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By Bradley Elliott, CEO of Anti-Money Laundering (AML) platform RelyComply
The Financial Sector Conduct Authority’s (FSCA) call for a centralised anti-fraud hub at its 2026 conference reflects a timely recognition that South Africa’s Financial Institutions (FIs) can’t successfully fight financial crime in isolation. However, centralisation alone is not sufficient. The real opportunity lies in turning shared intelligence into real-time, coordinated prevention across the entire ecosystem.
A siloed approach where each institution relies solely on its own intelligence, detection systems and response protocols will not keep pace with financial criminals who are becoming more agile, sophisticated and AI-driven with each passing day. In isolation, even well-resourced institutions are structurally outmatched by adversaries who collaborate, automate and iterate at machine speed.
“Agentic AI” systems can autonomously plan and execute full fraud campaigns, from reconnaissance through to monetisation. According to Mastercard research,payments company executives see synthetic identity fraud as the fastest-growing threat over the next year. The 2026 INTERPOL Global Financial Fraud Threat Assessment warns that AI-enhanced fraud is 4.5 times more profitable than traditional methods.
Deepfake technologies, synthetic identities and automated attack methods allow criminals to test and refine their approaches faster than ever before, overwhelming FIs’ defences. These systems can generate hundreds of thousands of synthetic identities, probe onboarding systems across multiple institutions simultaneously, and dynamically adapt based on which controls fail or succeed.
This represents a structural shift in the economics of fraud. Attackers benefit from network effects, while defenders remain largely linear and institution-bound. Fraud patterns are no longer isolated events; they are shared playbooks within criminal ecosystems. A successful synthetic identity technique identified in one market can be replicated across multiple institutions within hours. It also makes fraud effectively “free” to commit, dramatically lowering the barrier to entry and enabling far more actors to participate at scale.
From shared intelligence to real-time prevention
As such, we are seeing the rise of a system that rewards the speed of criminal learning over the resilience of institutions. Unless the legitimate players in the financial system can coordinate their defences, the financial criminals will outpace them. A call to share information is an important step in the right direction.
However, the real value of a centralised hub will lie in enabling real-time, automated intelligence exchange across institutions, regulators and adjacent sectors such as telecommunications and payments. If one bank detects a suspicious pattern, an automated alert shared with regulators and other institutions could stop the same attack elsewhere.
Much of the technical infrastructure to allow this is already in place. Banks, fintechs, mobile operators, and regulators hold complementary data that could dramatically improve fraud detection and prevention rates if pooled and analysed. For example, signals such as device fingerprint anomalies, SIM swap activity, and reused biometric patterns can be linked across institutions to identify coordinated fraud attempts before financial loss occurs.
The barriers to this form of data sharing are not technological, but cultural and regulatory. Many FIs still treat fraud intelligence as a competitive asset. Concerns around the Protection of Personal Information Act (POPIA) also create hesitation around data sharing. However, the key distinction is between sharing customer data and sharing fraud typology intelligence. The latter exposes criminal methodology, not personal information. Privacy-preserving technologies such as federated learning and secure multiparty computation now enable collaboration without exposing underlying customer data.
In light of this, it is encouraging to see industry associations and regulators embrace cross-sector fraud intelligence sharing as a regulatory norm. The FSCA has signed a memorandum of understanding with SABRIC and the Southern African Fraud Prevention Service to enable the sharing of real-time fraud data to ensure more coordinated action in the fight against financial crime.
It has also deepened collaboration with the Independent Communications Authority of South Africa to help combat the growing misuse of SIM swaps to facilitate fraud.
Regulatory clarity is now a critical enabler
Another major constraint is the lack of consistent regulatory frameworks for AI-driven risk management. There is currently no unified definition of AI governance or AI risk in South African financial regulation, and oversight practices differ significantly across institutions. Many firms still operate without formal AI governance frameworks or model explainability standards. This creates systemic blind spots in areas such as model auditability, decision transparency and accountability, all of which are increasingly exploited by AI-enabled fraud actors.
Explainability standards are particularly important because they enable regulators and institutions to understand not only what a model is flagging, but also why. Alongside this, auditability is equally critical, ensuring that model decisions and outputs can be traced, reviewed, and independently verified over time. The FSCA’s increasing focus on AI oversight, including sandbox experimentation and supervisory engagement, is an important move toward closing this gap.
Modern Regulatory Technology (RegTech) is also an essential piece of the puzzle, enabling regulators, FIs and fintechs to use AI as effectively as criminals do. AI-powered transaction monitoring, behavioural analytics and federated learning models provide a technical architecture for collaboration that preserves privacy while enabling collective intelligence. South Africa’s fintech sector is increasingly well-positioned to adopt these capabilities, with AI adoption already above the halfway mark, according to FSCA survey data. However, this needs to be both explainable and auditable. Financial institutions are often hesitant to fully adopt these systems because there is still uncertainty around whether regulators will accept AI-generated outputs, which in turn slows down meaningful progress and deployment at scale.
Towards a real-time prevention network
The removal of South Africa from the Financial Action Task Force grey list was a hard-won achievement that took sustained institutional effort. Maintaining that position requires moving from periodic compliance to continuous, real-time financial crime prevention. This means treating fraud intelligence as a shared utility rather than a proprietary advantage. It also requires embedding AI expertise within regulatory bodies, aligning governance frameworks across institutions, and establishing standardised definitions for AI risk, explainability and auditability.
The FSCA has started an important conversation. The next step is execution: building a real-time, privacy-preserving intelligence network capable of detecting and stopping fraud as it emerges, not after it spreads. In an era where financial crime is increasingly driven by autonomous AI systems, only equally intelligent and equally connected defences will be sufficient.
About RelyComply
RelyComply empowers banks, insurers, financial services providers, and innovative fintechs with a single, fully integrated KYC and AML platform. Designed for seamless implementation and rapid deployment, our intelligent technology enhances efficiency while detecting financial crime, enabling you to reduce risk and costs, ensure compliance, and drive strategic growth.
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