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 fraud prevention and detection market will grow from $24.8 billion to $65.8 billion between 2021 and 2026, according to Markets and Markets. With more buyers finding their comfort in online shopping, the rate of fraudulent activities will grow, and companies need robust systems to counter-attack.
Types of retail and e-commerce business frauds
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 frauds in the online retail space include:
- Clean fraud:
- ID theft:
- Friendly fraud:
This refers to any fraudulent transaction that bypasses the merchant’s regular checks.
These types of frauds happen when a fraudster uses the identity of another buyer and scams businesses.
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.
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.
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 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 e-commerce frauds
Many companies rely on a manual review of their orders. However, this process is extremely inefficient. It consumes both time and human resources and does not guarantee good results. According to a report, merchants spend 52% of their 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 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
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.
Automation through AI/ML
Retailers that use traditional methods run the risk of hitting high false positives. Legacy systems detect only known frauds. 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 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 fraud detection and prevention model
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. Automate your fraud prevention process with fewer false positives, better ROI, and improvement in potential savings. The solution offerings include:
*For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.