As Africa’s financial ecosystem undergoes a rapid digital transformation, the fight against financial crime is entering a new and more complex phase.
The rise of fintech, mobile money, and cross-border payments has expanded access to financial services—but it has also introduced new vulnerabilities that traditional compliance systems struggle to manage.
Financial institutions across the continent now face mounting pressure from regulators, global partners, and increasingly sophisticated criminal networks.
Against this backdrop, Artificial Intelligence is emerging not just as a technological upgrade, but as a strategic necessity.
In this exclusive Q&A, Shani Golov, VP Sales & Success at ThetaRay, shares insights into how AI is redefining anti-money laundering (AML), sanctions compliance, and anti-corruption efforts—and what African financial institutions must do to stay ahead.
The financial crime sector is evolving rapidly. What are the most significant global trends shaping AML and sanctions compliance today, and how are they impacting Africa?
Globally, there’s a clear shift from “box-ticking” to “effectiveness.” Regulators are no longer satisfied with institutions simply having systems in place—they want proof that those systems actually detect and prevent financial crime.
In Africa, this shift is especially pronounced due to FATF grey-listing pressures. Financial institutions are being forced to move away from manual, static processes toward automated, real-time monitoring.
This is not just about compliance. It’s about maintaining “trust equity” with global correspondent banks. Without that trust, access to critical dollar and euro payment corridors becomes increasingly difficult.
What are the key vulnerabilities facing African financial institutions, particularly in cross-border payments?
The biggest vulnerability is what we call the “data gap” in the correspondent banking chain.
When payments pass through multiple intermediaries, key data is often stripped or “cleaned” along the way. Legacy systems cannot reconstruct the full picture, which creates uncertainty.
That uncertainty leads to “de-risking,” where global banks withdraw services from certain regions because they cannot clearly assess the risk.
Our focus is on turning that “black hole” of missing data into a transparent, traceable map.
How can banks and fintechs balance strict compliance requirements with growth ambitions?
Compliance should not be the “Department of No.” It should be the “Department of How.”
The key is scalability. Institutions need systems that grow with them, without requiring exponential increases in staff. AI helps by automating the “noise”—particularly false positives—allowing human analysts to focus on complex, high-value risks.
When compliance is efficient, it becomes a competitive advantage. It enables faster onboarding, better customer experience, and expansion into new markets with confidence.
You will be speaking at the Sanctions, AML, Anti-Corruption and Ethics in Africa Conference in Nairobi. What message will you bring to delegates?
My main message is simple: don’t fear the machine.
There’s a perception that AI is a “black box” that regulators won’t accept. In reality, with Explainable AI, institutions gain more control and stronger audit evidence than they ever had with manual systems.
We need to shift the conversation from “AI versus human” to “human plus AI.”
What unique compliance challenges should be top of mind for African institutions?
Africa’s informal economy and leadership in mobile money present unique challenges.
Traditional AML frameworks were designed for conventional banking systems in cities like New York or London. They are not suited for high-volume, low-value digital transactions that define many African markets.
The challenge is applying sophisticated monitoring without disrupting user experience or excluding the unbanked.
Why are rule-based monitoring systems becoming obsolete?
Rule-based systems operate on “if/then” logic—for example, flagging transactions above a certain threshold.
But financial criminals adapt quickly. If the rule is $10,000, they send $9,999.
These systems are static, while financial crime is dynamic. They generate massive volumes of alerts—often 95–99% false positives—which overwhelms compliance teams and allows more sophisticated schemes to slip through unnoticed.
How does Cognitive AI differ from traditional machine learning models?
Most traditional models are “supervised”—they look for patterns based on past known crimes.
ThetaRay’s Cognitive AI is “unsupervised.” It learns the unique behavioral “DNA” of every customer or entity.
When something deviates from that normal behavior—even if it’s a completely new typology—the system flags it.
It’s the difference between reacting to known threats and proactively identifying unknown risks.
How does AI uncover new or evolving money laundering schemes?
AI focuses on relationships and anomalies rather than simple thresholds.
For example, it can identify networks of accounts that individually appear normal but exhibit unusual timing, geographic patterns, or transactional links when viewed collectively.
This allows institutions to detect “unknown unknowns”—patterns that would not be captured by predefined rules.
False positives remain a major challenge. How can institutions reduce them?
The key is context.
A transaction that is normal for one customer may be suspicious for another. AI incorporates this “human layer”—who the customer is, their behavior, and their network.
With this context, institutions can reduce false positives by 50–80% while improving the detection of genuine risks.
What is the value of real-time monitoring in compliance operations?
Real-time monitoring transforms compliance from a reactive to a proactive function.
It allows institutions to verify transactions instantly, ensuring a seamless customer experience. At the same time, it provides leadership with a live view of risk exposure.
Instead of looking in the rearview mirror, institutions are looking through the windshield.
How does Explainable AI build regulatory trust?
Regulators need clear, auditable explanations—not just outcomes.
Explainable AI provides human-readable reasoning for every alert, showing exactly what triggered the suspicion, whether it’s geography, transaction velocity, or network behavior.
This transparency strengthens trust and helps institutions meet stringent audit requirements.
How can fintechs scale while maintaining strong compliance frameworks?
Fintechs thrive on speed and agility, so their compliance systems must match that pace.
The solution is to embed AI-driven monitoring directly into the product flow. When compliance is automated and integrated, fintechs can expand into new markets without dramatically increasing operational costs.
Can you share real-world examples of AI-driven compliance success?
The impact is best seen in operational results.
For example, one global payments provider achieved an 86% reduction in false positives and a 70% increase in productive alerts, dramatically reducing investigation times.
Another institution reduced Enhanced Due Diligence workloads by 60% and cut manual analysis time by 40%.
These improvements go beyond compliance—they enable faster onboarding, improved customer retention, and confident expansion into new markets.
What trends should African financial institutions prepare for in the next 3–5 years?
Two major trends stand out.
First, “AI versus AI.” Criminal networks are beginning to use AI for deepfakes and automated laundering schemes. Institutions cannot combat these threats with outdated tools.
Second, regional integration initiatives—such as cross-border payment systems—will increase transaction volumes and complexity. Compliance systems must become equally seamless and interconnected.
How would you describe your leadership style?
My approach is grounded in transparency and real-world impact.
I focus on delivering tangible value and bridging the gap between strategy and execution. My goal is to empower teams to see AI not just as a technology, but as a business enabler.
Ultimately, we are driven by a shared mission: to create a world where money moves freely and safely, while protecting the integrity of the global financial system.
The Road Ahead
As Africa continues to lead in financial innovation, particularly in mobile and digital payments, the continent’s compliance landscape must evolve just as rapidly.
The message from industry leaders like Shani Golov is clear: compliance is no longer just a regulatory requirement—it is a strategic pillar for growth, trust, and global integration.
Institutions that embrace AI-driven, real-time, and explainable compliance frameworks will not only mitigate risk but unlock new opportunities in an increasingly interconnected financial world.
