Banks Unlock Real-time Entity Resolution with Graph Technology
In banking, the question isn’t just “Is this person who they say they are?” — it’s “Who exactly are we dealing with?” And answering that has never been harder.
In a global bank, the same individual can appear in dozens of systems, each with its own quirks, omissions, and blind spots. Their mortgage sits in one core, their credit card in another, their business account on yet another platform. Add in mobile banking, call centers, branch visits, and third-party fintech integrations, and the view becomes a jigsaw puzzle of partial truths.
Meanwhile, those identities, whether legitimate customers or fraudsters posing as them, move at the speed of commerce. A single interaction can span multiple jurisdictions, regulatory frameworks, and product lines before it’s even complete.
Every hop is another chance for risk to slip through the cracks.
Legacy tech stacks weren’t built for this reality. They can’t track identities in real time across billions of transactions, let alone connect behavior patterns scattered across silos.
For Chief Data Officers and IT leaders, the stakes are unforgiving: unify every customer record into a single, trusted profile, keep it accurate across channels and geographies, enforce role-based access control, and do it without slowing the business down.
There’s no tolerance for false matches that frustrate customers, no room for missed links that let high-risk actors operate unseen, and no time for overnight batch jobs that delay action until it’s too late.
This is why modern banking leaders are turning to graph technology, the only architecture designed to resolve identities with both precision and speed, at global scale.
Why Traditional Entity Resolution Falls Short
Traditional entity resolution in banking was never designed for the scale and complexity of today’s financial networks. Most institutions still lean on fuzzy logic, probabilistic matching, or rigid rule-based workflows to piece customer records together.
These tools do an adequate job of catching obvious matches, but they fall apart when the data gets messy—and in banking, the data is always messy.
They choke on high-cardinality data, like mobile numbers, email addresses, and aliases that appear across thousands of accounts. They can’t follow indirect or behavioral patterns, so linked activity often slips by unnoticed. And they rarely scale across multiple jurisdictions, product lines, or legacy systems without breaking down entirely.
The cost of those limitations is steep: more false positives, more missed risks, and countless analyst hours wasted on dead-end investigations. In a world where financial crime moves in milliseconds, that’s a gap no bank can afford.
The Graph Advantage in Entity Resolution
Graph databases link identities by relationships and context, not just fields or formatting. This means banks can:
- Detect if multiple accounts are linked by shared devices, IP addresses, or contact points
- Trace customer behavior across channels (e.g., credit card and mortgage activity)
- Model synthetic identities, mule networks, or layered ownership chains
- Build compliance-ready identity graphs for AML and KYC audits
Because relationships are first-class citizens in graph, traversal is lightning fast, even when surfacing complex, multi-hop connections across billions of data points.
Real-World Impact: From Identity to Insight
When entity resolution works the way it should, the results ripple far beyond the data team. Fraud investigators move faster. Compliance teams gain confidence in their reporting. Customer experience leaders finally see the full story behind every interaction. And for the banks that have made the shift to graph-based identity, these are everyday realities.
Here are two examples of graph-powered entity resolution delivering measurable outcomes at scale:
A multinational bank unified customer data across eight major divisions, including private wealth, asset management, and consumer banking, into a single, real-time identity graph. This connected view powers fraud detection, customer onboarding, and targeted cross-sell programs, all while eliminating the manual reconciliation steps that slowed operations.
During the customer onboarding process for new credit card applications, a major bank used TigerGraph’s entity resolution to cross-reference incoming applications with known fraudulent entities. The system identified suspicious patterns from shared phone numbers to overlapping device fingerprints, reducing onboarding fraud losses and enabling faster, compliant approvals.
These are foundational architecture upgrades. And they’re critical for evolving identity resolution challenges.
Identity Resolution Meets Fraud and Compliance
Fraud Detection
Graph resolves identities across accounts with similar or linked behavior, even if names and addresses don’t match. This enables the detection of synthetic IDs, coordinated merchant collusion, and suspicious account clusters in real time.
Rather than chasing isolated transactions, banks dismantle the networks behind them.
AML & KYC
With graph, banks can map complex ownership networks, detect beneficial ownership overlap, and trace shell structures or layering activity, even across jurisdictions. Analysts can visually explore transaction paths, verify ultimate beneficial owners (UBOs), and respond to regulator queries with confidence.
Customer 360 & CX
Banks can also stitch together real-time, multi-product Customer 360 views that unify transactions, products, and interaction histories. This enables personalized experiences, faster onboarding, and better retention strategies, all while keeping compliance guardrails intact.
Why This Matters to CDOs and IT Leaders
For bank leaders, entity resolution is the backbone of modern risk management, compliance, and customer experience.
Chief Data Officers see graph-based identity resolution as the key to finally unifying customer data across silos, producing a single, accurate source of truth that can power everything from advanced risk models to personalized CX platforms.
With a graph foundation, they gain trustworthy audit trails for regulators and free their teams from the brittle, one-off ETL workflows that have slowed innovation for years.
For Directors of IT and Infrastructure, the value is equally tangible. TigerGraph detects patterns in billion-edge graphs in a fraction of a second , integrates seamlessly with both batch and streaming sources, and scales as easily in the cloud as it does in hybrid deployments.
Built-in support for temporal graphs, multi-entity types, and schema evolution means the platform grows and adapts with the business, without costly overhauls.
What Makes TigerGraph Different
Unlike retrofitted graph layers or “graph-lite” solutions, TigerGraph offers:
• Native graph performance that is not built on top of relational or NoSQL backends
• Index-free adjacency for true real-time traversal
• ML/AI-ready features for hybrid modeling (e.g., proximity scoring, community detection)
• Proven enterprise scale powering billion-edge graphs in production for Tier-1 banks with sub-80ms query times
- Enterprise-class data management and security features needed for business-critical data.
In a world where customer identity is constantly evolving and increasingly exploited, top banks need infrastructure that doesn’t just store data but understands it.
TigerGraph doesn’t just connect the dots. It connects the dots that matter.
Reach out today to learn how your fraud, risk, and compliance teams can align on one source of identity truth.
