By: Yusufzai Khan, Associate Vice President—Product Engineering, BlueVerse, LTIMindtree

Digital banking is booming across the world, but fraudsters are evolving even faster. As their methods become more sophisticated, financial losses have soared into the hundreds of billions. Despite the evolution of fraud detection in the banking and financial services sector, high-profile failures continue to expose the limitations of current systems.  One of the most striking examples in recent times was a money laundering scandal involving a major banking institution, which led to the resignation of its CEO and triggered widespread regulatory and public backlash. This case underscores the urgent need for a paradigm shift in fraud detection, as legacy defenses are no longer adequate to counter fraud.

Layered Defenses Against Fraud

Over the years, financial institutions have built layered defenses that include:

• Rule-Based Systems: These systems flag transactions based on thresholds (e.g., amounts over $100).
• Workflows and Manual Reviews: Compliance teams investigate flagged transactions.
• Machine Learning Models: These detect anomalies based on historical patterns by learning from datasets.
• Transaction Monitoring Systems: Designed to track suspicious behavior across accounts and geographies.

While these systems are effective against known fraud patterns, they struggle with low-value, high-volume transactions, cross-border complexities, and emerging fraud tactics.

A Billion Dollar Fraud Story

A leading bank was accused by a financial crime regulator for breach of anti-money laundering laws. The bank failed to report 19.5 million international fund transfers totaling over $7 billion US dollars, many of which were linked to child exploitation payments in Southeast Asia.¹

How did the fraudsters get away with it? Here’s how the fraud bypassed controls:

• Transaction Structuring: Fraudsters used thousands of small transactions, each below the reporting threshold, to avoid detection.
• System Fragmentation: The bank’s anti-money laundering systems were siloed, lacking a unified view of customer behavior.
• Delayed Response: Despite internal awareness, the bank failed to act swiftly, and the CEO initially downplayed the issue.
• Product Abuse: A payment product was exploited to send funds to criminal networks without triggering alerts.

The scandal led to the resignation of the CEO and exposed the inadequacy of traditional controls in detecting sophisticated, distributed fraud schemes. This fraud was not an isolated incident. In the last few years, several ingenious fraud strategies have led to hundreds of billions of dollars being lost. Clearly, something has to change when it comes to fraud detection and prevention in banking.

Why Traditional Controls Fall Short

• Reactive Models: Machine learning models, while powerful, are often trained on historical data and struggle with zero-day fraud patterns, particularly when new, unseen tactics emerge suddenly.
• Static Rules: Easily circumvented by structuring transactions below thresholds.
• Lack of Contextual Awareness: Systems fail to understand relationships between entities (e.g., customer, vendor, and transaction purpose).
• Siloed Data: Fragmented systems prevent holistic analysis across departments and geographies.

The Future: Ontology, Knowledge Graphs, and Agentic AI

To combat modern fraud, banks and financial institutions must adopt intelligent, interconnected, and autonomous systems that leverage AI for fraud detection. This entails:

Ontology: It defines the semantic structure of financial entities such as customers, accounts, transactions, vendors, as well as their relationships. It enables systems to understand meaning, not just data.

Knowledge Graphs: These map real-time relationships between entities. For example, a graph can show how a customer is linked to multiple accounts, vendors, and geographies. It helps detect unusual patterns, such as a customer suddenly transacting with high-risk countries.

Agentic AI: Autonomous agents that monitor, reason, and act. They can:

• Continuously scan for emerging fraud patterns.
• Ask questions like “Why is this customer sending 200 small payments to the same offshore account?”
• Trigger investigations or block transactions proactively.
• Gartner predicts that by 2026, 40% of enterprise applications will feature task-specific AI agents, up from less than 5% in 2025. These agents will drive autonomous decision-making and workflow orchestration in fraud detection.²

A Future-Ready Fraud Detection System

The evolution of fraud detection in banking necessitates real-time fraud detection to counter ingenious fraudsters with novel methods. Imagine a fraudster begins sending hundreds of $99 transactions to an offshore account—just below the $100 reporting threshold.

In a future-ready system multiple components would detect such a case of fraud. Here’s how it would function:

1. Ontology Layer: Recognize the semantic meaning of structured transactions and their purpose.
2. Knowledge Graph: Detect that the sender has no prior history of international transfers and is now linked to a high-risk recipient.
3.  Agentic AI: Launch an autonomous investigation, correlate with other similar patterns across the bank, and identify a coordinated laundering scheme.
4. Action: Freeze the account, alert the compliance team, and notify regulators—all in real time.

This system doesn’t just detect fraud. It prevents it before damage occurs.

Conclusion: A New Era of Proactive Fraud Detection

The rise in financial fraud and money laundering cases is a wake-up call for the banking industry. It shows that legacy systems are no match for modern fraud tactics. Therefore, it is imperative for banks and financial institutions to enter a new era of fraud detection, shifting from reactive to proactive methods. The future of fraud detection in banking lies in semantic intelligence, relationship mapping, and autonomous decision-making capabilities that enable real-time fraud detection. By integrating ontology, knowledge graphs, and agentic AI, financial institutions can build resilient, proactive fraud detection systems that adapt to the ever-changing tactics of fraudsters. Harnessing AI and innovative technology won’t just detect fraud—it will empower you to stay ahead of it.

References

¹AUSTRAC and Westpac agree to proposed $1.3bn penalty, AUSTRAC, 24 September 2020, https://www.austrac.gov.au/news-and-media/media-release/austrac-and-westpac-agree-penalty. [austrac.gov.au]

² Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025 – STAMFORD, Conn., September 05, 2025 – Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025