Overview
Payment providers and financial institutions process millions of transactions daily. Legacy rule-based fraud systems generate large volumes of false positives — declining legitimate transactions and flooding compliance teams with low-value alerts. At the same time, skilled risk analysts spend a significant portion of their day manually pulling data from siloed systems to compile Suspicious Activity Reports and AML documentation.
SUDO builds Agentic Risk Operations on AWS that combines predictive machine learning with Generative AI agents. The platform does not just flag risk — it autonomously investigates it, delivering a complete investigation dossier to the compliance officer for review and final determination.
This dual approach protects the customer experience while reducing the operational cost of compliance at scale.
Challenge
The Cost of Legacy Risk Systems
These challenges increase customer friction, strain compliance teams, and expose institutions to regulatory and financial risk.
Financial organizations often face:
1
Static fraud rules that decline legitimate transactions, creating customer friction and driving card abandonment
2
Compliance analysts spending significant time manually pulling CRM, KYC, and banking data to build context on a single flagged case
3
Traditional relational databases that cannot detect complex, multi-hop money laundering rings using synthetic identities and shell accounts
4
Manual, error-prone production of audit documentation, PCI-DSS narratives, and Suspicious Activity Reports that scale poorly as transaction volumes grow
5
Batch detection processes that introduce delays between fraudulent activity and investigation, increasing financial exposure
Solution
An Agentic Risk Operations Platform on AWS
SUDO deploys a dual-layer risk platform separating real-time transaction scoring from autonomous investigation orchestration.
Amazon Kinesis
Streams transaction telemetry including amount, location, and device ID from all channels in real time before settlement occurs.
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Amazon Fraud Detector
Replaces rigid rules with custom machine learning models trained on historical fraud data, scoring every transaction in milliseconds to detect account takeovers and card fraud without adding checkout latency.
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Amazon Neptune
A high-performance graph database that maps complex account relationships — such as multiple accounts sharing the same device or network — enabling detection of organized AML rings that linear rule-based systems cannot identify.
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Amazon Bedrock
When Fraud Detector or Neptune flags a severe anomaly, Bedrock agents autonomously query internal APIs to gather KYC data, transaction history, and device logs, synthesizing all findings into a plain-language investigation dossier for the compliance officer.
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AWS CloudTrail
Maintains an immutable audit log of every AI decision, data query, and human intervention, ensuring complete audit readiness for regulatory review.
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Key Capabilities
Real-Time Contextual Fraud Scoring
Machine learning models score every transaction before settlement, dynamically adapting to shifting user behavior without blocking legitimate transactions.
Graph-Powered AML Detection
Amazon Neptune maps hidden connections between users, devices, and accounts to uncover synthetic identity schemes and multi-hop money laundering networks.
Autonomous Investigation Dossiers
Compliance officers open a flagged case to find a complete, AI-generated summary of why the transaction was flagged and a full risk profile of the account — eliminating the manual data hunt.
Human-in-the-Loop Regulatory Reporting
The AI pre-drafts Suspicious Activity Reports, audit narratives, and compliance documentation. Human officers review, edit, and approve before submission.
Immutable Audit Governance
Every AI action and human decision is logged in AWS CloudTrail, ensuring the organization is continuously prepared for PCI-DSS, ISO 27001, and AML regulatory audits.
Business Impact
Financial institutions benefit from:
- Significant reduction in false positive rates, preserving legitimate transaction volume and reducing customer friction
- Faster case resolution through AI-generated dossiers that eliminate manual data gathering for compliance analysts
- Reduced regulatory reporting overhead through automated drafting of SARs and compliance narratives
- Continuous audit readiness through immutable logging and standardized AI-generated reporting
- Stronger AML detection capability through graph-based relationship analysis that rule-based systems cannot replicate
By orchestrating predictive ML with agentic AI, financial institutions move from reactive compliance to efficient, continuously operating risk management.
