Saudi Arabia’s Battle Against Financial Crime: A Tech-Driven Approach with AI and Machine Learning

Amidst the tranquil desert landscape of the Middle East, a dangerous storm was brewing in the local banks. In a calculated hacking attack in Kuwait, nearly 100 unsuspecting clients fell victim to a new, cunning weapon - fraudulent emails.

Dangerous Storm in the Banking Sector

The attacks began with seemingly harmless links hidden in fraudulent emails, expertly disguised to mimic official communications. Logos of trusted entities, including Ministries of Communications and courier companies like DHL and Aramex, were used to gain recipients’ trust. After luring victims with the promise of a small delivery fee, the thieves gained remote access to victims’ devices and plundered the banking data realm. Stolen funds ranged from smaller sums of $1,010 to a staggering $50,544,600 per operation.

Given the insidious nature of these threats, maintaining the sanctity of personal information was more important than ever.

Enter AI in the Kingdom’s Fight Against Financial Crime

To combat the growing tide of money laundering and financial crimes, companies in the Middle East, including Saudi Arabia, are taking a tech-driven approach, leveraging AI and machine learning to strengthen their defenses.

Mozn’s AI-powered Risk and Compliance Platform, FOCAL

Saudi-based AI tech company Mozn, with its recent expansion into the UAE, has introduced an AI-powered risk and compliance platform called FOCAL. FOCAL uses advanced algorithms, pattern recognition, and APIs to integrate anti-money laundering (AML) and anti-fraud compliance capabilities into banks’ core systems. The platform combines data points to score risk for fraud and money laundering, automatically suggesting next steps based on the organizational risk appetite.

“FOCAL can detect user behaviors such as end-user location, beneficiary within a specific country or region, and user network, if the beneficiary is part of a suspicious network or fraudulent ring,” says Dr. Mohammed Al Hussein, CEO and Founder of Mozn.

AI and Machine Learning in Global AML Efforts

According to a recent global anti-money laundering research study, 57% of institutions have already integrated or are in the process of integrating AI and machine learning into their AML departments. One-third of financial institutions are accelerating the adoption of AI and ML technologies for AML.

“The findings highlight the growing recognition of AI and ML as crucial tools in combating money laundering, with financial institutions focusing on enhanced investigations, streamlined filings, and operational efficiency, even amidst the pandemic,” remarks Grozdana Maric, Head of Fraud & Security Intelligence EMEA Emerging and AP at SAS.

SAS’s Hybrid Analytics Approach for Enhanced Threat Detection

SAS utilizes a hybrid analytics approach, combining AI, ML strategies, and network analytics to help financial institutions easily identify sophisticated threats. Entity Resolution, a powerful method used by SAS, enables the organization to recognize user data and connect related entities within the data.

“Extracting valuable information from unstructured text sources, such as SWIFT messages or customer complaints, helps us enrich your dataset and gain invaluable insights,” notes Maric.

The Middle East AI Market and its Impact on Financial Crime Prevention

The Middle East AI market is projected to reach a value of $320 billion by 2030, indicating its immense potential as companies in the region increasingly tap into the space to combat financial crimes.

Embracing AI to Dismantle Financial Crime

Traditional financial crime detection methods, such as rule-based systems and manual reviews, face significant challenges, particularly when handling the massive volume of daily transactions and evolving regulatory compliance requirements. Manual review of each transaction is practically impossible, and rule-based systems that flag transactions based on predefined rules often generate numerous false positives, wasting time and resources on investigating innocent transactions.

However, institutions can better understand complex patterns and intricate links within and across transactions by leveraging AI’s continuous learning and advancements, enhancing their ability to detect and mitigate financial crime risks.

The Future of Financial Crime Prevention: Trusting Machines

AI’s effectiveness relies heavily on continuous learning and adapting to new data and emerging fraudulent schemes. To ensure a robust learning model and create a secure system, experts, like Yahya Alazri, Security Consultant of the Ministry of Transport, Communication & Information Technology, emphasize the importance of timely updates and appropriate data sets.

“Neglecting updates or using inappropriate data sets can create vulnerable spots where users are susceptible to specific scams,” says Alazri.

With AI and machine learning solutions, financial institutions can adapt, learn, and proactively mitigate risks in an increasingly complex and interconnected world, one transaction at a time, effectively eradicating financial crime.