Building a robust risk and fraud prevention strategy: best practices for abuse detection for sellers

Anirban Chakraborty, a specialist in Risk, Financial Crimes, Fraud, and Security, shares best practices for merchants to build a robust risk and fraud prevention strategy.

The rise of ecommerce has brought unparalleled opportunities for sellers, but it has also opened the door to sophisticated fraud schemes. In today’s dynamic ecommerce landscape, sellers face a growing spectrum of fraud risks - from identity theft and account takeovers to policy abuse and Fraud-as-a-Service (FaaS) that can jeopardize revenue, brand reputation, and customer trust. To combat these evolving threats, a proactive, multi-layered fraud prevention strategy is no longer optional; it is imperative. Did you know that nearly 60% of online merchants reported experiencing fraud in the past year? According to recent ecommerce fraud statistics from WiseNotify, businesses are facing escalating losses that underscore the urgent need for robust, multi-layered fraud prevention strategies.

Fraud in ecommerce is not a one-dimensional problem. Sellers must contend with a range of challenges, including:

• Identity theft and synthetic identities: fraudsters exploit stolen personal data or combine real and fabricated information to create synthetic identities at the account opening stage, enabling unauthorised transactions, and a plethora of policy abuse etc;

• Account Take Overs: cybercriminals gain access to legitimate accounts through phishing or credential stuffing, often draining stored payment methods, account balance;

• Counterfeit product listings: sellers listing fake or misrepresented goods, violating intellectual property laws, and deceiving buyers;

• Policy abuse: unauthorised reselling, exploiting price differences between regions or platforms, falsely advertising products as being in stock when they are actually drop shipped from another source, creation of fake positive reviews for their own products or fake negative reviews for their competitors' products to manipulate customer perception;

• Unethical listing practices: using deceptive product images or false descriptions to lure customers;

• Data manipulation: creating multiple accounts or falsifying credentials to bypass verification processes;

• The rise in fraud-as-a-service: the dark web has given rise to a thriving ‘fraud-as-a-service’ ecosystem, where bad actors can purchase pre-built tools and services to execute everything from identity theft to payment fraud. Fraud-as-a-Service (FaaS) describes a new type of fraud that is gaining prominence in the digital age. These services are typically offered on the dark web, where anonymity is guaranteed, making it easier for individuals and small criminal groups to commit fraud, as they do not need to have specialised knowledge or resources.

These activities erode consumer trust, hamper integrity, increases operational costs for platforms, and attract regulatory scrutiny. Fraud methods are becoming increasingly sophisticated, which underscores the urgency for a robust prevention framework. Traditional static solutions quickly become obsolete against adaptive fraudsters.

Adapting proven fraud prevention techniques can be highly effective in mitigating seller abuse. A comprehensive strategy should incorporate the following elements:

Identity validation: use solutions to verify and business licenses, tax IDs, and personal data during the registration process to truly evaluate the individual/business are who they claim they are;

Bank account validation: cross-verify seller bank details with government databases to flag shell companies or stolen identities;

Phone/email screening: validate contact information to ensure sellers are not using disposable numbers or emails.

Begin by conducting a thorough evaluation of seller activity on your platform. Analyse historical data to establish baseline behaviours and flag deviations that might indicate abuse. Advanced analytics can reveal patterns. Deploying machine learning models to analyse seller behaviour, such as:

• Irregular login patterns;

• Unwarranted changes in the seller profile;

• Unusual spikes in positive reviews;

• Rapid inventory turnover inconsistent with product type;

• Mismatched shipping and tracking data.

These patterns and signals would warrant further detailed investigation and appropriate actions.

Develop and enforce clear guidelines regarding product listings, review manipulation, and policy compliance. Transparency in rules and penalties creates a deterrent for potential abusers.

Educate fraud and abuse detection teams on the latest seller abuse trends and ensure periodic audits of established tech solutions and policies. An informed team is crucial for early detection and swift action.

