The coronavirus outbreak is devastating much of commerce in China, sending ripples across global financial markets. One bright spot has been the e-commerce industry, where food, medicine, and pretty much anything can be ordered online and delivered to people’s doorsteps, allowing people to avoid potentially risky exposures to commutable diseases in crowded social locations.
The proliferation of online shopping habits over the past decade has enabled the build-out of some incredible logistics networks. However, the purchase friction of “what if I don’t like it?” still causes many business opportunities to be missed.
One relatively recent innovation that removes some of this friction is an insurance product called return-shipping insurance.
During an e-commerce online checkout, customers can choose to spend $1 to $2 to insure for some return-shipping credit. The insurance premium is rolled into the shopping cart so no separate payment is necessary. If a customer is not satisfied with the product purchase, he/she can just return the product within seven days, and return shipping credit will be automatically processed within 72 hours, without going through any cumbersome claim process.
This insurance product is hugely popular in e-commerce in China. ZhongAn Online P&C Insurance Co. Ltd. (6060:HK), A pioneer in online-only insurance, has issued more than 5.8 billion insurance policies to date, most of which are return-shipping insurance.
Opportunities and Caveats
As with any opportunities, there are always caveats.
Insurance fraud can be rampant in an environment where anyone can register as an online merchant, and anyone can sign up to be an e-commerce customer. When someone can pay $1-2 to get $10-20 of credit over the Internet, there is a significant incentive to find schemes to attack the insurance business model. Early trials of return-shipping insurance at one e-commerce company were reported to have claim rates as high as 90%, caused by significantly high levels of fraud, which obviously does not make a sustainable insurance business model.
Last time we discussed mapping scenarios to known optimization techniques, the first part of Technology in the TED-ERA fan, using an example of driving engagement conversion.
This time we will continue discussing Technology in TED-ERA, focusing on practices in using data to train models to discover patterns.
How to Discover Patterns in Data to Fight Fraud?
For discovering patterns, we believe we must be vigilant in understanding data characteristics, be innovative in feature engineering, and have clarity in modeling strategy when fighting fraud using e-commerce data.
E-commerce data contains a rich range of granularity over time, entity, and interactions for catching fraud. Over the time horizon alone, as a customer is shopping for products, analysis granularities could include (but not limited to):
- the engagement with a product recommendation
- the exploratory actions within a shopping session
- a purchase decision with engagement across multiple sessions
- multiple purchases with an intent (such as Christmas, Superbowl, Valentine’s day, back-to-school)
- purchases in a life-stage, such as graduation, getting married, or the birth of a child
For each transaction, the entities involved include the customer, the merchant, the payment processor, and often the shippers for the physical goods.
There is also a rich set of interactions between these entities for each transaction, with interaction contexts and patterns across multiple transactions.
Understanding the level of granularity we choose to assess return-shipping insurance fraud is crucial for building an effective modeling strategy. However, the choice may not be so obvious.
The fraudulent intent could be coming from one or more of the following parties:
- the customers shopping for products
- the shippers
- a merchant corroborating with customers
- a hacker with access to multiple stolen identities on an e-commerce platform
- (or even) dark web players incubating merchant / customer identities for sale
Vigilance in Understanding Data Characteristics
One way to cut through the complexity is to look at the unit of decisioning. In the return-shipping insurance fraud scenario, this can be at the granularity where we can decide whether to offer the return-shipping insurance in the checkout process on the e-commerce platform. Depending on the platform’s product design choices, unit of decisioning could be at the level of an individual e-commerce transaction level, a customer level, or a merchant level. Information from the other levels of granularity can be incorporated as features in the model.
As with any evolving e-commerce business, biases in operations data can be introduced as part of business expansion, marketing campaigns, or bots/crawlers for a website. These biases can impact data characteristics such as sample size, data sparsity, and outliers. These data characteristics are crucial to assess and understand before constructing features, as common statistical metrics may often misinterpret the situation.
For anti-fraud models specifically, fraud cases are often a small portion of the overall transaction volume. Sample imbalance issues are common when training fraud models. Assess the degree of imbalance and choose more robust modeling techniques are crucial to modeling success. Common techniques include random over-sampling or under-sampling, informed under-sampling, synthetics sampling with data generation, and adaptive synthetic sampling.
The e-commerce scenario also includes a broad set of data types, including:
Individual samples such as articles, images, and videos on merchandise
Time-sequenced data such as browsing history, transaction history, customer service interaction history
Relationship-based data such as interactions between customers, merchants, payment processors, and shippers
Different data types call for different analysis infrastructures and can offer unique features and perspectives in modeling. For example, relationship-based data such as interactions between customers, merchants, payment processors, and shippers can be used to generate knowledge graphs that capture these relationships. Knowledge graph based approach has been shown to be effective in highlighting behaviors in organized financial fraud.
In summary, we discussed the first consideration of using data to train models to discover patterns: understanding data characteristics. This part includes the following practices:
- Understanding unit of decisioning
- Being aware of sample size, sparsity, and outliers
- Assessing sample distribution/imbalance
- Interpreting data types
Figure 5.1 Using Data to Train Models to Discover Patterns - Technology in TED-ERA fan
In upcoming blogs, we will be discussing other considerations in using data to train models to discover patterns, specifically innovating in feature engineering and clarifying modeling strategy.
If you have views, questions, or examples you would like to share, feel free to comment below or contact us directly.
About the authors:
Dr. Jike Chong is a director of data science at LinkedIn, leading a team to develop products to advance people’s careers in the context of the hiring marketplace. He previously built and grew several high-performing data science teams at Acorns, Yirendai (NYSE:YRD), and Simply Hired. Dr Chong also advises several startups and teaches at Tsinghua University.
Yue Cathy Chang is an executive leader in business development, sales and marketing for data products and services. Cathy has held strategic roles serving customers using data science- and engineering-based solutions at bluechip software enterprise companies and startups. Well-versed in tech and business, she has frequently spoken at data and AI events.
The writing and opinions expressed are solely our own and are not shared, supported, or endorsed in any manner by our employers.
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