Online Fraud Detection

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Functions

Sales & Marketing

Use Cases

Fraud Detection
Predictive Quality Analytics

Services

Data Science Services

The Challenge

SendinBlue, a relationship marketing SaaS solution, faced a significant challenge in validating new customers and ensuring the quality of their databases. The company had to ensure that all contacts on the list were opted in, which required manual validation. This process was not only time-consuming and required a large workforce, but it also severely delayed account validation for customers, damaging SendinBlue’s reputation. As the customer base grew, manual validation became increasingly unfeasible. The company needed a solution that could automate the validation process and scale with the growing demand.

About The Customer

SendinBlue is a relationship marketing SaaS solution that was launched in 2012. The company's mission is to become the simplest, most reliable, and cost-effective marketing platform. SendinBlue now powers marketing campaigns for more than 50,000 companies around the world. With offices in North America, Europe, and Asia, the SendinBlue team supports the product in six languages. Their platform integrates with the top e-commerce and CMS tools, and their system delivers over 30 million emails and text messages per day.

The Solution

SendinBlue turned to Dataiku Data Science Studio (DSS) to develop an automated fraud detection system. Using historical data from over 1 billion emails and associated events, thousands of blocked accounts, and hundreds of fraud criteria, SendinBlue built a scalable solution. The new system analyzes new customers and automatically classifies them as 'good,' 'bad,' or 'uncertain.' An algorithm then determines the customer’s credibility by taking into account sending volume, the scoring of the contacts, etc. Depending on the customer’s risk score, they may be blocked, validated, or sent to customer care for manual analysis. Dataiku was instrumental in deploying the data product into production, handling large amounts of different datasets, and designing, testing, and developing the solution in less than three months.

Operational Impact

The analytics initiative saved the equivalent of a full-time job and rationalized the global validation process, allowing SendinBlue to scale its team and customer growth exponentially.
SendinBlue was able to provide a better customer experience by considerably shortening validation delays.
The machine learning model now handles most of the new accounts, initially handling just 24 percent of the new accounts as part of a staged rollout.

Quantitative Benefit

The first iteration of their model leverages Random Forest and has an 83 percent precision rate on email address classification.
The machine learning model now handles most of the new accounts, up from initially handling just 24 percent of the new accounts.