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How machine learning helps to build a modern app on AWS to combat financial fraud, founded by former bankers Whitney Anderson and Cathy Ross in 2016, addresses the increasing challenge of financial fraud in the digital-first world using modern technology. With global fraud and cybercrime costing businesses over $6 trillion annually, the founders leveraged their industry experience to create a fraud and compliance platform powered by artificial intelligence (AI) and machine learning (ML). Their solution involves pooling secure and anonymized data from various digital ecosystem players to combat fraud collaboratively. encountered high fraud rates in the digital commerce and payments sector, prompting them to bring together industry participants to share data. By doing so, they achieved a remarkable 66% reduction in fraud. To efficiently process and analyze vast amounts of data from their cross-industry consortium, opted for an event-driven architecture built on Amazon Web Services (AWS).

The cloud-native modern app utilizes AWS components such as EC2, Lambda, S3, and DynamoDB to unify and analyze customer-level, institution-level, and cross-institution data. AWS’s serverless technologies, particularly Lambda functions, play a pivotal role in’s ability to answer thousands of risk assessment questions in milliseconds, achieving a decision accuracy of 99.9%. This scalability and speed were virtually impossible a few years ago, showcasing the transformative impact of AWS on their operations.

Being 99% serverless, utilizes AWS serverless technology to make their digital transactions safe and streamlined. They leverage no-code tools for customer onboarding, enabling banks and fintechs to integrate their system within 30 days, a process that would have taken six months to a year in the past.

Machine learning, facilitated by Amazon SageMaker, is a crucial component of’s strategy, resulting in an average 80% reduction in fraud cases, a 92% reduction in false positives, and a 30% increase in approvals for legitimate customers. AWS’s pricing and technology make it cost-effective for to deploy custom ML risk models with several hundred million features as inputs, allowing them to ask complex questions about identities and behaviors.

Collaborating closely with AWS, not only provides rapid and accurate fraud prevention tools to their clients but also ensures optimal cost efficiency. The partnership with AWS teams in retail payments, financial crime, and other areas enhances’s customer onboarding experience, especially for clients transitioning from on-premises to cloud environments.

Looking ahead, aims to scale further and become the preeminent fraud and risk management layer for all digital enterprises. With clients ranging from top-tier financial institutions to early-stage fintech startups, they anticipate playing a crucial role in mitigating risks associated with the upcoming FedNow Service launch in 2023.

In summary,’s success in combating financial fraud is attributed to its innovative use of AWS technologies, particularly serverless architecture, Lambda functions, and machine learning. The collaborative approach, leveraging secure data sharing and advanced analytics, has positioned as a leader in the global fight against fraud in the digital landscape.

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