Seasonal Surge in Payment and Fraud Trends
The holiday season brings joy and excitement, but it also ushers in a sharp rise in payment fraud and scams, putting both businesses and consumers at heightened risk. As digital transactions skyrocket, the year-end months create a lucrative ground for digital fraudsters. According to a 2023 report by AARP, around 80% of U.S. consumers have encountered some type of holiday scam, which is an alarming trend that intensifies as holiday shopping ramps up.
During this high-stakes period, businesses must contend with various fraud tactics, including account takeovers (ATO), identity fraud, and synthetic fraud schemes, each designed to infiltrate and exploit digital payment systems. With fraudsters leveraging the seasonal surge in transaction volume to mask suspicious behavior, identifying and managing these risks becomes more challenging than ever. This season’s spike in fraudulent activity underscores the need for robust, adaptable fraud prevention strategies, particularly those enhanced by AI and machine learning.
In this article, we will look into:
- Key Fraud Trends to Watch During Year-End Peak Periods
- The Need for AI-Enhanced Enterprise Risk Management (ERM)
- Real-World Applications of AI-Driven Risk Management Solutions
Key Fraud Trends to Watch During Year-End Peak Periods
With an influx of online transactions, fraudsters capitalize on the busy year-end festive and shopping seasons by deploying sophisticated tactics. Here’s a look at the most concerning fraud trends retailers face:
Identity Fraud and Account Takeovers (ATOs)
As consumers increase their digital shopping, instances of identity fraud and mobile money payment fraud also spike. Fraudsters frequently use social engineering tactics like phishing to gain access to consumers’ payment accounts like e-wallets and digital banking, leading to unauthorized transactions and stolen funds. In addition to payment fraud, account takeovers (ATOs) are common as criminals exploit personal data to hijack accounts and make unauthorized purchases.
Chargeback Fraud (Friendly Fraud)
Global e-commerce sales are expected to total $6.09 trillion worldwide in 2024 and the total revenue from online transactions is expected to surpass $6 trillion, after an 8.4% increase from the previous year. Chargeback fraud becomes a particular issue during the holiday season, as customers may initiate disputes on legitimate purchases, known as ‘friendly fraud’, leading to revenue loss. Retailers often face a surge in these dispute as consumers may not recall all holiday purchases or experience buyer's remorse. These behaviors drive up costs in revenue management, as they strain resources meant for legitimate chargebacks. In 2023, chargeback fraud resulted in a loss of $20 billion for merchants worldwide.
Phishing and Social Engineering Scams
The holidays see an increase in phishing scams, as fraudsters aim to collect personal information from unsuspecting consumers. This data is often used for identity fraud or to infiltrate e-wallet or other digital payment accounts, leading to financial losses for both consumers and businesses.
Anomalies in Shipping and Payment Locations
During the festive season, legitimate customers often place orders from locations that don’t match their usual shipping addresses, creating challenges in risk management. Fraud detection models may inaccurately flag these orders, considering them as indicators of fraud trends, like identity theft or drop-shipping schemes.
Mule Behavior and Money Laundering Schemes
During the holiday season, fraudsters exploit unsuspecting customers as ‘mules’ to move stolen funds or goods, often enticing them with fake job offers or financial incentives. This practice helps mask the origin of fraudulent transactions, blending into the holiday rush of online orders and shipments.
The Need for AI-Enhanced Enterprise Risk Management (ERM)
As year-end shopping peaks, enterprises face a surge in transactions and increased vulnerabilities, requiring more advanced enterprise risk management (ERM) to combat rising fraud trends. Traditional ERM tools often struggle to keep pace with high transaction volumes, complex customer behaviors, and intensified threats, making AI-enhanced ERM an essential asset in detecting and mitigating fraud.
#1 Adaptive AI for Real-Time Anomaly Detection
AI algorithms continuously monitor transactions and behaviors in real-time, adapting to detect unusual patterns that emerge during peak periods. This includes identifying sudden changes in transaction volume, unusual account activity, or irregular shipping addresses, helping businesses proactively address potential fraud in real time.
#2 Scalable Fraud Detection During Transaction Surges
AI-driven risk management systems scale automatically to handle transaction spikes during year-end, which is vital for managing high transaction volumes. This scalability ensures fraud detection remains effective without system slowdowns, enabling optimal functions despite increased demand on resources.
