About the Customer
eCommerce ($20m+ revenue)
Second largest mobile loyalty app provider
In North America with over 23 million customers
The Pain point
- Runs number of marketing campaigns round the year and requires dedicated data scientists/analysts to build checks to monitor and alert fraud as the campaign gets underway and prevent
- Manual intervention to go through dashboards, detect and block fraudsters
- 10 mins spent on each fraudster for validation (180+ users flagged on some days)
- Type of fraud keeps changing based on data and nature of acquisition campaigns - ads, seo, gift cards, affiliate, invitation perks
What is their starting point / ecosystem / tech stack
- Java, Python, Docker, Kubernetes
- Streamsets
- Google Cloud Platform
- MySql, BigQuery
- Third party machine learning tools - R, Python
Compelling Event (Trigger point)
- Internal initiative to drive towards high-value user acquisition
- Lack of understanding in behavioral data from user leaves with less confidence on existing fraud rule engine
- Data collection and analysis largely solved by BigQuery + Tableau
- Initial fraud rule engine largely static and manually patched for every new discovery of fraud
- Customer support Workflows for inspecting and flagging fraudsters is done in another interface ; with communication lag between the fraud rule engine and experts
Predera Benefit
- Mature end-to-end AI driven platform for fraud
- Machine Learning continuous improvement using self-learning and updation techniques
- Business stakeholders can define their own definitions of Fraud and contribute to the improving AI engine
- Customer service get direct alerts in their work environment (Slack) from the AI engine and the feedback updates the model
Evaluation
- Easy to install and setup
- Business stakeholders can teach AI for Fraud Engines in 8 hours
- Able to migrate all existing ML models and rules in less than a week
- Integrated with data stores on AWS/ GCP - no extra support needed from IT/ data teams
- Didn’t need to re-write workflow integration as it directly integrates with Slack and Hipchat for feedback from Customer support
- Dedicated expert level support for onboarding
Business Impact
- Business stakeholder -> fraud analysts -> customer support representatives loop was tightened, reducing the feedback time by 80%
- Saved 280 hours of stakeholder productivity per year
- Reduced time to resolution of Fraud by 75% from ~20 mins to ~5 mins
- Handle more fraud queues and volume with less people
ROI
“We would need two full-time data scientists with knowledge of marketing and acquisition campaigns for 6 months just to build out our Fraud Engine capability let alone the rest of monitoring and continuous automatic improvement provided by Predera. We could spend $300,000 to do that, or just buy Predera and have a mature Fraud and AI / ROI management platform today”