Build Better Fraud Detection Without Customer Data.
Your data science teams need to build and validate fraud detection models, transaction monitoring algorithms, and risk scoring systems, but development requires customer transaction data you cannot access in non-production environments.
Building predictive models for card fraud, wire transfer monitoring, or account takeover detection demands realistic transaction patterns, yet you can't use actual customer data for model training and testing. Your models lack testing on edge cases, leading to high false positive rates that overwhelm investigators and high false negative rates that let fraud slip through.
- Faster Model Development: Build and test fraud detection tools immediately without waiting for data partnerships or privacy approvals, reducing development cycles
- Higher Model Quality: Train on diverse fraud typologies and edge cases that may not exist in your current customer base, improving detection rates before deployment
- Reduced False Positives: Validate model performance against known ground truth, tuning thresholds to balance detection and investigation workload before impacting operations
Prove Your Compliance Controls Work, Without Client Data.
Your institution faces regular regulatory examinations from the Fed, OCC, CFPB, or other agencies who expect to review transaction monitoring, AML controls, and customer communication practices. Examiners want to see your systems work on realistic scenarios, but you can't provide actual customer data for testing.
Internal audit teams need to validate compliance tools before examinations, yet lack the diverse, complex transaction patterns that would prove your controls are effective. Mock examinations use oversimplified data that fails to reveal gaps, leaving you exposed when real scrutiny arrives.
- Examination Readiness: Demonstrate control effectiveness to regulators using comprehensive test scenarios without customer data exposure
- Validate Controls Before Audits: Test AML, fraud detection, and transaction monitoring systems against realistic patterns, identifying gaps before examiners do
- Reduce Examination Cycle Time: Provide realistic evidence of system performance immediately, eliminating delays waiting for data sanitization or customer consent
Evaluate Fraud and AML Tools Without Compromising Client Data.
Your institution needs to evaluate fraud detection systems, AML monitoring platforms, or customer analytics tools that promise to transform operations, but vendors require transaction data and customer communications you cannot provide. Technology committees demand rigorous proof of concept before multi-million dollar investments, yet POC processes stall for months navigating data governance, privacy reviews, and risk committee approvals.
Even when you attempt to use anonymized data, the sanitization process destroys the transaction patterns and behavioral signals the tools need to demonstrate value.
- Objective Vendor Comparison: Benchmark competing solutions against datasets with ground truth labels, making data-driven purchasing decisions instead of relying on vendor claims
- Eliminate Procurement Delays: Launch vendor evaluations immediately without privacy reviews or customer consent, reducing evaluation cycles from year to months
- Better Technology Choices that Reduce Risk: Test tools on complex, realistic scenarios including fraud patterns and suspicious activities, ensuring investments deliver value before deployment
Our patent-pending process uses AI agent simulations to create realistic transaction networks, customer account histories, fraud patterns, AML scenarios, wire transfers, customer communications, and regulatory filings. All with embedded scenarios designed to test fraud detection systems, validate AML controls, and evaluate financial AI tools.
Seedless' founding team brings extensive experience managing data challenges in highly-regulated environments, including leading legal and data intelligence functions at global enterprises where they navigated complex compliance requirements and AI deployment at scale. This expertise in regulated industries, combined with technical backgrounds in AI/ML simulations, uniquely positions them to address the data scarcity problems blocking financial services AI adoption and compliance validation. This isn't a generic data company adapting to financial use cases, it's fit-for-purpose technology from industry veterans who understand your challenges.
Build Better Fraud Detection Without Customer Data.
Your data science teams need to build and validate fraud detection models, transaction monitoring algorithms, and risk scoring systems, but development requires customer transaction data you cannot access in non-production environments.
Building predictive models for card fraud, wire transfer monitoring, or account takeover detection demands realistic transaction patterns, yet you can't use actual customer data for model training and testing. Your models lack testing on edge cases, leading to high false positive rates that overwhelm investigators and high false negative rates that let fraud slip through.
The Seedless Advantage that matches the format of the other bullet headlines:
- Faster Model Development: Build and test fraud detection tools immediately without waiting for data partnerships or privacy approvals, reducing development cycles
- Higher Model Quality: Train on diverse fraud typologies and edge cases that may not exist in your current customer base, improving detection rates before deployment
- Reduced False Positives: Validate model performance against known ground truth, tuning thresholds to balance detection and investigation workload before impacting operations
Prove Your Compliance Controls Work, Without Client Data.
Your institution faces regular regulatory examinations from the Fed, OCC, CFPB, or other agencies who expect to review transaction monitoring, AML controls, and customer communication practices. Examiners want to see your systems work on realistic scenarios, but you can't provide actual customer data for testing.
Internal audit teams need to validate compliance tools before examinations, yet lack the diverse, complex transaction patterns that would prove your controls are effective. Mock examinations use oversimplified data that fails to reveal gaps, leaving you exposed when real scrutiny arrives.
The Seedless Advantage that matches the format of the other bullet headlines:
- Examination Readiness: Demonstrate control effectiveness to regulators using comprehensive test scenarios without customer data exposure
- Validate Controls Before Audits: Test AML, fraud detection, and transaction monitoring systems against realistic patterns, identifying gaps before examiners do
- Reduce Examination Cycle Time: Provide realistic evidence of system performance immediately, eliminating delays waiting for data sanitization or customer consent
Evaluate Fraud and AML Tools Without Compromising Client Data.
Your institution needs to evaluate fraud detection systems, AML monitoring platforms, or customer analytics tools that promise to transform operations, but vendors require transaction data and customer communications you cannot provide. Technology committees demand rigorous proof of concept before multi-million dollar investments, yet POC processes stall for months navigating data governance, privacy reviews, and risk committee approvals.
Even when you attempt to use anonymized data, the sanitization process destroys the transaction patterns and behavioral signals the tools need to demonstrate value.
The Seedless Advantage that matches the format of the other bullet headlines:
- Objective Vendor Comparison: Benchmark competing solutions against datasets with ground truth labels, making data-driven purchasing decisions instead of relying on vendor claims
- Eliminate Procurement Delays: Launch vendor evaluations immediately without privacy reviews or customer consent, reducing evaluation cycles from year to months
- Better Technology Choices that Reduce Risk: Test tools on complex, realistic scenarios including fraud patterns and suspicious activities, ensuring investments deliver value before deployment
