01
Signal-first intelligence
Entity, intent, and risk signals are extracted together so underwriting teams see context, not raw strings.
Financial Data Enrichment
From messy transaction trails to underwriting confidence.
Mazerik transforms fragmented transaction data into structured intelligence your team can approve, audit, and act on in seconds.
Live Data Flow
Raw Bank FeedUnstructured
PAYPAL INST XFER 123 HYPE QIMAWEB
MERRITT PKWY GREENWICH CT
PLAZA TIRE AND AUTO GRASSVALLEY
Mazerik OutputStructured
PayPal -> Transfer rail
Qima Cafe -> Merchant entity
Category -> Food and beverage
3D Signal Model
Fraud Guard
Compliance Trace
Entity match
Category confidence
Audit-ready output
A layered, explainable intelligence surface that turns statement noise into policy-ready decisions.
Trusted by modern finance teams
98.7%
Entity precision
<85ms
Average response
65+
Markets covered
420 classes
Classification depth
Product narrative
A cleaner way to read financial intent.
Move from manual interpretation to explainable automation with a workflow tuned for fintech risk, ops, and compliance.
02
Designed for operational trust
Every enriched record ships with traceable attributes that compliance and fraud teams can inspect instantly.
03
Built to plug into existing rails
Drop into decision engines and dashboards with consistent schemas across regions and data sources.
How it works
From fragmented inputs to decision-ready outputs in three steps
A predictable process your risk, operations, and finance teams can follow without introducing complexity.
01
Ingest
Connect transaction feeds from statements, ledgers, and payment rails in a single intake layer designed for inconsistent formats.
02
Enrich
Normalize merchants, categories, and recurring behavior with explainable metadata so teams can trust every output field.
03
Decide
Route structured signals into underwriting, compliance, and finance workflows with policy-ready context attached.
Security and compliance
Built for teams that need confidence before automation
Operational controls and traceability patterns that support fintech-grade review standards.
Audit trace on every record
Each enriched decision surface includes source-linked attributes and confidence context for policy reviews and exception handling.
Operational governance guardrails
Apply team-level thresholds and escalation policies before automation steps are approved in production workflows.
Data-handling posture built for fintech
Security-conscious defaults and environment isolation patterns are designed for sensitive financial operations.
Integration paths
Choose the implementation route that matches your team
Start with code, start with the dashboard, or combine both as your workflows mature.
API and SDK path
For engineering teams, integrate via REST or SDK and move from sample payloads to production-ready enrichment quickly.
Dashboard path
For ops-first teams, create a workspace first and move into dashboard-based enrichment without standing up custom pipelines on day one.
Use cases
Built for the teams responsible for financial accuracy
From risk to operations, each workflow starts with clearer transaction intelligence and ends with faster, safer decisions.
Underwriting
Enhance risk assessment with enriched financial data for more accurate and efficient underwriting decisions.
- Merchant identity clarity
- Recurring behavior visibility
- Review-ready risk context
Transaction Approval
Streamline approval workflows with structured, enriched data for faster and smarter decisions.
- Faster approval operations
- Explainable output traces
- Configurable review paths
Personal Finance
Deliver meaningful financial insights to consumers through enriched transaction understanding.
- Category normalization
- Entity grouping consistency
- Clearer spending insights
Customer Fidelity
Build deeper loyalty programs through granular spending pattern analysis.
- Spending pattern intelligence
- Segment-ready signals
- Offer personalization
Accounting
Automate bookkeeping and expense classification with structured transaction intelligence.
- Bookkeeping automation
- Expense classification
- Audit-ready categorization
Compliance
Strengthen review workflows with structured transaction context that teams can audit, explain, and act on quickly.
- Explainable output traces
- Audit-ready review context
- Configurable escalation paths
Operating model
How Mazerik fits into high-trust financial operations
Adopt the platform in controlled phases so each team can validate quality before scaling automation.
Phase 01
Design your decision model
Define the outcomes, confidence thresholds, and workflow checkpoints that matter most to your team.
Phase 02
Ship with guarded automation
Deploy enrichment into production paths with human review loops where policy requires additional control.
Phase 03
Continuously improve signal quality
Use feedback and edge-case discovery to tighten operational quality without rebuilding downstream systems.
Introducing Atelier
Get real-time insights into your business finances with your personal AI agent.
Atelier
Your personal AI Agent
What percentage of invoiced revenue came from discounted invoices last quarter?
Discounted invoices represented 16.1% of invoiced revenue last quarter. Average discount rate was 10.5%, with strongest concentration in enterprise contracts renewed in week 3.
Highlight merchants with recurring cash-flow anomalies in the last 30 days.
Draft a risk summary for accounts with rising chargeback probability.
Ask Atelier anything
Get Access to AtelierTestimonials
Trusted by risk, operations, and finance teams
Teams adopting Mazerik and Atelier report faster underwriting cycles, cleaner compliance reviews, and stronger confidence in automated decisions.
“Mazerik gave our underwriting team decision-grade transaction context in minutes instead of days. We cut manual review time by over 40%.”
Alina Rao
VP of Risk · Northline Capital
“The enrichment confidence and traceability made compliance sign-off dramatically easier. Our ops team finally trusts the automation layer.”
Devon Mitchell
Head of Operations · Summit Ledger
“The Atelier workflow became our fastest path from raw feed to board-ready insights. It feels like having an analyst on-call 24/7.”
Mina Duarte
CFO · Kiteforge
FAQ
Frequently asked questions
Everything teams ask before launching Atelier and transaction intelligence workflows in production.
How does the AI agent use our transaction data?
Atelier uses the same enrichment pipeline as our core API, then layers query reasoning on top of normalized entities, categories, and confidence signals. You get explainable outputs with trace fields for audit and policy review.
Can we connect Atelier to existing dashboards and decision engines?
Yes. Atelier is designed to plug into your current stack through API responses and structured exports, so teams can route insights into BI dashboards, case-management tools, and underwriting workflows.
Is the model output deterministic enough for compliance workflows?
For compliance-sensitive processes, each answer includes source-linked enrichment attributes and confidence metadata. Teams can configure guardrails and thresholds before automated actions are taken.
Do you support multi-country transaction data?
Mazerik supports multi-market data normalization with region-aware merchant/entity handling so global portfolios can maintain consistent decision logic across geographies.
From our blog
Fresh thinking on enrichment and fintech AI
Supported by
QED Investors
QED Investors
Lake Star
Lake Star
Cash App
Cash App
ComplyAdvantage
ComplyAdvantage
Ramp
Ramp
Build 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.