Reducing AML False Positives Without Replacing Systems
Discover how financial institutions can overcome data silos and high false positives in BSA compliance. Learn how a unified decision intelligence platform enables proactive network analysis to effectively disrupt complex financial crime.
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.
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 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.
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 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.
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FAQ
Do I need to replace my current transaction monitoring or case management systems to use DataWalk?
No. DataWalk is designed to work with your existing AML tools. It acts as a central intelligence hub that improves the data quality going into your systems and accelerates and improves alert triage and investigations.
How does DataWalk reduce the number of false positive alerts?
It reduces false positives in two main ways. First, before alerts are created, it cleans, connects, and adds context to your data. Better data quality means your monitoring systems generate fewer incorrect alerts. Second, after an alert is created, it automatically adds relevant information, such as whether a similar alert was previously closed as a false positive, helping analysts resolve cases faster.
What kind of data is used to create DataWalk’s contextual intelligence?
DataWalk combines your internal data (like KYC information, transactions, and account details) with external sources. You can include data such as adverse media reports and beneficial ownership information to build a complete 360° profile for each entity.
How does DataWalk help my analysts make faster decisions?
DataWalk gives analysts a complete view of all relevant information in one place. Instead of gathering data from multiple systems, an analyst can see a customer’s connected relationships, transaction history, and risk indicators visually. This allows them to quickly understand the full context of an alert and decide if it is truly suspicious.
What is a knowledge graph?
A knowledge graph is an intelligent data model that organizes information as a network of real-world entities (like people, organizations, and events) and the relationships between them. This preserves critical context that is often lost in traditional databases or spreadsheets.
What is “Composite AI” in the context of financial compliance?
Composite AI in financial compliance means combining multiple specialized AI techniques to solve complex analytical tasks that traditional tools alone can’t handle. In DataWalk, this is implemented as a two-stage, sequential pipeline:
- Graph Analytics Stage: Data from multiple sources is integrated into a unified knowledge graph. Graph analytics and inference techniques are used to resolve entities, map indirect ownership paths, and calculate risk scores. These operations run as calculated columns, virtual paths, or scheduled dependency refreshes in the core DataWalk engine — or can be executed on demand.
- Agentic AI Stage: Once the graph is computed, an Agentic AI layer uses these results to drive further analysis and reporting. This could be done through user-triggered workflows, automated scripts (for example, in a Jupyter notebook), or custom in-platform applications that take the resolved graph and risk signals as inputs and produce auditable reports, alerts, or recommendations as outputs.
By combining these tasks into a clear, repeatable pipeline, Composite AI makes advanced compliance use cases — like Ultimate Beneficial Ownership (UBO) identification — manageable, explainable, and defensible.
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