List of Amazon Fraud Detector Customers
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Since 2010, our global team of researchers has been studying Amazon Fraud Detector customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Amazon Fraud Detector for AML, Fraud and Compliance from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Amazon Fraud Detector for AML, Fraud and Compliance include: Standard Bank, a South Africa based Banking and Financial Services organisation with 51000 employees and revenues of $12.00 billion, ActiveCampaign, a United States based Professional Services organisation with 1000 employees and revenues of $150.0 million, FlightHub Group, a Canada based Leisure and Hospitality organisation with 120 employees and revenues of $120.0 million and many others.
Contact us if you need a completed and verified list of companies using Amazon Fraud Detector, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Amazon Fraud Detector customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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ActiveCampaign | Professional Services | 1000 | $150M | United States | Amazon Web Services (AWS) | Amazon Fraud Detector | AML, Fraud and Compliance | 2020 | n/a |
In 2020, ActiveCampaign deployed Amazon Fraud Detector to identify and block fraudulent account sign-ups and phishing-driven account abuses within its customer onboarding and identity processes. The Amazon Fraud Detector deployment is described in the Fraud Detection Apps Category and was initiated to address a spike in phishing observed in Q1 and Q2 2020 across ActiveCampaign's global operations, with program governance managed from the United States headquarters.
Implementation centered on building and deploying machine learning models and real-time scoring endpoints directly into the onboarding pipeline. The implemented models were tuned to flag malicious sign-ups with a low false positive rate, and configuration work included detection rules, scoring thresholds and automated scoring to minimize manual review and operational overhead.
Amazon Fraud Detector was integrated into existing onboarding workflows so flagged events could trigger automated blocking actions or routing to fraud review queues, maintaining continuity with ActiveCampaign's identity processes. The operational scope covered global account creation flows and phishing response procedures, with monitoring and incident coordination conducted from the US headquarters.
Rollout was executed rapidly in response to the Q1 Q2 2020 phishing surge, and governance focused on lightweight operational controls, model monitoring and periodic retraining triggers to preserve accuracy. The approach emphasized minimal operational overhead while maintaining low false positives for Amazon Fraud Detector in production.
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FlightHub Group | Leisure and Hospitality | 120 | $120M | Canada | Amazon Web Services (AWS) | Amazon Fraud Detector | AML, Fraud and Compliance | 2021 | n/a |
In 2021, FlightHub Group implemented Amazon Fraud Detector and moved the solution into production in February 2021 to reduce checkout fraud and distinguish genuine low-price-seeking travelers from fraudsters using stolen cards in its ecommerce and payments flows. The deployment used Amazon Fraud Detector as part of the Fraud Detection Apps Category and targeted online booking checkout workflows within FlightHub Group's Canadian ecommerce operations.
The implementation emphasized real time risk scoring and rule based decisioning, with custom model configurations and score thresholds tuned to separate high risk transactions from legitimate low price seekers. Automated decision rules were executed at payment authorization to allow dynamic accept, review, or reject actions, and model scoring was instrumented to support continuous tuning and operational monitoring.
The Amazon Fraud Detector instance ran on Amazon Web Services and integrated directly into the checkout and payment authorization pipeline, delivering fraud scores to the payment gateway and order orchestration components. Governance included the February 2021 production rollout, ongoing policy tuning and monitoring of false positive rates. FlightHub Group reported that abort and checkout abandonment rates fell from about 5 percent to below 2 percent, and that the deployment produced the lowest chargeback rates the company has seen.
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Standard Bank | Banking and Financial Services | 51000 | $12.0B | South Africa | Amazon Web Services (AWS) | Amazon Fraud Detector | AML, Fraud and Compliance | 2022 | n/a |
In 2022, Standard Bank Insurance deployed Amazon Fraud Detector as part of a Fraud detection implementation to automate and accelerate funeral and insurance claims screening in South Africa. The deployment targeted claims intake and adjudication workflows within Standard Bank Insurance, focusing on reducing manual checks and improving payout speed for low risk claims, with production results discussed in an AWS blog.
The implementation of Amazon Fraud Detector included configuration of probabilistic risk scoring and automated decisioning logic to classify claims into risk tiers. Standard Bank used real time scoring and custom rule sets to separate low risk claims for accelerated processing from higher risk claims requiring manual investigation, leveraging model hosting and score thresholding consistent with typical Fraud detection capabilities.
Operationally the solution was integrated into the claims processing workflow to enable automated routing of low risk cases to payout systems and exception routing of higher risk claims to claims operations teams. The scope was Standard Bank Insurance claims operations in South Africa, affecting claims adjudication, customer service, and payout processing, and enabling reviewers to focus on complex cases rather than routine checks.
Governance practices established around the deployment included monitoring of score thresholds, audit trails for decisioning, and periodic model review to maintain detection quality. Production outcomes included a reduction in turnaround for low risk claims from 48 hours to under 6 hours, and an increase in Net Promoter Score of approximately 36 percent between February and August 2022, while also reducing manual screening effort and accelerating payouts.
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