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Transaction Security

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Overview of <strong>Transaction</strong> <strong>Security</strong> Technology Solutions<br />

5 Machine Learning /<br />

Big Data Analytics<br />

Machine Learning Engines and Big Data Increasingly Used to Combat Fraud<br />

• Machine learning has emerged as a technology that can effectively combat fraudulent<br />

transactions<br />

• Rather than segregating specific types of transactions and investigating, these advanced<br />

solutions are capable of synthesizing historical and real-time transactions to construct<br />

individual progressive models to detect fraudulent patterns (1)<br />

Online<br />

Machine Learning Engine<br />

Block Fraudulent<br />

<strong>Transaction</strong>s<br />

Point-of-Sale<br />

Detection Rules<br />

Contextual<br />

Analysis<br />

Incoming Real-<br />

Time Card<br />

<strong>Transaction</strong>s<br />

Trend<br />

Analysis<br />

Decision Rules<br />

Automatically Track<br />

Fraudulent Activity<br />

ATM<br />

Location<br />

Analysis<br />

Live Profile<br />

Database<br />

Categorize Fraudulent<br />

<strong>Transaction</strong><br />

Customer data is<br />

constantly aggregated<br />

via payment portals –<br />

merchants, POS, ATM,<br />

etc. – and processed /<br />

tagged with data points<br />

Client profiles are stored in a database,<br />

modeled and constantly updated<br />

Source: Feedzai, IBM Research.<br />

(1) IBM Research, “Using machine learning and stream computing to detect financial fraud.”<br />

Due to the fact that<br />

machine learning<br />

software updates with<br />

live transactions, the<br />

ability to react rapidly<br />

and change fraud<br />

patterns is automated<br />

27

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