#3 Advanced Machine Learning for Pattern Recognition
Machine learning models learn from historical fraud data, allowing ERM systems to identify and respond to new fraud trends as they develop. During the holiday season, machine learning algorithms help the risk management systems to detect less obvious threats, like fraudulent return and refund requests or gift card scams.
#4 Risk Scoring for Transactions and Accounts
AI-enhanced ERM applies dynamic risk scoring to transactions and accounts, flagging high-risk activities based on multiple factors such as transaction size, location, device type, and behavior. This helps quickly escalate suspicious transactions, protecting revenue while minimizing manual checks.
#5 Enhanced Data Protection and Compliance
The use of AI in ERM can enforce data protection and compliance by ensuring secure data handling, storage, and transaction monitoring across all digital touchpoints. This protects sensitive information from breaches, particularly critical during the festive season when digital payment use rises significantly.
Real-World Applications of AI-Driven Risk Management Solutions
Blocking Scam Calls in Real-Time
In peak shopping seasons, fraudulent call activities rise as scammers attempt to exploit the increased customer activity. Neural Technologies’ SCAMBlock intercepts and halts high-risk calls before they reach the customer, drawing on real-time data to identify and block scam patterns. The solution’s automated protection secures customers against phishing attempts, significantly reducing exposure to identity theft and keeping communication channels safe.
Assuring Revenue Integrity with Data-Driven Insights
During year-end peaks, ensuring each transaction is accounted for becomes essential. Revenue assurance applications leverage AI and machine learning to monitor revenue streams, detect anomalies, and resolve discrepancies proactively. Through seamless data extraction, processing, and quality checks, businesses maintain an accurate understanding of their revenue pathways. This keeps revenue flow secure, even amid high transaction volumes, safeguarding against any loss and supporting efficient revenue management.
Detecting and Mitigating Fraud Across Channels
Real-world AI fraud management solutions stand out for their ability to identify both conventional and emerging fraud types. With holiday transactions at an all-time high, these tools apply machine learning to analyze patterns in real-time, rapidly recognizing fraudulent behaviors like chargeback abuse, account takeovers, and identity fraud. By acting quickly, businesses can secure their platforms and reassure customers of their safety, reducing fraud risks without disrupting holiday transactions.
Managing Credit Risk Throughout Customer Lifecycles
High spending seasons challenge businesses to manage credit risk effectively. AI-powered credit risk solutions assess creditworthiness in real-time, adapting to changing customer spending behaviors. By applying dynamic credit limits and predictive risk scoring, these solutions provide timely insights into potential credit risks. This helps businesses control exposure to bad debt, especially valuable as consumers extend credit usage during holiday periods, thereby balancing customer flexibility with financial security.
Streamlining Customer Application Processes with AI
A sudden influx of loan or credit applications during holiday peaks can burden traditional application reviews. AI-based solutions expedite these processes by automating application risk assessment, providing clear oversight on customer eligibility without delays. This accelerates customer onboarding without sacrificing risk standards, enabling companies to quickly accommodate legitimate applications while efficiently filtering out high-risk ones.
Mitigating Money Laundering Risks and Ensuring Compliance
AI applications in anti-money laundering (AML) empower companies to monitor high-volume holiday transactions, identifying suspicious activities associated with potential laundering schemes. Using predictive analytics and automated KYC checks, these solutions keep onboarding fast yet secure, maintaining compliance and preventing financial risk. By continually monitoring for anomalies, businesses can confidently meet increased demand while ensuring regulatory standards are upheld.
AI-Driven Revenue Protection and Risk Management Solutions for Your Business
Neural Technologies’ highly flexible, AI-powered Revenue Protection suite of solutions offers an advanced approach to securing revenue streams by identifying and addressing risks as they arise. Using AI and machine learning, our solutions are designed to rapidly target and mitigate areas of revenue leakage with real-time precision, providing businesses with powerful insights and fast recovery capabilities, especially during year end peak periods.
By combining behavioral profiling and advanced analytics, the platform identifies vulnerabilities across multiple touchpoints, flagging any irregularities that could lead to revenue loss. As a result, businesses can quickly address and resolve leaks, improving operational efficiency and maintaining a stable revenue flow. Additionally, its adaptability means that as fraud trends evolve, the platform remains responsive—learning and adjusting to emerging fraud patterns for enhanced, long-term revenue protection.
