Fraud Retail

Fraud prevention in retail and e-commerce with automation

Retail and e-commerce businesses took a giant leap during the pandemic and so did fraudulence. According to a study in Statista, losses in online payment came close to $20 billion in 2021, a 14% increase in one year. Of all the sales outlets, 54% of customers encountered frauds in online transactions, followed by spam calls, door-to-door sales, postal mail, and in-store sales.

With these statistics in hindsight, business owners are looking for robust systems to help them detect frauds and prevent revenue loss. Consequently, the global ecommerce fraud prevention and detection market will grow from $24.8 billion to $65.8 billion between 2021 and 2026, according to Markets and Markets, driven by the rise in account takeover and payment fraud. With more buyers finding their comfort in online shopping, the rate of fraudulent activities will grow, and companies need robust systems to counter-attack, such as fraud detection ecommerce tools and systems.

Types of Ecommerce Fraud: Understanding Common Fraud Trends and Prevention

Fraudsters are everyday inventing new vulnerabilities to exploit, and the fact that most of us have been targeted, if not fallen victim to fraud, shows the widespread nature of the problem. The sophistication of these fraudulent transactions has also increased over time. The most common online types of e-commerce frauds include:

Clean fraud:

This refers to any fraudulent transaction that bypasses the merchant’s regular checks.

ID theft:

These types of frauds happen when a fraudster uses the identity of another buyer and scams businesses.

Friendly fraud:

This involves an intentional malpractice of ordering an item and then claiming a chargeback after receiving the goods. In such cases, customers often deny that they received the goods.

Phishing:

This refers to emails that con people into clicking on a link that reveals confidential information to a fraudster. The fraudster can then use it to carry on online transactions.

Botnets:

Fraudsters hijack and remotely control a network of computers that become nodes to carry out further fraudulent activities.

It is clear that one cannot depend on manual ecommerce fraud detection methods for such a wide range of challenges over millions of orders. Take the case of false positives wherein a system that is not mature triggers an alarm even for a legitimate transaction. This can cause customer dissatisfaction who may switch over to your competitors.

Manual detection of online retail frauds

Many companies rely on a manual review of their orders. However, this process is extremely inefficient and often fails to detect and prevent fraud effectively. It consumes both time and human resources and does not guarantee good results in detecting and preventing fraud. According to a report, merchants spend 52% of their ecommerce fraud management budget on manual reviews.

eCommerce is a highly competitive market. Customers switch from one company to another quickly. Businesses are not only vying to provide the best offers but also gain customer loyalty and retention. Companies need to simplify fraud detection and prevention to make better decisions and increase revenue.

Automation through online identity verification and Fraud Risk Reduction

One of the most efficient and low-cost online verification methods is IP intelligence (geolocation). It automatically calculates the location of the device and combines it with other order acceptance rules to calculate the fraud risk associated with that transaction. This, in turn, can support fraud detection ecommerce systems by ensuring only legitimate transactions are processed.

Boost Security with Fraud Detection Ecommerce – Explore Automated Solutions Now!

Automation through AI/ML for Ecommerce Fraud Prevention

Retailers that use traditional methods run the risk of hitting high false positives. Legacy systems detect only known frauds, which leaves them vulnerable to emerging threats like account takeover fraud. On the contrary, AI-based systems use predictive algorithms to proactively detect anomalies and raise red flags. This helps retailers identify weak spots and anticipate potential fraud issues before they happen.

Deep learning and AI algorithms analyse vast amount of historical data and isolate millions of data points to keep your transactions secure. AI-driven systems can either block the transaction or raise an alert for the company to investigate. Sophisticated systems bring agility through self-learning and optimising algorithms based on past transactions. Here is how a typical AI-/ML-based system works. As the system matures, it becomes better at each of these steps:

• Inputting the data
• Extract the data points to analyse
• Training the algorithm and continuous improvisation
• Creating a ecommerce fraud detection and prevention model

These steps together ensure that fraud detection ecommerce systems not only block fraudulent transactions but also continuously learn and adapt to new fraud trends.

Infosys BPM's Ecommerce Fraud Management Solutions

At Infosys BPM we are using a Human-ware approach to come up with solutions for organisations to combat fraud. Our AI-/ML-powered and future-ready technology solutions for the retail and e-commerce industry, together with a team of experienced professionals, is helping transform businesses and combat payment fraud. Automate your ecommerce fraud prevention process with fewer false positives, better ROI, and improvement in potential savings. The solution offerings include:

• eCommerce fraud management
• PoS fraud management
• Product counterfeit management

By incorporating advanced fraud detection ecommerce capabilities into our solutions, we can deliver comprehensive protection across all points of risk in the e-commerce value chain.

View our retail and e-commerce fraud management solution.

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