Combating e-commerce fraud: Harnessing the power of AI, ML, and RPA to safeguard profits

The rise of the e-commerce industry has brought immense convenience and opportunities for businesses and consumers alike. However, this growth has been met with an increase in fraudulent activity, causing significant financial losses for companies. Merchant losses related to online payment fraud are expected to exceed $343 billion by 2027. To combat this persistent threat, the e-commerce industry can harness the power of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to help mitigate these losses, prepare for new fraud attempts and ensure that the customer experience remains intact. 

Utilizing AI-enabled, automated financial operations (FinOps) solutions allows for continuous real-time transaction monitoring. These technologies can effectively bolster security measures by enabling vendors to monitor transactions in real-time, compare them to historical data and safeguard against fraudulent activities by identifying suspicious activity that deviates from normal behavior. Moreover, the adaptive nature of AI empowers it to continually learn and adapt to emerging dishonest tactics, making it an indispensable asset in the relentless battle against illicit activities.

Early Fraud Detection and Prevention

97 percent of global fraud prevention decision-makers at e-commerce companies experienced fraud in the past 24 months. To decrease this problem, many are turning to AI and ML to solve the growing issue. AI and ML algorithms have proven to be highly effective in identifying patterns and anomalies in large datasets. These technologies can detect fraudulent activities early and prevent financial losses by analyzing historical transactional data and user behavior. Machine learning models can learn from patterns of fraudulent behavior and continuously evolve to detect new types of fraud. To flag suspicious activities in real time, AI-powered fraud detection systems can evaluate purchase history, payment details, device information, geolocation and browsing patterns.

Improved User Authentication

Identity theft and account takeover are common fraud techniques in the e-commerce industry. Traditional username and password-based authentication systems are no longer sufficient to counter such threats. AI and ML can enhance user authentication by implementing biometric identifiers like facial recognition, fingerprint scanning, or voice recognition. These technologies offer a higher level of security by making it harder for fraudsters to impersonate legitimate users. Moreover, ML algorithms can continuously learn and adapt to evolving fraud techniques, ensuring robust protection against unauthorized access and protecting authentic customer accounts.

Curb Staffing Shortage Challenges

Many companies across industries continue to deal with the complexities of labor shortages, but AI can help ease this stressor. Where businesses once employed several people dedicated to reviewing transactions, automated accounting featuring AI and ML can analyze millions of data points to flag irregularities. E-commerce businesses that leverage the power of a FinOps solution can rest easy knowing that costly e-commerce marketplace chargebacks and overbilling are being handled, identifying errors in claims, duplicate billing, and the more than 100 potential chargeback categories.

Fraudulent Pattern Detection

AI and ML can assist in identifying complex patterns and relationships often missed by traditional rule-based fraud detection systems. For instance, ML algorithms can detect irregular purchasing behaviors, such as sudden high-value orders or multiple orders from different locations using the same credit card. By recognizing these patterns, e-commerce platforms can proactively investigate suspicious transactions and take appropriate measures to mitigate potential losses.

Enhanced Transaction Risk Scoring

Integrating AI and ML in e-commerce platforms can lead to the development of sophisticated risk-scoring models. These models assign risk scores to individual transactions based on customer behavior, transaction history, and geolocation. By leveraging historical data and real-time analytics, risk scoring enables businesses to implement dynamic security measures, such as step-up authentication or additional verification, for high-risk transactions, reducing the chances of fraudulent activities slipping through the cracks.

Streamlined Fraud Investigation and Resolution

RPA can play a crucial role in streamlining fraud investigation and resolution processes. RPA technology can automate repetitive manual tasks involved in fraud investigation, such as data gathering, verification, and documentation. Businesses can save valuable time and resources by reducing human error and accelerating the investigation process. RPA can also be integrated with AI and ML algorithms to analyze multiple data sources simultaneously and provide accurate insights for decision-making, enabling faster resolution of fraudulent cases.

Fraudulent activities significantly threaten the e-commerce industry, leading to substantial financial losses and damaged customer trust. Businesses can bolster their fraud prevention and detection mechanisms by harnessing the power of AI, ML, and RPA. Early fraud detection, improved user authentication, fraudulent pattern detection, enhanced transaction risk scoring, streamlined investigation processes, and collaborative intelligence are some ways these technologies can be leveraged to mitigate fraud-related losses, so businesses can spend less time worrying about accounting and more time increasing profitability and driving growth.

Image Credit: Gustavo Frazao / Shutterstock

As founder of DimeTyd, Rohan Thambrahalli is a serial entrepreneur and innovator focused on advancing E-commerce. With more than 20 years of experience in sales, marketing and business development, Rohan is a leader passionate about refining and re-engineering E-commerce technologies to drive exponential growth for global brands ranging from beauty, health and personal care to electronics, automotive and industrial/commercial.