AI Transforms Banking Security Across South Africa, China & Russia

Banks in South Africa, China, and Russia are using similar AI technologies to combat fraud and improve financial security, according to Aleksey Zaytsev of the Skoltech–Sberbank Applied Research Laboratory in an interview with TV BRICS. South African banks invested over R20 million in AI systems in 2024, reflecting a global trend rather than a unique breakthrough. A key tool is graph neural networks, which analyse client connections and transaction patterns to detect suspicious activity, even in new fraud scenarios. AI is also being used to personalise financial services, including tailored loans and dynamic insurance pricing. However, regulators warn of risks, including data privacy concerns and algorithmic bias, and South Africa’s FSCA is preparing regulatory frameworks for responsible AI use in banking.

Associate Professor and Head of the Skoltech–Sberbank Applied Research Laboratory, Aleksey Zaytsev, has commented in an interview with TV BRICS on the growing use of artificial intelligence (AI) in South Africa’s financial sector. Earlier reports indicated that more than half of the country’s major banks invested over 20 million South African rand (approximately US$1.26 million) in AI systems to combat fraud in 2024.

According to Zaytsev, such investments should not be viewed as a unique breakthrough, but rather as part of a natural process observed in all healthy and developing markets. He highlighted graph neural networks as one of the most promising technologies in the fight against financial fraud.

“What matters is how a client interacts with other people: who they transfer money to, in what amounts, and how typical these behavioural patterns are. The network of connections between clients forms a graph of relationships around each individual. Analysing these links makes it possible to detect fraud even in cases where the system has not previously encountered this type of deception,” Zaytsev explained.

As an example of a country where such technologies are advancing most rapidly, he cited China, where the large number of banks and artificial intelligence researchers is driving rapid technological progress. He noted that similar trends can be observed in Russia, where a strong community of machine learning specialists has developed within the banking sector and has long been working on comparable models.

Commenting on the exchange of technologies and best practices among BRICS countries, Zaytsev observed that professional interaction largely takes place at a local level, primarily through industry conferences within individual countries. Broader discussions of methodologies, he said, tend to occur in academic settings.

“On a more global level, there are now many international conferences on machine learning. Among the leading forums relevant to BRICS, for example, is the Asian Conference on Machine Learning, which attracts a very strong line-up of professors. Academic conferences discuss the latest methods and provide insight into where the field is heading, as well as which approaches are effective and which are not,” he said.

Zaytsev also addressed the role of artificial intelligence in the personalisation of financial services. He noted that AI technologies not only help to identify fraudsters, but also enable banks to gain a better understanding of clients’ needs. At the same time, he acknowledged that these technologies remain imperfect. He added that banks in different countries tend to tackle fraud in broadly similar ways: while external tools are often used to assess borrowers’ creditworthiness, institutions generally rely on their own internal systems to protect against fraud.

According to a report by South Africa’s Financial Sector Conduct Authority (FSCA), artificial intelligence in the financial sector is being used not only to enhance security, but also to personalise services. Algorithms are helping to offer customers tailored loans and insurance products, while dynamic pricing models are being introduced in the insurance market. Under these models, premiums may change in real time based on data from vehicle telematics or fitness trackers.

At the same time, regulators have highlighted a number of risks, including threats to data privacy, potential algorithmic bias and systemic vulnerabilities. In response, the FSCA plans to develop regulatory approaches for the use of AI technologies within the financial sector.

Source of photo: Khanchit Khirisutchalual / iStock

Source of article : TV BRICS