Reducing AML False Positives Without Replacing Systems
Improve Data, Alert, and Investigation Quality With DataWalk’s Contextual Intelligence
Executive Summary:
Banks continue to grapple with a persistent challenge: managing the overwhelming volume of AML alerts, most of which ultimately prove to be false positives.
By transforming fragmented data into actionable intelligence, DataWalk enables financial institutions to drastically reduce false positives and improve true positive detection — all without replacing existing monitoring, screening or case management systems.
DataWalk acts as a central AML intelligence hub that complements and enhances current detection and investigation environments through two key scenarios:
- Reducing false positives before they occur through better data quality and entity context
- Accelerating and improving alert triage and investigations with enriched, connected insights
With this overview in mind, let’s explore how these capabilities come to life — along with practical examples and illustrations of DataWalk’s technology.
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1. Pre-Alert Enhancement: Turning Siloed Data into Contextual Intelligence
Before an alert is even generated, DataWalk enriches and refines customer and transaction data to strengthen the accuracy of your detection systems, addressing the foundational problems of data silos and quality that drive false positives.
Figure 1: Example of a bank’s connected & contextualized intelligence foundation for AML operations
How it works
- Data Unification & Context: Data is contextually organized into a knowledge graph centered around business objects (e.g., people, accounts, transactions, etc.). This combines internal and external data (e.g., KYC, adverse media, beneficial ownership, and anything else you want) to create a 360° contextual profile for each entity.
- Entity Resolution: DataWalk uses advanced algorithms and graph calculations to automatically resolve duplicates and link related entities across siloed systems (e.g., matching variations of names, addresses, or identifiers) eliminating false positives caused by data quality issues and lack of context.
- Relationship and Advanced Network Analysis: DataWalk's graph foundation identifies hidden links among customers, counterparties, and high-risk entities to uncover indirect exposure and network risk.
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Why it matters
The principle is simple: Quality in = Quality out. With richer, cleaner, and contextualized data feeding your transaction monitoring and screening systems, fewer irrelevant alerts are triggered—while the alerts that are generated carry greater precision and meaning.
Illustrative Examples
Example 1 (Entity Resolution): Customer A and Customer B share similar names, the same address and the same phone number. DataWalk identifies them as the same person, merging their profiles. Since Customer A is classified as low risk, Customer B is as well—eliminating a potential false positive before it reaches an analyst.
Example 1 Pre-Alert: Matching entities with DataWalk
Example 2 (Advanced Network Analysis): John Smith regularly transfers money to Counter-party A, who accesses the system through IP address X. DataWalk identifies that this same IP address is also used by Customer D, an individual on the institution’s blacklist. The relationship network reveals indirect exposure, prompting reassessment of both D and C’s risk profiles and preventing a false negative.
Example 2 Pre-Alert: Identifying hidden risks in your AML data with advanced network analysis
2. Post-Alert Optimization: Contextual Intelligence for Faster, Smarter Triage
Once alerts are generated, analysts still face the challenge of sorting false positives from truly suspicious cases.
DataWalk’s contextual analytics empower Level 1 AML investigators to make dramatically faster, more informed decisions by leveraging the context of connections.
How it works
- Contextual Alert Enrichment and Triage: Automatically augment alert data with related customer, transaction, and external data sources. This includes analyzing historical alert context, such as verifying if a similar alert for the same counterparty was previously closed as a false positive.
- Advanced Entity Resolution: Merge duplicate alerts referring to the same individual or entity based on the underlying graph network structure, reducing redundant workload for analysts.
- Anomaly and Relationship Detection: DataWalk applies sophisticated graph algorithms (like shortest path and community analysis) to surface non-obvious links, such as finding the shortest connection path between a customer and a blacklisted entity or identifying a suspicious ring structure of related accounts. These insights are then used to flag the case accurately.
- Visual Investigation and Decision Support: Analysts can visualize relationships, behaviors, and risk indicators in a single knowledge graph environment—drastically accelerating triage and providing full transparency for how entities were matched and risk was determined.
Results
By applying contextual intelligence at the point of investigation, DataWalk enables:
- Substantial reduction in false positives by integrating past investigation outcomes.
- Accelerated investigation times.
- Improved identification of true financial crime risk.
Illustrative Examples
Example 1 (Integrating Past Context): An alert is triggered for a high-risk customer transacting with a counterparty on a sanctions list. DataWalk's contextual analysis immediately reveals that the same previous alert for this specific customer/counterparty pair was already closed as a false positive. The new alert is automatically closed or marked as low-risk, eliminating a recurrent false positive.
Example 1 Post-Alert: Contextual Analysis of connected AML alerts identifies a false positive
Example 2 (Network Risk using Community Analysis): A customer initially assessed as low risk triggers an alert. Rather than having the case closed as a false positive, DataWalk automatically applies community-analysis to map the customer into a broader relationship network. Within seconds, the algorithm uncovers that the customer shares personal identifiers—such as a device and residential address—with multiple counterparties. One of these linked individuals has prior SARs filed.
Example 2 Post-Alert: Advanced network analysis reveals a false negative
Example 1 Post-Alert: Contextual Analysis of connected AML alerts identifies a false positive
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Markus Hartmann is an expert in designing integrated compliance solutions that enhance existing Anti-Money Laundering (AML) infrastructure. He specializes in leveraging data unification and contextual intelligence to improve alert quality and accelerate complex financial crime investigations.
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