The evolution of the internet and smartphones has opened the doors for merchants and businesses to venture into the online world. Legacy businesses now have access to platforms that can tremendously maximize their digital footprint, leading to an abundance of choices for customers. It’s easier than ever to create a digital store and in turn, even easier for fraudsters to abuse these models.
The digital world has helped birth multiple models for merchants.
· Aggregator models try to create hyper-local options for services like cabs, hotel bookings, food delivery, etc.
· Marketplace models connect customers and sellers from different parts of the country and enable sellers to reach a wider customer base.
· Other models like peer-to-peer marketplaces aim to connect customers with a specific demand, and sellers with an appropriate offering. However, these are often unregulated.
The Problem
Unfortunately, the digital disruption of these models is accompanied by a rise in fraudulent activities. So far, customer fraud was more prevalent than merchant fraud. However, unlike customer fraud where a business goes through only monetary losses, partner fraud also leads to bad customer experiences and an adverse impact on brand reputation. The problem is compounded by the fact that 1 out of every 5 listings on marketplaces are fraudulent, according to research conducted by Besedo, an online moderation company. This is more alarming when we consider that up to 34% of marketplaces see partner fraud as a risk, as per a survey by Ravelin.
Marketplaces face a wide variety of threats when it comes to fraud, especially towards sellers, vendors, and merchants.
· Product-based sector –
o The most common ones include the selling of unauthorised products such as guns, explosives, etc., that violate not only company policies but also a country’s policies.
o Merchants may market and sell counterfeit products as genuine products.
o Another method is to sell pirated versions of books, movies, and games which are copyright violations sold to unsuspecting customers.
o Damaged/ refurbished/ used products may be sold as new unused products or may not be called out clearly when the products are listed online, catching innocent customers off-guard.
o Another major scam can be from merchants who collect money for goods that they don’t deliver.
· Hospitality sector –
o Fraudsters may list fake hotels and add fake reviews for these listings to attract customers to non-existent properties.
o Hotel aggregators will then have to scramble to identify alternate properties or provide refunds and appeasements to impacted customers within a limited timeframe.
o Another challenge that these aggregators face is collusion between the customers and partners leading to abrupt cancellations and commission avoidance which translates to revenue loss.
· Food and Beverages sector –
o Food aggregators too deal with concerns around missing items or incorrect order deliveries, often amounting to refund-related losses.
· Ridesharing sector –
o Cab aggregators may face deceitful drivers who use GPS spoofing to fake a trip in addition to artificially induced surge pricing by turning off their phones to create demand in a specific location – the latter is done in collusion with other drivers in what could be called a fraud ring model.
o Aggregators can also encounter incentive abuse which occurs when drivers create fake bookings using multiple phones.
Similar partner-driven frauds and abuses are common across aggregators and the digital marketplace business model. There are two primary reasons for this situation:
Global businesses: Most of these marketplaces/ aggregators are global and attract partners from across countries. Validating each partner based on the rules and regulations of each country would require diverse hyperlocal teams.
Scale: The marketplaces see an addition of hundreds of thousands of listings by merchants on a day-to-day basis. Verifying each partner/ merchant requires a lot of manual effort and the onboarding process would be extremely time-consuming.
The Solution - A tier-based risk mitigation model
A tier-based risk mitigation model involves the creation of multiple tiers that will be based on a business’s capacity to absorb risks. Let us consider an example of a marketplace and define 5 tiers.
Tier 1 would be the entry-level tier with stringent limits as the trustworthiness of the merchants in this tier is usually very low. By default, newly signed-up merchants dominate this tier.
Tier 5, at the other end, would be the most trusted tier without any limits to the number of transactions or total transaction value a merchant could make. Usually, prominent brands and merchants with very low-risk scores are part of this tier.
There are a few key parameters that can be considered while establishing these tiers.
1. The monetary value limit for transactions a seller can make in a certain tier. This is usually dependent on the risk-absorbing capacity of the marketplace. For example, a new seller can be allowed to make transactions totaling not more than $500 in a month.
2. The total number of transactions a merchant is allowed to make limits the number of customers that may end up having bad experiences. A new seller may not be allowed to accept more than 10 transactions in a month while a proven seller can make unlimited transactions.
3. The amount of time to keep the seller in a specific tier for a certain period. Once a seller is thoroughly validated across all the checkpoints of a marketplace and builds reputation, they can be moved to the next tier with better perks.
4. The payment terms can be defined based on the trustworthiness of a tier. The lowest tier can have a higher payment period while the highest and trusted tier can have a lower payment period.
Key considerations
1. The tiers are not a one-way linear progression. At any point, any merchant is prone to move from one tier to another, both ways. A trusted merchant in Tier 5 is still at risk of falling to Tier 4 depending on their performance. This process helps a marketplace to push the merchants to maintain the highest standards.
2. Consistent monitoring and feedback mechanism needs to be implemented which would help us to quickly downgrade a merchant from one tier to another as and when risks arise. This means a merchant is continuously monitored based on reviews, feedback, and performance at any point during their lifecycle.
3. A strong but simple mechanism for merchants to raise disputes and prove genuineness.
4. A composite risk score evaluating all risk parameters would help to easily categorize each merchant into respective tiers based on their risk level.
5. Keeping an eye on dormant merchants who are building history without any activity over a period. These accounts may be suddenly revived and abused by mala fide merchants.
While we may refer to partners by different names across various digital business models, the onboarding, verification, validation, and continuous monitoring of these partners pose a huge challenge for most of these organizations.
Employing a tier-based continuous monitoring framework that would allocate a merchant into respective tiers based on their risk levels will be a logical approach to help organizations take the first step in fighting partner-driven fraud and abuse.
(The article is written by Santosh Kumar P & Anand Chandrashaker)
Anand’s team is responsible for identifying client needs and deploying transformation levers that bring together technology and business domains – through levers such as analytics, process mining, automation, and implementation of proprietary / COTS solutions. He has managed a variety of roles in the past that includes FP&A, Corporate Strategy, M&A, and post-merger Integration.
Santhosh is a professional with 14+ years of experience supporting various domains and currently develops Fraud Management & Loss Prevention solutions in the Retail, eCommerce, and Travel & Hospitality space.
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