Guide
What is graph intelligence?
Graph intelligence refers to graphical analysis that establishes links between entities such as users, devices, accounts, and transactions to represent how they relate across systems. Unlike rule-based and point solutions that examine signals in isolation, graph intelligence analyzes them in context to deliver deeper insights into how they are inter-linked.
It leverages generative and autonomous AI to create contextual risk profiles, detect patterns, and expose hidden linkages across connected records and timelines to power proactive decision-making. Because graph intelligence focuses on connections between entities, it helps identify fraudulent users, collusion, hidden network risks, and criminal rings operating across channels.
Why is graph intelligence needed?
From individuals to organized groups, fraud has evolved into a highly interconnected, global network, comprising multiple accounts, fake identities, and intermediaries working together. Fraudsters use device rotation, shared phones, common emails, addresses, and government IDs for identity farming. They also leverage the collective insights on existing user verification mechanisms that businesses use, to bypass them. Traditional rule-based systems that rely on scoring single events cannot detect these connections, allowing fraudsters free movement across business networks spanning multiple industries.
Furthermore, evolving regulations require businesses to track how accounts and transactions connect. Therefore, to expose coordinated activity, businesses need cross-entity fraud detection that can uncover connections across accounts, devices, and transactions.
Graph intelligence changes control points from isolated checks to network-based containment, providing businesses with a deeper view of the entities involved, complete with their interconnections. This helps speed up investigations for link-based detection, proactive protection from crime syndicates, and strengthen regulatory compliance.
Benefits that graph intelligence offers
Graph intelligence shifts fraud detection from a single event at a time to analyzing relationships across accounts, devices, and transactions. This provides businesses with multiple benefits including:
The ability to easily spot fraud that involves multiple entities while also narrowing down the focus of investigation. By providing businesses with the context for risk scoring, decision-making, and case management, graph intelligence-powered network view reduces the time spent on piecing data together and increases the time for review and decision-making. Over time, with the feedback loop, this graph evidence strengthens machine learning models to uncover evolving fraud patterns that older systems would miss. Furthermore, by sharing threat intelligence, businesses can prevent fraudsters from hopping between products or platforms.
Connecting data from internal and external sources to link accounts, devices, IPs, and transactions through shared identifiers. Fraud graph analytics helps easily trace activity across records for identity link resolution and switch between link types, cutting repetitive steps and speeding up action.
Early Containment: Exposing clusters of connected activity, while algorithms perform network-level risk scoring to spot risky inter-connections for further review. When a new node matches past fraud patterns, all the related accounts are flagged early. This allows for quick containment of fraudulent activity and reduces losses.
Reduced False Positives: Network context adds evidence for each decision and allows businesses to use link strength and transaction history to refine thresholds. This contextual fraud detection reduces false alerts, enabling analysts to focus on cases where connections exist and reducing friction for genuine customers.
Improved Operational Throughput: Graph visualizations improve investigational efficiency by helping analysts spot the linkages faster and more easily. While pre-linked evidence reduces manual search efforts, automated rules act on network signals to remove repetitive tasks fro review queues, allowing security teams more time to focus on analysis and remediation.
How does graph intelligence work?
Graph intelligence uses multi-source data ingestion to pull identity, device, transaction, and behavior data from sources both internal and external to a business. The key steps involved are:
Data ingestion and enrichment: Graph intelligence supports data enrichment for fraud analytics by ingesting records from payments, identity services, and devices through event streams, batch files, and APIs. It then standardizes the fields, aligns timestamps, and validates the checks performed to reduce errors. To improve match accuracy, it adds reference data and tags to the records during enrichment and before inserting them into the graph.
Identity matching and standardization: Graph intelligence uses an identity resolution engine that connects data points to identify whether they belong to the same person or account. It uses rule-based checks for exact matches and confidence-based models for the unclear ones. It assigns every user an individual IDs, removes duplicates, and keeps a record of the source of each data point, making verification easy.
Graph linkage and analytics: Graph analytics for fraud detection uses lines, called edges to show the interconnections between users, accounts, or devices. This enables teams to investigate the connections over time and use the evidence to build cases.
Decisioning and integration: Graph intelligence powers real-time fraud decisioning enabling businesses to instantly block, challenge, or approve actions during sign-up, login, or payment. Fraud fighters also get graph views and linked evidence allowing for faster resolution and compliance with reporting needs.
