Robinhood
Financial crime investigation
Analysts manually reviewed mountains of data for every alert. Now, AI agents summarize and verify facts, boosting efficiency by ~20%.
- ~20% investigative workflow efficiency gain
Fraud signals buried in unstructured bank data. Generative AI now categorizes each account before models score 500 transactions per second.
A global pay-by-bank payment processor with 110 million customers across 30+ countries, authorizing or declining more than 500 transactions per second in real time.
The previous system wasn't built to analyze transaction data at the volume and speed required for real-time authorizations. The data science team had...
“Using Amazon SageMaker, we manage the whole lifecycle for our ML models. With AI technology on Amazon Bedrock, we’re categorizing bank account activity to help the models be more accurate.”
Account-to-account payment network for instant bank transfers and open banking.
Cloud computing platform and on-demand infrastructure services.
Trustly's Fraud detection is part of this use case:
Related implementations across industries and use cases
Analysts manually reviewed mountains of data for every alert. Now, AI agents summarize and verify facts, boosting efficiency by ~20%.
Privacy rules bottlenecked AI scaling. A secure internal platform now cuts AML investigation time by days and security resolution by 50%.
Securing 12,000 servers overwhelmed legacy tools. Now, analysts use GenAI to investigate and remediate threats using natural language.
Analyzing 200-page reports took senior reps hours. Now, AI extracts key insights, empowering any sales rep to build tailored client decks.
Escalations from 2.5M daily transactions bottlenecked teams. Multi-agent AI now dynamically routes and resolves complex payment issues.
Bond selection took days of manual review. Custom AI agents now surface risks, empowering analysts to execute trades in hours.
Routine administrative tasks once tied up human experts for entire days. AI now completes these workflows in just minutes.
Fraud signals buried in unstructured bank data. Generative AI now categorizes each account before models score 500 transactions per second.
A global pay-by-bank payment processor with 110 million customers across 30+ countries, authorizing or declining more than 500 transactions per second in real time.
The previous system wasn't built to analyze transaction data at the volume and speed required for real-time authorizations. The data science team had...
“Using Amazon SageMaker, we manage the whole lifecycle for our ML models. With AI technology on Amazon Bedrock, we’re categorizing bank account activity to help the models be more accurate.”
Account-to-account payment network for instant bank transfers and open banking.
Cloud computing platform and on-demand infrastructure services.
Trustly's Fraud detection is part of this use case:
Related implementations across industries and use cases
Analysts manually reviewed mountains of data for every alert. Now, AI agents summarize and verify facts, boosting efficiency by ~20%.
Privacy rules bottlenecked AI scaling. A secure internal platform now cuts AML investigation time by days and security resolution by 50%.
Securing 12,000 servers overwhelmed legacy tools. Now, analysts use GenAI to investigate and remediate threats using natural language.
Analyzing 200-page reports took senior reps hours. Now, AI extracts key insights, empowering any sales rep to build tailored client decks.
Escalations from 2.5M daily transactions bottlenecked teams. Multi-agent AI now dynamically routes and resolves complex payment issues.
Bond selection took days of manual review. Custom AI agents now surface risks, empowering analysts to execute trades in hours.
Routine administrative tasks once tied up human experts for entire days. AI now completes these workflows in just minutes.