Fraud is never an isolated event and occurs in an ecosystem, which is why a patchwork of tools will not help against attackers, who are becoming increasingly clever. All events, all actors, all channels must be considered in real-time in correlation with each other, explains Joao Faisca at INFORM.
In the dynamic financial landscape of the Middle East, where telecommunications and banking are seamlessly integrated, mobile money has emerged as a cornerstone of financial activity. With the involvement of the IT team, this convergence could deliver opportunities and challenges, particularly in the realms of fraud prevention, credit risk management, and anti-money laundering compliance.
The region’s rapid adoption of mobile financial services has necessitated IT teams to support innovative approaches to ensure security and trust. Enter the Hybrid AI approach, a sophisticated blend of artificial intelligence and human expertise, designed to navigate the complexities of the modern financial ecosystem.
IT executives, tech-savvy professionals, software developers, among many others, have come to embrace 5G as it inaugurated a new telecommunications era. Bringing faster speeds and more reliable connections to its area of coverage, a focus on 5G monetisation and the development of private 5G networks highlighted the region’s commitment to leveraging advanced technology to enhance connectivity and enable financial services.
The convenience and efficiency of mobile payments, underpinned by the region’s robust telecommunications infrastructure, have fundamentally transformed how financial transactions are conducted.
Role of governments
Recent statistics underscore the urgency and potential of this technological evolution. With noncash payments in the UAE growing from 39% in 2018 to 73% in 2023, and fintech revenues in the Middle East, North Africa, and Pakistan projected to rise significantly by 2025, the momentum is clear.
However, this convenience comes with increased risks. Mobile money fraud, credit risk, and challenges in anti-money laundering compliance are at the forefront of concerns for Communication Service Providers, CSPs, who are increasingly assuming the roles traditionally held by financial institutions.
The digital nature of transactions, coupled with the speed and anonymity they offer, creates a fertile ground for fraudulent activities. The UAE, for example, has been actively pursuing hundreds of cases related to financial crime, including money laundering and terrorist financing. Their Executive Office for Anti-Money Laundering and Counter Terrorism Finance has been instrumental in spearheading these efforts, resulting in prosecutions and deportations.
While this demonstrates the UAE’s commitment to clamping down on illicit financial flows and enhancing the integrity of its financial system, it also shows the need for IT and technological support in combating financial crime.
Role of CSPs
In response to these challenges, CSPs themselves have a duty to take control of risk assessments and to prevent fraud. For effective fraud prevention and minimisation of payment defaults, it is essential to check creditworthiness and other factors that could indicate possible fraud as early as during customer onboarding, and throughout the entire customer cycle. Only those who report suspicious behaviour immediately can isolate harmful cases.
There is no denying that companies are keen to improve control and therefore data security, relying heavily on IT. However, experts warn that many telecommunications companies, in particular, are still inadequately equipped and work with a fragmented collection of tools and mechanisms to prevent fraud.
A patchwork of tools will not help against attackers, who are becoming increasingly clever. This is why a holistic strategy is necessary. After all, fraud is not an isolated event but always occurs in an ecosystem. All events, all actors, and channels must therefore be considered simultaneously, in real-time and in correlation with each other. What is more, smart machines and algorithms help to identify patterns and counter them quickly.
Hybrid AI
A hybrid AI approach offers a sophisticated blend of artificial intelligence, data analytics, and human insight, meaning the integration of data-driven and knowledge-driven artificial intelligence methodologies. The former includes machine learning algorithms, for example.
They search huge amounts of data for recurring correlations and patterns that indicate criminal behaviour. The latter refers, for example, to fuzzy logic or dynamic score cards, which human experts use to define complex rules for dealing with certain patterns of behaviour.
These kinds of systems make it possible to derive concrete decisions and recommendations for action even from imprecise data. Therefore, the hybrid AI approach leverages the vast amounts of data generated by mobile transactions, but also industry experience gained over many years.
Best practice knowledge-based rules can be implemented to detect and prevent fraud and other illegal activity within the payment, online transactions, and credit management environments. This hybrid approach takes the best of knowledge-based and machine learning worlds to create a powerful financial crime-fighting strategy.
Challenges of mobile transactions
The fluid and high-volume nature of mobile transactions in the region presents complex challenges for anti-money laundering compliance. Anti-money laundering compliance is particularly demanding in a landscape characterised by rapid, high-volume transactions.
In the case of mobile money, where customers use their phones as a bank account for financial operations, like registrations, logins, financial transactions, or loan requests, even an integrated approach that takes fraud prevention and anti-money laundering compliance into account, FRAML, seems feasible.
In this field, best practice AI-based solutions combine fraud prevention, anti-money laundering compliance, and credit management in one end-to-end platform throughout the customer journey. For example, when a new customer signs up for registration, AI can perform customer segmentation and risk scoring based on various input data, but at the same time scan the customer against watchlists and sanction lists.
Comparable integration of fraud-related and compliance-related evaluations during the entire customer lifecycle will dynamically trigger in real-time with each new action a customer takes.
The strategic adoption of hybrid AI technologies is critical in navigating the complexities of the Middle East’s digital financial ecosystem. The telecommunications sector’s growth, coupled with the rapid adoption of 5G and advancements in mobile financial services, provides a fertile ground for implementing hybrid AI solutions.
As the region continues to evolve, the application of these technologies will be instrumental in fostering a secure, inclusive, and innovative financial landscape.
