By Abhijit Gairola, Head of Engineering and Awadhesh Ranjan, Head of Compliance at Skydo
Cross-border payments have always been a double-edged sword, a powerful enabler of global trade and economic growth, but also a prime target for fraud, money laundering, and compliance risks. As more and more businesses expand beyond borders, the traditional security frameworks are struggling to keep up, both with the rate and scale of expansion. Manual reviews, outdated fraud detection methods, and rigid compliance processes are no longer enough to handle the sheer scale and complexity of global transactions.
This is where artificial intelligence (AI) steps in, not to replace human oversight, but to enhance and strengthen it. Yet, even as AI strengthens transaction security, the challenge remains: Can technology truly stay ahead of increasingly sophisticated fraudsters, or is this an endless game of cat and mouse?
The problem with “post-fact” fraud detection
For years, fraud detection in financial transactions has been largely reactive. Even with prediction models in place, the usual flow has mostly been the same: A suspicious transaction occurs, someone notices it after the fact, and then the long process of tracing, verifying, and blocking funds begins. By then, the damage is often done.
This traditional “post-fact” model relies heavily on human intervention, and its limitations are clear:
- Too slow: By the time a transaction is flagged, bad actors have often moved the money through multiple layers, making it nearly impossible to recover.
- Too rigid: Legacy compliance frameworks depend on static and heuristic rules; fixed thresholds for transaction sizes, known fraud patterns, or pre-defined blacklists. But fraud isn’t static. Fraudsters evolve faster than rules can be updated.
- Too costly: The sheer manpower needed to manually verify transactions at scale is not sustainable, neither in time nor cost.
AI changes the game by shifting fraud detection from reactive to proactive, from tracing transactions after the fact to identifying risks before they materialise.
AI as the “predictive firewall” of global payments
Rather than working with pre-set and heuristic rules, AI learns from transaction patterns in real time. It doesn’t just flag transactions that exceed a certain limit—it contextualises behaviour. Here’s how:
Take, for example, an IT consultancy in India that typically receives $10,000 per month in client payments. Out of nowhere, they suddenly receive $200,000 from an unrelated entity. Now, traditional methods would ordinarily compare this against the set transaction cap and flag it. However, AI goes several steps beyond and examines:
- Historical payment trends (Has this company ever received large payments before?)
- Industry norms (Is this transaction amount common for an IT consultancy?)
- Counterparty verification (Does the sender have a known payment history in similar industries?)
If the transaction is genuinely out of place, AI doesn’t immediately block it but escalates it for real-time review. This ability to detect anomalies with context is what makes AI so much more effective than rigid compliance rules.
Yet, AI is not infallible. The same intelligence that detects fraud can also create unnecessary roadblocks if not calibrated correctly. The key is balance: AI should assist, not obstruct.
The challenge of evolving fraud tactics
The thing about fraudsters is, they don’t stand still, they adapt. . The moment financial institutions close one loophole, bad actors find another. This is particularly true in identity fraud, where criminals use increasingly sophisticated methods to bypass traditional verification checks.
AI plays a critical role in solving this. Rather than relying on basic ID verification, AI-powered compliance systems can now:
- Cross-check live KYC data with government records
- Analyse biometric markers to prevent deepfake attempts
- Use facial recognition to match users across multiple identity databases
But this raises another question: Does AI make compliance foolproof? Not quite. Technology alone isn’t the answer. AI helps speed up and refine security processes, but it still requires human judgment to make final calls. The reality is, AI doesn’t eliminate risk; it minimises it.
Where AI stumbles: The issue of false positives
One of the biggest pain points in compliance today is false positives, transactions wrongly flagged as suspicious. Imagine a business that expands into a new market and suddenly sees a surge in inbound transactions. Without AI, this might result in an account freeze.
But even AI-powered systems aren’t perfect. A name match in a sanctions list, for instance, doesn’t necessarily mean the customer is a fraudster. If John Doe from Mumbai is mistakenly flagged as Jon Doe from New York, who was implicated in a financial crime, a manual review is still necessary.
AI helps solve this by:
- Cross-referencing multiple identity markers (DOB, address, nationality)
- Assigning risk scores instead of outright blocking transactions
- Filtering out false positives through contextual analysis
This way, AI allows compliance teams to focus on actual risks rather than wasting time on false alarms.
The future: AI as a compliance copilot, not a gatekeeper
AI isn’t here to replace compliance teams, it’s here to empower them. Instead of manually reviewing thousands of transactions, compliance officers can focus on high-risk cases while AI handles routine screening.
What does the future look like?
- Faster, real-time transaction approvals – AI will further reduce manual interventions, making cross-border payments almost instantaneous.
- Adaptive fraud prevention – AI models will continuously learn and evolve to stay ahead of fraud patterns.
- Seamless compliance automation – Document verification, sanction list screenings, and identity checks will become even more streamlined.
But most importantly, AI will remain a tool, not a decision-maker. The human element in compliance will always be essential, and businesses that strike the right balance between AI-powered efficiency and human oversight will set the standard for secure international transactions.
As we move forward, one thing is clear: The real challenge isn’t whether AI can detect fraud—it’s whether businesses can leverage it effectively without creating unnecessary friction for legitimate users. Those who master this balance will lead the next era of safe, seamless global transactions.