Fraud types graph intelligence can help detect
Graph intelligence helps detect fraud that is executed through networks rather than just isolated events. It maps links between accounts, devices, and payment points to reveal fraud rings working together. Bureau’s graph intelligence, called Graph Identity Network (GIN), not only supports fraud prevention at onboarding, but also powers investigations after sign-ups. By detecting cross-entity coordination through fake identities, hacked accounts, mule networks, and marketplace abuse, it enables investigators to trace fund flows, credential reuse, and shared devices. Some fraud typologies detected by GIN include:
Synthetic identity fraud: Supports synthetic identity detection by linking fragmented identity data across applications and services. It can detect the reuse of phone numbers, payment details, or device information, to expose use of a fake identity. This evidence informs decisions on whether to block or flag identities already tied to fraud.
Account takeover (ATO): Powers account takeover detection by spotting sudden changes in an account, such as addition of new devices or payment methods, changes in account access controls, or the accounts that receive fund transfers. It compares these changes with patterns from past fraud cases to detect signs of compromise and flag the accounts with unusual activity for additional review.
Mule networks: Makes mule account detection more effective by identifying accounts that act as middlemen in transaction chains, tracking fund transfers between accounts, and exposing accounts involved in receipt or payout of funds across unrelated customers or services being reused across channels. Using this evidence, businesses can place holds, ask for proof of the source of the money, or escalate the case to a law enforcement agency.
Collusion and marketplace abuse: For collusion detection, GIN links buyers, sellers, couriers, and devices that share addresses, payments, or behavior patterns. This helps with early detection of scams such as fake review groups, return fraud, promo abuse, or referral fraud where fraudsters collaborate, enabling businesses to take down listings, block accounts, and recover money.
Graph intelligence finds usage across use cases and industries
Onboarding, payment screening, dispute resolution and post-incident forensics are some use cases where graph intelligence is used. Businesses across industries rely on it for faster investigations and fraud loss mitigation. Whether deployed privately or through consortium fraud intelligence, Bureau’s GIN integrates seamlessly with KYC systems, payment gateways, and case management tools for more efficient network fraud detection.
Some industries using fraud graphs are:
Banking and financial services: GIN assists banking fraud detection by spotting loan stacking, mule accounts, and payment fraud across channels. The graph connects KYC records, transactions, and device logs to identify linked applications and account activity. This helps teams to trace money flows and create suspicious activity logs.
E-commerce and marketplaces: These platforms use GIN to prevent collusion between sellers and buyers, fake listings, and refund abuse. By connecting order histories, shipping addresses, and device details, GIN exposes coordinated fraud across accounts, enabling security teams to take appropriate remedial action, such as removing listings, suspending accounts, and recovering funds.
Ride-hailing and gig economy platforms: Gig economy fraud detection involves using GIN to uncover passenger-driver collusion, referral abuse, and account farming. By linking trip records, payment methods, and device data, it unearths coordinated fraud. This data informs decisioning on removal of abusive accounts and incentive adjustments.
Crypto and digital asset services: In crypto fraud detection, GIN is used to map wallet clusters and track the movement of funds across addresses. It connects deposits and withdrawals to uncover layering, wash trading, and reused addresses. These insights enable businesses to freeze funds, process chargebacks, and work with law enforcement.
Telecommunications: Operators use GIN for telecom fraud detection by spotting SIM swap rings and recycled accounts linked to fraud. GIN helps connect device IDs, SIM allocations, and call data to identify suspicious reuse patterns, supporting preventive activities such as instantly blocking all account activity and escalating to regulators.
How GIN helps businesses remain compliant
GIN speeds up AML KYC graph analytics, reviews, and monitoring through reliable audit trails in the form of timestamped connections between users, accounts, and transaction flows. It also allows businesses to collaborate against fraud by sharing risk signals and graph evidence, while still maintaining the privacy of raw identity data masked.
Some ways GIN strengthens fraud detection compliance are:
KYC / KYB enhancement: Enhanced KYC with GIN supports linking new users with past accounts, shared devices, and funding sources that are associated with fraud. It also helps continuously monitor and track related party activity, such as hidden family or ownership ties to inform action on further user/entity review or blocking.
AML and transaction monitoring: GIN-powered AML fraud detection helps analysts map how money moves across payment systems and spot suspicious patterns like layering and structuring.
Audit and reporting support: Graphs with clear interconnections between events and users strengthen audit trail fraud detection. They also allow analysts to use timestamped links and logs for rebuilding complex activities during audits and using snapshots for reports, regulator filings, or law enforcement requests.
Data privacy and governance: GIN uses tokenization and hashing to share fraud signals that prevents exposing personal details and ensures greater data privacy. Furthermore, with access controls and audit logs, it limits who can see the data and why.
