Identity quality
Canonical mapping
Resolve noisy strings to consistent merchant identities.
TX Enrichment / Entities Identification
Find the who behind every transaction
Gain granular context on all entities in a transaction.
Identify transaction counterparties with context your teams can trust across underwriting, fraud, and operations.
Entity Resolver
Raw string to canonical identity
Resolve ambiguous descriptors into consistent counterparties with confidence context.
Canonical NameEntity TypeConfidence
Raw feedPAYPAL INST XFER QIMA
ResolvedQima Cafe Ltd.
TypeMerchant · SMB
Structured identity context reduces manual interpretation overhead.
Demystify your transactions' entities
Break raw strings into canonical merchants, intermediaries, marketplaces, and people so downstream products can reason over clean identities.
Separate merchants from payment rails and intermediaries.
Standardize merchant understanding across fragmented descriptors.
Turn transaction strings into entity context suitable for product logic and review workflows.
Unparalleled coverage no one can match
Mazerik is designed for the full long tail of entities, not just the most common merchants in a sample dataset.
Normalize variant naming into one canonical entity profile.
Surface granular entity insights teams can inspect before automation.
Use cleaner identity data to power more delightful user-facing transaction experiences.
Entity identification outcomes teams can operationalize
Entity outputs are structured for policy workflows, not just display labels, so teams can act with confidence.
Decision value
Context rich
Attach metadata useful for risk and compliance review.
Coverage posture
Long-tail aware
Designed for real-world variability beyond common merchants.
Role-based entity intelligence use cases
Different teams rely on entity understanding for different decisions; this section maps those workflows.
Risk
Counterparty risk interpretation
Use cleaner entity mapping to reduce ambiguity during affordability and credit policy checks.
Operations
Case routing by counterparty type
Direct manual reviews by entity profile and transaction context instead of raw string rules.
Finance
Vendor and merchant grouping
Normalize fragmented vendor naming for cleaner spend and cash-flow reporting.
Entity workflow from raw strings to clear counterparties
A repeatable process for transforming ambiguous merchant text into operationally useful entity profiles.
Capture raw transaction descriptors
Ingest transaction narrations and identifiers from your selected data channels.
Resolve to canonical entities
Match records to normalized merchant or counterparty identities with supporting context.
Push entity outputs downstream
Apply entity context in decision engines, case tools, and internal analytics workflows.
Workflow view
Counterparty profile panel
Context cards expose identity attributes for decision workflows.
Workflow view
Counterparty profile panel
Context cards expose identity attributes for decision workflows.
Merchant clusterIntermediary traceReview notes
Primary merchantQima Cafe Ltd.
IntermediaryPayment rail linked
ConfidenceHigh
Entity profile snapshots keep product and ops decisions aligned.
Entity architecture and field model
Built to separate raw transaction text from normalized identity layers your systems can consume repeatedly.
Parsing and normalization
Extract meaningful patterns from variable transaction strings before identity resolution.
Entity resolution layer
Map transaction artifacts to canonical entities with context fields for downstream usage.
Delivery for decisions
Expose entity outputs in consistent shapes for product and operations teams.
Entity API field sample
entity.id
string
Stable identifier for the resolved entity.
entity.name
string
Canonical display name for the counterparty.
entity.type
enum
Classification such as merchant, marketplace, or intermediary.
entity.confidence
number
Resolution confidence indicator for review workflows.
Entity identification FAQs
Common questions teams ask about integrating and operationalizing entity-level transaction context.
Can entity outputs be reviewed before automation?
Yes. Teams can layer review thresholds and exception policies before automating downstream actions.
Will this work with inconsistent transaction descriptions?
The workflow is designed for inconsistent real-world transaction strings and variable formatting patterns.
Can we use entity data in both product and internal workflows?
Yes. The same normalized entity layer can power customer-facing and internal decision workflows.
Got the who, now how?
You can use our products via our API or through our intuitive UI dashboard depending on your needs. Your 14-day free trial with 2,000 transactions is just a click away.
Via API or SDK
Do you have developers onboard? Then you can quickly integrate our Python SDK or REST API and get set up in minutes.
Read docsVia the Mazerik Dashboard
No developers onboard? No problem. Our intuitive UI allows you to identify entities using CSVs and PDFs as your data sources.
Get startedBuild with confidence
Join hundreds of companies taking control of their transactions
Mazerik is the most accurate financial data standardization and enrichment API. Any data source, any geography.