Online Fraud Trends of 2024: Fraud Detection Machine Learning, eCommerce Scams and more

These Are the Fraud Trends to Know in 2024

Cyber-Enabled Fraud

Cyber-enabled fraud (CEF) is an escalating issue involving organised crime syndicates that are now exploiting advanced digital technologies and artificial intelligence to trick victims and conduct financial extortion. These syndicates are structured into specialised groups, including those focused on money laundering operations to process ill-gotten gains. 

The CEF money laundering networks utilise a variety of actors and mechanisms, such as money mules, shell companies, and legitimate businesses, across different types of financial institutions. These include banks, payment services, and virtual asset service providers (VASPs). Techniques employed to obscure the origins of their illicit funds include the use of cash transactions, trade-based laundering, and unlicensed services.

CEF money laundering often exploits weak know-your-customer (KYC) policies, underscoring the importance of robust verification processes to prevent unauthorised activities and protect sensitive information.

Some red flags that help detect suspicious transactions relating to CEF are:

• Transfers of funds to and from high-risk money laundering jurisdictions
• Large frequent transactions with recently established companies 
• Rapid or immediate high or low-value transactions from newly opened accounts 
• Transactions or activities that are inconsistent with the purpose of an established account, entity or beneficiary

Heightened Crypto Risks

The rapid proliferation of cryptocurrency-enabled technologies necessitates closer oversight. Their decentralised, virtual nature makes them appealing for fraudulent activities. Cryptocurrencies are unsupervised by any government or central bank, unbound by geographical borders, can maintain anonymity and enable purely digital transactions.  

The Financial Action Task Force (FATF) pioneered a comprehensive strategic response to cryptocurrency risks in 2018, amending its standards to include cryptocurrencies explicitly, followed by updates and clarifications. Its regulatory framework for cryptocurrencies parallels traditional financial oversight, mandating countries implement the full AML/CFT framework with adjustments for the unique technologies involved. Continuing its pattern, European regulations like the Markets in Crypto-Assets (MiCA) outline how crypto businesses must adhere to stringent anti-money laundering standards, reflecting the global push for more secure and regulated online financial activities. 

eCommerce Scams

eCommerce consumers are seeing a rise in scams due to vague business policies and deceptive practices on eCommerce sites, from misleading product representations to aggressive sales tactics. Awareness of these red flags is crucial for consumers and businesses to safeguard their interests.

Signs of potential scams include: 

• Unclear shipping details, which indicate a lack of transparency about costs and policies
• Poor product presentations, such as low-quality images or errors in descriptions
• Extremely low prices, especially for well-known brands 
• Urgency tactics or high-pressure situations that encourage immediate purchase
• Absent contact information or non-functional contact methods

Ambiguous AI

While AI significantly boosts the fintech sector’s efficiency and accuracy in detecting fraud, it introduces complex challenges. The lack of standardised regulations for AI’s responsible use and the biases embedded in AI systems complicate adherence to compliance and ethics, necessitating a prudent approach to deployment. Marketplaces must carefully manage potential biases affecting decisions in crucial areas. The technological advances of AI that empower fintech firms also create vulnerabilities that cybercriminals exploit, requiring stringent security strategies to counteract these evolving threats.

When implementing AI in fraud detection or any related applications, there are several red flags that organisations need to be aware of to ensure the technology is used effectively and ethically. 

Some examples include:

• Unexplained decision making: AI systems that do not provide clear, understandable explanations for their decisions –  it is crucial that AI decisions can be interpreted and justified, especially when they affect financial transactions or customer relationships
• High false positive rates: If an AI system often mistakenly flags legitimate transactions as fraudulent, it may suggest issues like overfitting or incorrect feature weighting in the model
• Lack of adaptability to new fraud techniques: AI systems that are not regularly updated or trained on new data may fail to catch novel or evolving fraud tactics
• Ignoring contextual and qualitative data: AI models that rely solely on quantitative data and ignore qualitative contextual information may miss important cues that can indicate fraud 

Misuse of NGOs and Crowdfunding Platforms 

Crowdfunding platforms and NGO misuse are primary concerns for the FATF, presenting unique obstacles for online business operations that require robust, proactive defences. Criminal organisations cleverly mask their fundraising activities on social media as legitimate humanitarian efforts linked to charities, complicating tracking efforts by the intelligence community, private sector and donors.

They channel funds through various means, including:

• State funding through cash transactions, cross-border payments, trade-based terrorism financing, exchanges, and banking systems
• Business portfolios, including real estate and investments 
• Fundraising through social media and crowdfunding initiatives via bank accounts, payment services, and crypto exchanges
• Misdirected humanitarian aid