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 knowledgedriven 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
Mobile money fraud , credit risk , anti-money laundering are at the forefront of concerns for Communication Service Providers . 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 , highvolume 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 , antimoney 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 . •
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