Implementation challenges
The need to connect with multiple systems and data sources can make it difficult to set up graph intelligence infrastructures. Some common implementation challenges and ways to overcome are as described below:
Data integration and quality: Pulling data together from disparate sources and in different formats can impact data quality in fraud networks. To ensure graphs stay accurate, teams must maintain high quality of data by checking it frequently for any changes, removing errors, and testing new sources.
Scale and performance: For scalable graph fraud detection, the system must be able to handle large volumes of data without compromising speed or accuracy. Teams must consider using techniques like indexing, sharding, and caching to speed things up. Consider setting SLAs (service level agreements) to ensure both real-time checks and big batch jobs run within specified timelines.
Governance, privacy and legal constraints: Fraud data governance can help ensure sensitive information is used safely and legally. Consider assigning role-based access, data masking, and audit logs to prevent unauthorized access. When sharing data across countries, complete the legal agreements specifying consent and rules on the duration and purpose of the data storage.
Operational adoption and change management: For efficient operational adoption of GIN, train analysts on how they can use graph results in their daily work, empowering them to spot patterns, run graph searches, and share evidence. Success metrics can comprise volume of fraud detected, time saved, and reduction in false alarms
Future of graph intelligence
The future developments in graph intelligence will power automated fraud detection that is not only more accurate, but also privacy-centered and adaptive to evolving fraud tactics. It will make fraud detection smarter and faster with predictive fraud network intelligence. New AI methods such as graph neural networks (GNNs) are already being used to reveal hidden links and patterns that are often missed by humans and rule-based fraud detection software. GNN fraud detection models will continue to evolve and flag “embeddings” (also known as advanced patterns) indicative of suspicious accounts or behaviors, for further investigations. Strong governance and explainability of these models will be needed for businesses to stay compliant.
With federated fraud intelligence and secure methods, such as hashing and multi-party computation, businesses will be able to share fraud signals without exposing sensitive data. Consortiums will collaborate to set the rules on threat intelligence to be shared and dispute resolution. The network will grow stronger using enriched identity signals; and automated fraud workflows will become common, where alerts are routed automatically and policy actions applied without manual delays. Playbooks will provide guidance on incident management, complete with result evaluation for continual improvements.
Why choose Bureau GIN
Bureau’s Graph Identity Network combines identity decisioning with advanced graph analytics in a single, powerful fraud detection network. It leverages advanced machine learning models, device fingerprinting, vision AI, and graph analytics to give a 360° view of identity, behavior, and networks. By ingesting a wide range of signals and resolving them into connected graph entities, it reveals hidden fraud patterns and provides analysts with link histories, scores, and contextual insights, for faster and smarter risk-decisioning.
Unlike rule-based methods that cannot identify hidden links and patterns, Bureau GIN connects client data with ecosystem data to uncover the networks behind fraud and flag mule accounts, collusive users, fraud rings, and other hidden risks in real time.
With more than 1 billion encrypted identities, the biggest strength of Bureau GIN is network risk detection. It can examine shared devices, repeated payment cards, and unusual transaction flows, to establish money movement and expose laundering channels, collusion, and fraudulent onboarding attempts. Bureau GIN can analyze links to spot hidden threats even when a single account looks “clean,” in isolation. Its top 10% scores can detect 100% of mule accounts, allowing businesses to stop fraud quickly and accurately.
Businesses can deploy Bureau GIN through an API or private cloud. The network scores feed directly into ML models for fast, explainable decisions. Businesses can start small with pre-trained onboarding models that block risky accounts right away, move to private network mapping to strengthen monitoring, and later tap into Bureau’s global GIN to uncover mule networks across industries and platforms.
Bureau’s unified risk decisioning platform is purpose-built to solve real-world complexities. With custom workflows, APIs for real-time scoring at interaction points, and threat intelligence from a global network of clients, Bureau provides businesses with strong operational controls, access logs, and governance features for regulatory compliance. With seamless deployment, change management, and ongoing model maintenance, Bureau helps businesses level up their defenses, reduce losses, speed up decision making, and remain compliant with regulatory requirements.
From reactive defense to predictive interdiction, learn how Bureau GIN transforms fraud strategies, identifies collusive users, and helps dismantle fraud rings. Schedule a free demo now.
Key Takeaways
Bureau GIN is an AI-powered risk stack that uncovers hidden fraud networks in real time.
Unlike rules-based systems, GIN predicts and adapts to evolving threats.
Network risk is the strongest signal of fraud in GIN, which traditional models often miss.
Bureau GIN can be deployed on-premise, in a private cloud, or integrated via an API.
Bureau GIN’s top 10% scores catch 100% mule accounts with minimal false positives.
Frequently asked Questions
