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RTO Prediction: Advanced Analytics Preventing eCommerce Businesses from Frauds

The eCommerce market has seen tremendous growth in the past few years, especially after COVID-19 outbreak. According to a report by Statista, the Indian eCommerce market grew from US$ 21.9 billion in 2018 to US$ 84 billion in 2021. The report also states that the market shall generate revenue of US$ 200 billion by the end of 2027. 

However, since its inception, the eCommerce industry has been susceptible to multiple frauds. The industry has to deal with malicious activities at both ends, including sellers and buyers. There have been several instances when the payments are not made by the customers, resulting in heavy loss to the business. Irrespective of the policies and systems developed for fraud detection, the hoaxers are finding new ways to strike eCommerce companies.  

eCommerce companies are implementing advanced technologies such as machine learning, artificial intelligence, and data analytics to prevent fraud at the business end. However, before understanding how these technologies avoid the losses due to the cons, let’s figure out what eCommerce frauds are and what types of online scams an eCommerce business has to deal with.

What are eCommerce Frauds?

A deception during a monetary transaction done by either the buyer or sellers over the internet for personal or financial gain is considered eCommerce fraud. These frauds committed at the sellers’ or buyers’ end negatively impact the business and operations of the brands, further deteriorating the company’s market value.

Types of Frauds eCommerce Businesses Face?

types of eCommerce frauds

The business of an eCommerce company relies on the order pickups, deliveries, and post-purchase experience provided to the end customers. All these services require payment transactions. It is here where the tricksters commit the acts and fraud the company. Let’s look at the ways the fraudsters trick an eCommerce business.

Card Testing Fraud

A card testing fraud is when someone acquires access to one or more stolen credit card numbers by stealing them or purchasing them on the dark web. Though they have credit card numbers, they are unaware of the following things:

  1. Whether the card numbers are valid for a successful transaction?
  2. What is the limit of the credit card?

To test the scenarios mentioned above, fraudsters visit eCommerce websites to make a test purchase. They often use scripts or bots to test multiple credit cards swiftly. The goal of these purchases is to test the validity of the card. Once the credibility of the card is established, the fraudsters start making larger purchases. Since these initial purchases are petite, they are pretty difficult to detect. Nevertheless, when the eCommerce companies discover these frauds, the products are already shipped to the location involving the shipping cost and, in the case of RTOs, the reverse logistics cost.

Friendly Frauds

Friendly frauds or chargeback frauds are when a customer purchases an item or services online and then requests refunds from the payment processor, claiming the transaction was invalid. The card companies must return the transactional value to the customer, which the retailers pay. These types of frauds are used to get the purchased items for free. Therefore the eCommerce owners need to implement a robust fraud detection tool that can help them identify such transactions and prevent the sellers from experiencing losses.

Interception Fraud

Interception fraud is when the fraudsters provide the shipping and billing address matching the address mentioned in the stolen card. Once the order is placed, the tricksters intercept the delivery partner by either contacting customer care or changing the shipping address tracking the location of the delivery representative.

Such frauds can be stopped by analyzing the delivery addresses with the help of RTO prediction tools. The tools’ advanced data analytics can identify the defaulter addresses and flag them as bad addresses, which alerts the eCommerce businesses to take preventive measures to stop RTO and loss of money.

eCommerce businesses generally have to deal with one more type of fraud. However, this can not be considered an actual fraud, but it results in more significant loss to the companies, especially to the small and medium-scale businesses. Let’s look at a scenario and try understanding this “fraud.”

RTO Encouraging Scenario

A customer wishes to purchase a product online. After analyzing all the platforms where the product is available, the customer makes the purchase and opts for COD as a payment option.

Choosing COD as the payment method frees the customer from any payment commitments before receiving the order. Leveraging this, the buyer orders a product from multiple eCommerce platforms but only accepts the one that arrives first and returns all the other orders.

Though this scenario seems right from the buyer’s perspective, the seller must bear the forward and reverse logistics costs. And, if the same seller lists the ordered product on various platforms, the loss increases.

How RTO Prediction Prevents Brands from Frauds

RTO prediction tools use advanced data analytics and machine learning to analyze past data to keep track of shoppers’ payment and  buying patterns. The technologies allow the RTO prediction platforms to process and sort the defaulter addresses while presenting the historical RTO percentages  for each buyer. Additionally, RTO prediction platforms analyze historical addresses where deliveries have failed in the past and build this into a meaningful insight that prompts the seller to either correct the address by adding a precise location/landmark or cancel the order altogether.

Using the historical order data and machine learning, a RTO prediction platform not only allocates the risk associated with each order but is also capable of suggesting corrective actions that can reduce the risk.

How Pickrr Predict Helps the Sellers Prevent RTO Loss

Pickrr Predict Helps

The solution provides options to its sellers to segregate orders by the risk associated with them and further provide options to  edit, approve, and cancel the order that helps in significantly reducing the seller’s RTO rates. 

Risk attributed to an order can either be because of the buyer address, buyer historical RTO profile, buyer demographics and/or the product characteristics like invoice value, SKU etc.

Now the seller can connect with the customer and convert the COD orders to prepaid. This will reduce the risk of payment fraud by the customers of the eCommerce brands.

Secondly, the options of edit, or cancel can help the sellers approve or reject the order to save both forward and reverse logistics. A seller might leverage various other benefits by using Pickrr Predict for RTO prediction. Let’s look at some of these benefits:

Calculate the Customer’s RTO Score

Utilizing the data collected within seven years, Pickrr Predict analyzes the reasons that resulted in products getting RTO by the end customers. The tech-based tool identifies the causes such as late delivery, faulty products, unavailable to accept the delivery, or wrong address and calculates the net RTO score of the COD customers. The RTO score of the customer can help the sellers to manage the payment options before shipping the orders, which further reduces the RTO rate for the sellers.

Identify Incorrect/Invalid Contact Information

One of the significant reasons for the order being RTO is incorrect or invalid contact information. Pickrr Predict reviews the pattern of contact information inputs from a particular buyer and analyzes whether the inputs are genuine typographical errors or intentional. The AI model of the RTO prediction tool highlights the invalid contact information provided intentionally by the end customer. Identifying such customers allows the eCommerce sellers to communicate with the customers via other media and ensure the delivery is done on time while reducing the RTOs.

Highlighting Bad Address

Bad addresses are those areas where malicious activities are quite frequent. Such regions are infamous as the delivery representatives are either ambushed, looted, or are the victims of armed conflicts. Using advanced analytics, Pickrr Predict can recognize such addresses and allow the sellers to ask the customers to either change the location or cancel the order.

Conclusion

Where card testing, friendly, and interception frauds are quite frequent in the eCommerce industry, RTO predictions are among the most helpful tools for business owners. The projections can help the sellers to identify the risky address and determine whether to deliver the product to the address or not. Additionally, RTO prediction tools such as Pickrr Predict provide necessary information about the customer’s buying pattern, which can help the sellers take the actions required to prevent the RTO. To know more about the advanced features of Pickrr Predict, click here.

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