Strise Data Position Paper

Setting the Standard for Ethical Data Practices in AML

Executive Summary

Strise turns fragmented AML data into actionable insight through SuperData, the data and entity-resolution layer of the platform. At the core of our approach is a commitment to ethical data practices, regulatory compliance, and transparent vendor evaluation and data-quality assurance.

Introduction

Tackling AML’s Data Challenges

In AML, the problem is twofold: fragmented datasets and inefficient workflows. Strise addresses these challenges with advanced graph models, transparent use of AI, and robust vendor assessments.

What sets Strise apart

Strise’s architecture performs data ingestion and processing before AML case handlers conduct due diligence. All customers subscribe to the same source of truth in a multi-tenant cloud environment.

Advantages of Pre-processing

Dynamic Ownership Changes: Automatic screening when ownership changes occur
Conflict Resolution: Identifies ownership conflicts between manual edits and registry updates
Data Integrity: Always retains the original version of data

Advantages of Multi-tenant Architecture

Cost Efficiency: Shared infrastructure lowers costs
Seamless Updates: Real-time updates for all customers
Scalability: Grows with customer needs
Collaborative Insights: Aggregated, anonymized improvements
Streamlined Integration: Consistent API and data models across tenants
Enhanced Security: Strict data segregation, encryption, GDPR and AMLD6 compliance

Data Philosophy

Ethical Data Use: Built on publicly available data
Quality first: Trusted data providers with continuous evaluation
Privacy by Design: Privacy embedded at every stage
Transparent AI: Clear insight into AI decision-making

How Strise Uses Data

Strise uses graph models to represent the complex web of relationships in the business world. Our 7-step process (steps 2-5 happen before due diligence):

1. Sourcing data

Local Registries: Prioritised for precision via intermediaries like Creditsafe
Global Sources: Partnerships with Sayari Labs for hard-to-access jurisdictions
Regulatory Lists: Sanctions and PEP coverage via Trapets and Dow Jones
Dynamic Updates: All sources updated daily

2. Automatic cleaning and deduplication

Triangulation: Cross-referencing multiple sources
Metadata Utilization: Birth dates, addresses for validation
User-Friendly Deduplication Tooling: Intuitive interface for manual review

3. Manual cleaning and deduplication

Dynamic Linking: “Connect owner” button for entities with limited information
Streamlined Search: Results filtered by country and entity type
Flexible Merging: Metadata-supported confident merging

4. Control calculations

Ownership in Strise refers to relationships where entities own parts of other entities, including indirect ownership through multiple layers.

UBOs vs ABOs:

UBOs: Physical persons assumed to control an entity through ownership, family connections, or agreements
ABOs: Physical persons with less confidence in their beneficial owner status

Country-specific guidelines are available for Norway, Denmark, Sweden, Finland, and the UK.

5. Manual editing of data

Users can edit existing owners, delete owners or roles, and add new owners or roles. All changes are instantly reflected across the platform and tagged with the user who made them.

6. Screening

Customisable Fuzzy Matching: Adjustable thresholds for team-wide screening precision
Sanctions Screening: Consolidated data from OFAC, OFAC SDN, UK, EU, and UN
PEP and RCA Screening: Data from Trapets and Dow Jones
Adverse Media Screening (AMS): Proprietary NLP-based solution processing millions of articles daily

7. Monitoring

Continuous Customer Monitoring: Automatic flagging of changes
Continuous Risk Updates: Automated risk assessments triggered by data changes
Customisable Automated Checks: No-code rule setup
Global PEP and Sanction Screening: Continuous, automated compliance

Vendor Evaluation

Annual Reassessments: GDPR, security, data quality evaluations
Benchmarking: Regular comparisons with competitors
Certifications: ISO 27001 and SOC 2 Type II certified vendors

Disclaimer on Data Quality

Data quality depends on third-party providers and public sources. Coverage varies between countries. Strise continuously works to improve data quality through partnerships and technology.

Commitments and Future Vision

Strise is committed to ethical, transparent, and secure data practices. We will continue to invest in data quality, vendor evaluation, and AI transparency to ensure our customers have the best possible tools for AML compliance.