powered tools to detect and prevent these threats.
Fraudsters and cybercriminals no longer rely on rudimentary phishing emails or simple social engineering tactics to attack financial services organizations. The game has changed. Today, they’re weaponizing artificial intelligence (AI) to launch sophisticated, highly targeted schemes and attacks at scale. Traditional rule-based fraud detection systems are increasingly proving inadequate against adversaries who can train models to evade them with machine precision.
Here’s the challenge: AI can generate realistic phishing campaigns, craft synthetic identities, and probe defenses with incredible speed. These attacks can mimic legitimate customer behavior patterns, with cybercriminals training their own models against open banking application programming interfaces (APIs) to learn how to circumvent fraud detection controls. As a result, fraud attempts appear very believable to human targets and blend seamlessly into normal network traffic.
Finding the sweet spot: Preventing fraud without harming the customer experience
Combating such attacks requires a tricky balancing act by financial institutions. Every missed fraudulent transaction cuts directly into profits, while every false positive drives away customers and inflates operational costs through manual review processes. In banking, this margin game is particularly painful. Both sides of the equation create significant pressure on security teams and bottom-line results.
What’s more, security and fraud prevention personnel are overwhelmed by an ever-growing tide of alerts, most of which are not real threats. The traditional response is to tighten detection parameters to reduce false positives, but anyone who has run a security operations center knows the danger of making this tradeoff. Tighten the aperture too much, and you widen the detection gap. That gap is exactly where AI-enabled adversaries thrive, slipping through unseen while defenders filter out a smaller slice of alerts.
Thankfully, AI can power impressive defenses just as effectively as it can detect fraud and cyberattacks. But coming out on top in this AI arms race requires financial institutions to apply AI across the entire fraud detection workflow.
For starters, AI should be deployed to spot the subtle inconsistencies that synthetic identities and automated attacks leave behind. Just as attackers are using AI to blend in, banks can use AI to identify the telltale digital fingerprints of fraud that human observers would likely miss. Machine learning models continuously adapt to close the very gaps adversaries attempt to exploit, matching the sophistication of AI-powered attacks. This approach moves beyond traditional pattern recognition to identify behavioral anomalies that would be invisible to rule-based systems.
AI can also correlate signals and context to elevate the attacks that matter most, significantly reducing false positives. Rather than simply flagging anomalies, AI highlights those most likely to pose a serious threat by correlating signals and context. This approach transforms the endless flood of alerts into a curated stream of threat intelligence, empowering analysts to focus on investigating and addressing serious threats that require human expertise. Analysts can then work from a curated set of threats instead of drowning in an endless flood of notifications.
AI excels at enriching alerts, pulling contextual information, and handling routine investigation steps. However, the final judgment and response should remain with experienced professionals who can apply business knowledge and ethical considerations that machines cannot replicate.
“AI does not replace human judgment,” said Mike Nichols, vice president, product management at Elastic. “Instead, AI frees analysts from drowning in noise so they can focus on investigations that matter. Instead of AI reducing their importance, it expands the role of Tier-1 analysts. With AI taking on the heavy lifting of correlation and enrichment, those analysts have the time and tools to develop deeper expertise and contribute at a higher level.”
A practical example: Elastic’s approach
Elastic demonstrates how this vision can be effectively implemented. Its Search AI Platform combines traditional search with vector search capabilities, powering retrieval-augmented generation that grounds AI responses in organizational data rather than generic outputs.
Elastic developed the platform with the approach that AI should be open, transparent, and embedded directly into workflows rather than bolted on as a sidecar that requires constant retraining or another layer of complexity. Security teams don’t need another tool to juggle. Instead, they need AI woven into the way they already work.
Central to Elastic’s solution is Attack Discovery, a feature within the core security solution and the company’s new AI SOC Engine. Instead of burying analysts under raw alerts, it understands the context of those alerts, correlates them using learned patterns of human correlation, and ensures that legitimate threats rise to the top of the priority list. The system highlights campaigns using tactics and techniques like those defined in MITRE ATT&CK, a globally accessible knowledge base of adversary tactics and techniques, to ensure that real attacks surface above the noise.
As a result, security teams start each day focused on investigating and addressing the most critical incidents instead of sifting through thousands of false positives. This approach transforms how security operations function, moving teams from reactive alert processing to proactive threat hunting.
Elastic’s approach also embodies agentic workflows where AI doesn’t just generate answers but also takes meaningful action within the platform. For example, with only a handful of sample logs, Elastic’s Automatic Import can generate integration packages directly in the custom integrations page, reducing weeks of manual work down to minutes. The Elastic model-agnostic framework gives organizations control over whether to use hyperscaler models or run their own on-premises solutions, maintaining flexibility and security requirements.
The human-AI partnership
The secret weapon against AI-powered fraud lies in pairing AI’s speed and scale with analysts’ judgment and domain expertise. This combination creates a security model that’s both more effective against sophisticated threats and more sustainable for the professionals defending against them.
The future is not AI replacing humans, but rather AI empowering them. Supported by AI, defenders can move from endless alert review to higher-value investigation and defense, making the role more engaging and more effective. When banks embrace AI by broadening detection, prioritizing attacks, and automating repetitive tasks, they’re not just keeping pace with adversaries — they’re reclaiming the advantage.
The real competitive edge will come from organizations that successfully pair the speed and scale of AI with the judgment and domain knowledge of experienced professionals, creating a security model that can adapt and evolve alongside increasingly sophisticated threats.
To learn more, visit us here.
