Turning E-Commerce Data into Actionable Fraud Intelligence
We delivered an AWS-based fraud monitoring solution that transforms Shopify order data into actionable risk scores, dashboards and targeted alerts for operational teams.
Our Customer’s Challenge
A supermarket retailer needed a more effective way to identify potentially fraudulent activity across its Shopify e-commerce operation. Although order and customer data were already being captured, fraud investigation remained dependent on manually reviewing transactions and interpreting individual warning signs.
Fraudulent behaviour rarely presents through a single obvious indicator. Suspicious transactions might involve unusually high order values, repeated purchases within a short period, mismatched customer and delivery information, multiple names associated with the same account, non-standard addresses or purchases involving products considered particularly vulnerable to fraud.
The retailer therefore required a solution that could consolidate these signals, assess each order consistently and give operational teams a clear view of where intervention might be required. The solution also needed to avoid overwhelming users with low-value notifications, route information to the correct store team and remain configurable as fraud patterns and business priorities evolved.
Our solution
We designed and delivered an AWS-native fraud monitoring and alerting solution integrated with the retailer’s existing Shopify data pipeline.
The solution introduced a configurable fraud-scoring model that evaluates each order against a series of behavioural and transactional indicators. These included:
Unusually high transaction values or purchasing frequency.
Sudden changes from established customer behaviour.
Billing, delivery, name or address inconsistencies.
Multiple customer identities associated with the same details.
Non-standard order or delivery addresses.
Transactions involving flagged events, products or collections.
Rather than treating each rule as definitive evidence of fraud, the solution combines the indicators into a composite fraud score. Configurable thresholds determine which transactions require attention, enabling the retailer to balance fraud detection against the operational cost of reviewing false positives.
We built an Amazon QuickSight dashboard to provide a consolidated view of risk across customers and transactions, including high-risk orders, fraud-score distribution, purchasing trends and the individual indicators contributing to each score. Filters and drill-down capabilities allow users to move from portfolio-level trends to the underlying order information required for investigation.
To make the solution operational rather than purely analytical, we also implemented automated processing through AWS Glue and location-specific notifications through Amazon SNS. Alerts are routed to the appropriate operational team based on the order location, with unknown or unsupported locations suppressed to prevent misdirected notifications. Alert content was subsequently refined to make the information easier for recipients to interpret and act upon. The completed work included resolving authorisation issues, improving the usability of fraud emails and preparing the service for direct subscription by the retailer’s nominated users.
The Results
- Improved visibility: Operational teams gained a centralised view of fraud risk across e-commerce transactions rather than relying on fragmented data and manual interpretation.
- Earlier intervention: High-risk orders can be surfaced automatically when they cross the agreed fraud-score threshold, allowing teams to investigate before fulfilment or financial exposure progresses.
- Consistent decision-making: Each transaction is evaluated against the same defined rules, reducing the subjectivity and variability inherent in manual fraud reviews.
- Actionable intelligence: Alerts identify the factors contributing to the fraud score, giving recipients the context needed to make informed decisions rather than presenting an unexplained risk rating.
- Reduced operational noise: Composite scoring and configurable thresholds prevent isolated, low-risk anomalies from generating unnecessary notifications.
- Scalable fraud controls: The rule-based architecture allows indicators, thresholds, flagged products and routing logic to be adjusted as purchasing behaviour, fraud tactics and business requirements change.
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