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Key Drivers for AI Adoption

There is no doubt that artificial intelligence (AI) is shaping the future of the financial services industry, both globally and here in South Africa. For banks and merchants, the question is not whether they should implement these technologies, but how they can strategically leverage them to gain a competitive edge.

The primary drivers for AI adoption are enhancing efficiency, mitigating fraud, and providing more tailored services to a competitive market.

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More Than Technology

Balancing AI with Holistic Strategies for Fraud Prevention

AI and machine learning algorithms are being used to analyse vast datasets in real-time. This allows for the identification of anomalous transaction patterns that would be imperceptible to humans, significantly reducing financial losses for both the banks and their customers.

However, using AI is not a silver bullet. Effective fraud prevention requires a multi-pronged strategy: combining customer education, strong compliance frameworks, and collaboration with regulators with the practical use of new technology. 

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AI Dominates Financial Fraud Detection

There are numerous conversations about AI’s role and immediate applications in fraud detection and risk management.

As of December 2024, 71% of financial institutions use some form of AI or machine learning for fraud detection.

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AI's Role in Streamlining Compliance and Regulatory Reporting

One of the key roles AI can play in this multi-layered approach is in enhancing compliance and reporting.

By automating transaction monitoring, AI can ensure faster and more accurate reporting to regulatory bodies.

By streamlining processes like compliance checks and regulatory reporting, it is possible to reduce costs and improve accuracy.

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Unlocking Customer Value Through Data and AI

Another significant area of AI application lies in customer engagement and personalisation.

Payments software generates rich streams of behavioural and transactional data, and AI can unlock new ways to deliver value from this information.

The Intersection of Payments Data and AI

Unlike many enterprise datasets, payments data is highly structured, trusted, and complete. With the adoption of ISO 20022, it’s become even richer and is providing contextual and high-fidelity insight into every transaction.

This positions payments infrastructure to become a core enabler of AI innovation — providing real-time, high-quality data streams into model training and inference pipelines.

But banks will need modern systems that support real-time, clean, structured data flows for continuous inference; historical completeness and auditability for model training; and embedded controls to maintain data integrity and lineage at scale. 

Practical Applications

Driving Tangible Outcomes with Artificial Intelligence

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AI-powered assistants elevate the customer experience by resolving queries instantly and providing hyper-personalised offers, which boosts customer loyalty.

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AI is advancing financial inclusion in South Africa by enabling more inclusive and accurate credit scoring. By analysing a wider range of data beyond traditional credit history, lenders can expand access to credit for underserved segments of the population.

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AI is set to transform the payments software lifecycle, using GenAI to automate tasks like code generation and testing for faster, more robust deployments.

The AI Advantage

The Competitive Edge in a New Payments Era

As the industry adapts to regulatory shifts such as ISO 20022 and the growing demand for instant payments, financial services businesses that leverage AI to streamline integration and compliance will have a competitive edge. 

The intersection of AI, payments and drivers such as mobile-first adoption, regulatory priorities, and financial inclusion suggests a future where payments software becomes not only more intelligent but also more adaptive to the unique needs of South African businesses and consumers.

Use Cases

AI Technology Use Cases and Implementations

 

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Agentic AI & automated workflows

  • Agentic AI systems perform specialised finance tasks precisely and autonomously, such as managing chargeback claims or fraud detection, and can link together into workflow chains with higher-level orchestration.

  • Recent academic advances like CASE (Conversational Agent for Scam Elucidation) deploy conversational agents to gather scam intelligence, use LLMs (e.g., Google’s Gemini) to parse feedback, and strengthen enforcement mechanisms. Implementations on Google Pay India have seen a 21% rise in scam interventions.

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Blockchain, CBDC, and secure infrastructure

  • Hybrid systems like SecurePay combine permissioned blockchains with central bank digital currencies (CBDCs) to enhance security, enable auditability, and improve processing speed, achieving ~256 transactions per second with low latency.

  • AI-driven compliance: Leveraging digital currencies and blockchain is a key focus, especially for cross-border and platform-economy payments.

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Real-time fraud detection and risk control

  • AI models are increasingly being used to identify anomalies at transaction time, reducing both fraud and false declines.

  • Firms like Nasdaq, Verafin, and BioCatch are pairing behavioural biometrics (up to 3,000 signals per event) with transactional data for real-time fraud analysis.

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Smarter payment routing and optimisation

  • AI is being deployed to smartly choose the best transaction routes (e.g., gateways or processors) based on historical success, latency, and dynamic performance: boosting success rates by 4 – 6%.

  • Digital wallets like Visa’s network engine leverage AI to optimise transaction routing for speed and cost efficiency.

Research Report

Register for Electrum’s upcoming Payments Report

Learn more about the drivers of modernisation of SA’s payments ecosystem.