ATM Fraud Detection with Oracle Property Graphs

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Vertex edge A Property Graph (also called node)

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edge edge label edge properties edge ID directed edge vertex (node) vertex properties vertex ID a vertex can have a label

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Every row of a table has a fixed, identical structure Connections at a table level (not row) Nodes and edges can have any number of properties Connections at a node level (can be seen as row level)

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Spain Italy John Doe Company A Company B Company C Company D Located in Located in Located in Located in Buys from Buys from Buys from Buys from Money laundering and VAT frauds Owns

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Good guy Bad guy Card cloned while using an ATM Somewhere else

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bank customer account account customer ATM account CC transfer money to owns owns owns get money from

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• • • • • • The presentation was a live demo, screenshots aren’t as explicit as seeing it…

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{ "account_id": "a839", "timestamp": "2019-03-10 13:49:32 +0000", "atm": "24/7", "amount": 20, "location": { "lat": "53.7835061", "lon": "-1.3794149" }, "transaction_id": "555aa4f6-433b-11e9-b9b9-0242ac140005" } Location of the ATM

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Kafka stream Property Graph

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Kafka stream Property Graph A Java app consume the Kafka stream and update the graph with the new messages received.

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The number of edges increase over time, it’s the Kafka messages being loaded in the graph

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PGQL query, looks like SQL but has the extra GRAPH syntax

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PGQL query, looks like SQL but has the extra GRAPH syntax

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The UK set of ATMs used is a bit too sparse over a big area. Too many of the randomly generated transactions seem to be fraud.

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The UK set of ATMs used is a bit too sparse over a big area. Too many of the randomly generated transactions seem to be fraud.

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• • • • • • • more about graphs: Data Lineage Made Easy with Graph Database 14:50-15:20 | Sentosa 1 (the other room) UKOUG Techfest 2019, Dec 2019 in Brighton