A recent American Banker article, “Knock on wood: Are banks doing enough to cope with Mythos?” raises a timely and uncomfortable question about advanced AI models like Anthropic’s Claude Mythos. 

As highlighted in the article, INETCO CEO Bijan Sanii points out a critical truth:

The truth of the matter is, there’s hundreds of pretty significant financial institutions that are not well protected, that don’t have an ability to inoculate themselves against this.

The conversation is being fueled by the emergence of AI technology capable of identifying software vulnerabilities at a speed and scale that was previously unimaginable. What once took skilled attackers months or years can now potentially be achieved in hours.

The shift to “machine-speed” risk

At the heart of the issue is a growing imbalance: AI is accelerating the discovery of vulnerabilities faster than organizations can remediate them.

This creates what experts are calling a remediation gap — a widening window between when a weakness is identified and when it can be fixed. For banks operating complex interconnected systems, that gap represents real exposure.

Legacy infrastructure and third-party dependencies only compound the problem. Many financial institutions still rely on older technologies and vendor ecosystems that weren’t designed for this level of scrutiny or speed.

Public confidence vs. private concern

While some bank leaders are publicly downplaying the risks, the reality behind the scenes appears more nuanced. Security teams are actively testing these new AI capabilities, reassessing their defenses and grappling with how to respond to threats that operate at machine speed.

This isn’t just a theoretical risk. It’s a rapidly evolving operational challenge.

Why this matters now

The rise of AI-powered vulnerability discovery changes the rules of the game:

• Attack surfaces expand faster as systems are analyzed more deeply
• Time-to-exploit shrinks dramatically
• Detection systems that rely on delayed data are too slow to catch threats in real time

In this environment, prevention alone is no longer enough. Financial institutions need the ability to see, understand and respond to threats as they happen, not after the fact.

The role of real-time transaction intelligence

This is where real-time visibility and the ability to harness field-level transaction intelligence in milliseconds plays a critical role, and where Know Your Transaction (KYT) becomes essential. KYT enables continuous real-time analysis of transaction behavior, allowing financial institutions to detect anomalies and payment fraud as it unfolds. 

Explore how KYT strengthens real-time payment fraud detection and prevention in our latest blog.

Gaining access to real-time transaction intelligence will help organizations enhance their ability to:

• Identify subtle behavioral patterns indicative of emerging threats
• Detect and prevent payment fraud and system misuse in real time
• Strengthen cyber-resiliency across legacy and modern environments

In a world where AI can expose vulnerabilities instantly, real-time intelligence becomes the foundation of defense.

Closing the remediation gap

The real question isn’t whether AI will reshape the threat landscape, because it already has. What must be asked now is whether financial institutions can adapt quickly enough to keep pace.

Closing the remediation gap requires more than faster patching cycles. It demands in-flight detection and continuous prevention and visibility across every transaction flowing through the system.

That’s the difference between reacting to threats and staying ahead of them.

Read the full article in American Banker. Source: American Banker, “Knock on wood: Are banks doing enough to cope with Mythos?”, April 30, 2026 by Penny Crosman

FAQ

What is real-time transaction intelligence?

Real-time transaction intelligence is the ability to decode, correlate and analyze every element of a transaction as it happens across systems, channels and networks in milliseconds.

It goes far beyond simple transaction monitoring. It involves:

• Decoding: Inspecting full transaction payloads, not just summaries or logs
• Correlation: Linking activity across channels, sessions, devices and systems
• Contextual analysis: Understanding behavior, timing and anomalies in real time

This level of intelligence enables organizations to move from after-the-fact detection to prevention that starts while transactions are in flight. 

Why does this matter?

AI-powered threats operate at machine speed, so defenses must as well.

Without real-time transaction intelligence, attacks can complete before alerts are generated, fraud signals remain hidden in fragmented systems and security tools lack the context needed to act precisely.

With it, suspicious behavior can be identified mid-transaction, threats can be stopped before authorization or completion and false positives can be reduced through deeper context.

What types of attacks can real-time transaction intelligence help prevent?

Real-time transaction intelligence is particularly effective against:

• Man-in-the-middle (MITM) attacks altering transaction data in-flight
• Malware-driven manipulation of payment requests or responses
• Authorized push payment (APP) fraud using social engineering
• Credential compromise and account takeover with subtle behavioral shifts
• Zero-day exploits that bypass traditional signature-based defenses

How can financial institutions fight AI-powered threats with AI?

As AI accelerates the speed and sophistication of attacks, traditional defenses are no longer sufficient. Static rules, delayed detection and perimeter-based controls cannot keep pace with threats that learn and adapt in real time.

Modern financial institutions must now fight AI with AI. This means combining real-time transaction intelligence for deep visibility and context, self-learning AI models that continuously adapt to behavior, transaction firewall capabilities that monitor transactions in flight, and AI-driven investigation agents that accelerate response and decisioning.

Together, these capabilities form a closed-loop defense system that can detect, decide and act within the lifecycle of a transaction, not after it.

How does transaction intelligence power modern payment fraud prevention?

Defending against AI-driven threats requires more than visibility. It requires a modernized fraud prevention stack that combines transaction intelligence with AI, automation and precision controls, including:

Complete transaction visibility

Provides a full 360-degree view of every message field — application payload, metadata, network data, request/response timing — across all payment channels with no blind spots.

Real-time, end-to-end integrity 

Monitors transactions in-flight from initiation to authorization and back. This enables detection of MITM attacks, malware-based manipulation and zero-day threats before completion.

Self-training individualized AI models

Creates a unique behavioral model for every user, card, device and terminal that self-updates after every transaction.

Transaction field-level blocking

Delivers precision-blocking of specific fraudulent transactions without disrupting legitimate ones, outsmarting WAFs that can only block at IP or port level.

AI-powered investigation agent

Accelerates fraud investigation with AI-driven agents specifically built for payment fraud. These systems automatically triage alerts, surface the most relevant cases for immediate analyst attention, reduce the operational burden of manual false positive reviews and provide explainable AI decisions for faster action.

The result is up to 40% faster investigation and response times, enabling analysts to focus on high-priority threats.