Entity Resolution in Financial Crime:
A Practical Guide to Uncover Hidden Risks The High Cost of Silo’d Data

The total cost of financial crime compliance in the U.S. and Canada has reached a staggering $61 billion , with most financial institutions reporting that costs continue to rise. This is not just the cost of regulation - it is the cost of inefficiency driven by silo’d data.

For organizations combating financial crime, the greatest vulnerability is often not a sophisticated laundering scheme, but the fragmented data scattered across internal systems. When “John D. Smith,” “J.D. Smith,” and “J Smith” exist as separate entities across different databases but are actually the same individual, the result is not merely a data quality issue - it is an intelligence failure.

These blind spots directly lead to missed detections, redundant investigations, and analysts spending hours manually piecing together data instead of conducting true analysis. In today’s environment, silo’d data is more than an operational nuisance - it’s a systemic barrier to effective risk management, regulatory compliance, and organizational agility.

This guide explains why legacy data-matching tools can no longer address this complexity and introduces a modern, graph-based approach to turn fragmented data into your most powerful investigative asset.

Why Rule-Based Systems Are a Recipe for Failure

For years, organizations have relied on rule-based systems to connect disparate data. These approaches are brittle, reactive, and unable to keep pace with dynamic criminal behavior. The result: investigative teams that are overwhelmed, reactive, and blind to emerging risks.

Brittle Rules Can’t Keep Up with Evolving Threats

Legacy systems depend on static logic - for example, requiring an exact match on name and date of birth. Criminals exploit these limitations with ease, introducing small variations in names, addresses, or identifiers that break the connection between related entities. The result is an endless, unwinnable game of cat-and-mouse, where detection always trails deception.

The Unstructured Data Blind Spot

Up to 80% of investigative intelligence resides in unstructured sources - notes, SARs, emails, social media (?) or narrative reports - yet traditional systems ignore it because it doesn’t fit neatly into relational tables. Critical context remains hidden, and investigations begin with incomplete information. Without incorporating unstructured data, even the most advanced matching algorithms operate with only part of the truth.

The False Positive Tsunami

Attempting to loosen rigid rules to catch more matches only creates a flood of false positives - often exceeding 90% of alerts. Analysts spend valuable time clearing meaningless alerts instead of uncovering genuine threats, leading to fatigue, inefficiency, and high turnover. The system becomes a cost center instead of a risk-mitigation facility.

From Matching Records to Building an Intelligence Asset

True entity resolution is not about connecting tables - it’s about connecting intelligence. A modern approach builds a unified knowledge graph that fuses customer records, transactions, open-source data, and unstructured text into one cohesive analytical environment.

This enables a 360° contextual view of every entity and its relationships - transforming data from a passive repository into a continuously evolving intelligence asset.

Achieve a Single Source of Truth with a Knowledge Graph

Rather than temporarily joining siloed data for a single query, the DataWalk platform fuses that data permanently into a unified, scalable model. Analysts can instantly see that a person in the CRM, an account flagged in the transaction system, and a name mentioned in an investigation report all refer to the same entity.

This persistent context eliminates manual data stitching and ensures every analysis starts from a single, trusted foundation.

AI That Thinks Like an Analyst, Not a Spreadsheet

DataWalk’s Composite AI combines multiple analytical techniques - machine learning, graph reasoning, and expert-defined logic - to think contextually, just like a human analyst. This enables the system to uncover complex, indirect, or hidden relationships that traditional relational systems can’t detect.

Example:
Imagine Person X owns shares in Company A, which owns shares in Company B, which in turn owns shares in Company C, and Company C owns part of Company D. Meanwhile, Person Y holds a significant stake in Company D.

In a traditional system, there’s no link between X and Y - they would appear completely unrelated. But in a knowledge graph, these indirect ownership paths are automatically connected, revealing that X and Y are in fact related through a multi-level corporate structure.

Sometimes, even if certain attributes differ (for example, different spellings of names or addresses), synthetic data unification can reveal that these two records actually represent the same person. This process - called data synthesis - transforms fragmented data into meaningful intelligence, identifying links and relationships that were previously invisible.

That’s what makes Composite AI so powerful: it doesn’t just match rows - it reasons across relationships, replicating human intuition at scale. It connects the dots between distant entities, creating a unified picture of risk and exposure.

Turn Unstructured Text into Actionable Intelligence

With advanced entity extraction, DataWalk automatically reads unstructured text - SARs, case notes, or narrative reports - and identifies entities such as people, organizations, or phone numbers, placing them directly into the knowledge graph. Instantly, they are connected to structured data, revealing hidden relationships and previously invisible context that can shift the course of an investigation.

From Reactive Alerts to Proactive Discovery

Most financial crime units remain stuck in a reactive loop - drowning in alerts, manually reconciling data, and missing the bigger picture. A graph-based, Composite AI-driven approach shifts investigations from reactive alert-handling to proactive discovery.

With true entity resolution, teams gain the foundation to uncover unknown unknowns - the hidden networks and subtle relationships that define modern financial crime.
By unifying fragmented data into a living intelligence asset, you transform investigations from repetitive triage into strategic discovery.

Questions Executives Should Ask

• Do our analysts spend more time reconciling data than analyzing it?
• Can we integrate new data sources within days, not months?
• Can we connect structured and unstructured insights in real time?
• How long does it take to perform a Tier 1 triage on an alert?

If triaging a single alert requires using more than three systems or takes over 10 minutes, your organization may have a data fragmentation problem.

A modern investigative environment should allow analysts to see full context in one place - in minutes, not hours - without switching tools or losing information.

Executive Takeaway

Entity resolution is no longer a technical process - it is a strategic intelligence function.
Organizations that unify their data into a graph-based, Composite AI environment gain a persistent advantage: the ability to detect the undetected.

Action Points for Executives

• Audit your current entity resolution workflows for unstructured data and cross-system blind spots.
• Quantify analyst time spent on manual reconciliation versus investigative analysis.
• Evaluate graph-based Composite AI platforms for scalability, explainability, and integration speed.

It’s time to move beyond fragmented data and empower your teams with the full context of every risk. The difference between reactive compliance and proactive discovery begins with true entity resolution.