While robust measures are essential, it is equally important to ensure that genuine sellers are not unfairly penalised and add unnecessary friction to the seller experience Striking this balance helps maintain a positive marketplace experience.

Seller fraud prevention is inherently complex, requiring a balance of technology, policy, and human oversight. Even with a strategic framework, robust tools, and models, platforms often struggle. Some common pitfalls include:

1. Inadequate identity verification at scale: platforms often prioritise rapid seller onboarding to drive growth and cater to customer demands. This might sometimes open some gaps in the identity verification processes. Manual reviews are time-consuming, while automated systems may lack the sophistication to detect forged documents or synthetic identities. For example: a seller tries to register with a stolen tax ID or a counterfeit business license. Basic checks (such as matching names or some sort of entity verification) might pass, but a deeper validation like cross-referencing against government databases/data consortium is what will make the verification full proof. Partnering with third-party verification services to automate checks against global business registries, tax authorities, and sanctions lists and implementing biometric verification are also some strong controls for consideration.

2. Fragmented systems and data silos: when different tools and systems operate in isolation, critical data can be missed. Disconnected seller management platforms and fraud detection systems or even isolated enforcement tools may lead to blind spots, allowing abuse to slip through the cracks and making it easier for unethical practices to go undetected. It is imperative to establish robust data infrastructure for holistic fraud detection.

3. Reliance on outdated technologies: fraudsters constantly refine their tactics, and static systems quickly become obsolete. Without periodic audits, frequent enhancements, and technological refreshes, abuse and fraud detection might be relying on legacy systems that fail to capture or respond to modern abuse strategies effectively.

4. Collusion and networked fraud: fraudulent sellers operate in organised networks, spreading activities across multiple accounts to evade detection for example: a ring of sellers uses shared devices, IP addresses, and bank accounts to list counterfeit products. Individually, each account appears legitimate; collectively, they dominate search rankings. Deploying network graph analysis to map connections between sellers, buyers, and devices and enforcing entire clusters of linked accounts instead of individual sellers should be the approach.

5. Inconsistent enforcement and lack of transparency: when policies are vague or enforcement is applied unevenly, it creates loopholes that sellers can potentially exploit. Inconsistent application of rules can lead to uncertainty among sellers about what constitutes abuse, thereby undermining the effect of established policies and penalising good/large established sellers.

Seller abuse presents a unique challenge in the ecommerce space, it demands a proactive and multifaceted approach. By adapting proven fraud prevention techniques and continuously monitoring seller behaviour, marketplaces can protect their reputation and ensure a fair, trustworthy environment for all participants. Seller fraud prevention strategies fail not due to a single flaw but a combination of technological limitations, operational inertia, and strategic misalignment. A balanced strategy that incorporates advanced analytics, real-time monitoring, and transparent policy enforcement not only deters abuse but also supports long-term marketplace integrity. Embracing these best practices is essential for any platform committed to mitigating seller abuse and fostering a secure, reliable, and trustworthy shopping experience.

As fraudsters continue to refine their tactics, the need for dynamic, adaptive prevention strategies has never been greater. Ecommerce platforms must invest in advanced analytics, embrace continuous monitoring, and foster cross-departmental collaboration to stay ahead of emerging threats. Now is the time for industry leaders to act—by strengthening these strategies, they not only protect their revenue and reputation but also build lasting trust with their customers.

About Anirban Chakraborty

With about 10 years of professional experience, Anirban Chakraborty specialises in the Risk, Financial Crimes, Fraud, and Security domains. He is a Senior Industry Specialist in fraud Analytics at Amazon, leading large-scale technical projects focused on merchant registration abuse, identity fraud, and Account Takeovers. Before joining Amazon, he spent 6 years at Ernst and Young (EY), where he held the position of Technology Consulting Manager within the Financial Crimes practice. His expertise spans various areas, including Regulatory Compliance, AML Transaction Monitoring, Fraud Detection and Prevention, and Sanctions Screening. Anirban has a formal education in Business Analytics and Data Science and is passionate about leveraging Machine Learning and AI for effective fraud detection and prevention.