Thank you very much.
Hi I have completed the course and would like to claim the $100 in aura credits. I have submitted the form as requested. Thanks Michael.
Hi, I have completed the course, received the mail for the aura credits, redeemed them, but still they are not available, what can be the problem?
Hi Nada.- if you are using an Aura Free account and you successfully redeemed them - they are there but do not show up on the Dash Board.. I have shared it with product, but you will be able to use the credits when you try. Let us know if for some reason you cannot.
Yes -- as the post above states, for the AuraDB Free Tier accounts the credits are there but are not able to be seen. Adding a CC will not change that, The team is aware though, but feel free to use them.
hey finished the course and filled the form when can I expect the aura credits into mu account?
@Ari_Neo4j I submitted the project, but it's still not shown in banner
I just added it! Your are officially on the banner. @prashant7090 is added too!
Hi - We send out the Aura credits 2x a week. I believe the next batch will be scheduled for today.
VAT-TaxGraph AI
What It Does
VAT-TaxGraph AI is a Neo4j-powered forensic tax investigation agent that uses graph intelligence to detect suspicious trading behavior, hidden business relationships, and potential VAT fraud.
The agent converts natural language investigation requests into Cypher queries and performs graph-based relationship analysis across companies, directors, and transaction networks.
The system focuses on identifying:
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circular trading patterns
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Companies sharing directors
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non-filer participation in active trade flows
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suspicious transaction chains
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seller-only entities
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tightly connected trading clusters
The agent uses Text2Cypher to generate investigation queries dynamically from user prompts.
Dataset and Why a Graph Fits
I generated a synthetic tax compliance dataset consisting of:
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Companies
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Directors
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VAT trading transactions
Graph modeling was ideal for this case because fraud detection depends heavily on relationships and multi-hop traversal patterns rather than isolated records.
The graph structure allows detection of:
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hidden ownership structures
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indirect trading relationships
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circular transaction flows
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interconnected suspicious entities
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fraud rings across multiple companies
Graph Schema
Nodes:
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Company(pin, name, filing_status)
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Director(director_id, name)
Relationships:
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(Director)-[:DIRECTS]->(Company)
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(Company)-[:SOLD_TO]->(Company)
The dataset was imported into Neo4j AuraDB Professional using Aura credits and queried through Cypher-based investigation workflows.
AI Investigation Capabilities
Example investigation prompts:
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“Identify circular trading patterns involving Non-Filer companies connected through shared directors.”
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“Find companies connected through shared directors that are actively trading with each other.”
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“Trace multi-hop trading relationships connected to companies controlled by the same director.”
The agent generates graph traversal Cypher queries such as:
MATCH p=(d:Director)-[:DIRECTS]->(c1:Company)-[:SOLD_TO*1..4]->(c2:Company)
RETURN p
LIMIT 50;
Technologies Used
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Neo4j AuraDB Professional
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Cypher Query Language
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Text2Cypher Agent
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Synthetic Fraud Detection Dataset
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Graph Traversal and Path Analysis
Screenshots
- Aura Agent Console Screenshot
- Graph Visualization Screenshot
Why I Built This
Traditional tabular analysis struggles to uncover hidden relationships between entities involved in suspicious trading behavior.
Graphs make these connections visible and traversable.
TaxGraph AI demonstrates how Neo4j and AI agents can support forensic investigations through explainable graph reasoning and connected intelligence.
