gotcha! network analytics to augment fraud detection · gotcha! network analytics to augment fraud...

Gotcha! Network Analytics to augment

Fraud DetectionBig Data in the Food Chain: the un(der)explored goldmine?

Author: Véronique Van VlasselaerVéronique Van VlasselaerVéronique Van VlasselaerVéronique Van Vlasselaer

SAS Pre-Sales Analytical Consultant

December 4th, 2018

IntroductionFraud Analytics Using Descriptive, Predictive and Social Network Techniques:

A Guide to Data Science for Fraud Detection

Network Analytics! Say what?!

• Main analytical question in fraud:

Given the current network, who shall be the next one that commits fraud?

• Traditional approach in an fraud context:

• Finding descriptive patterns (e.g. multivariate outliers) or predictive patterns(e.g. predictive analytics) in massive amounts of structured data

• Traditional approach in an fraud context:

• Finding descriptive patterns (e.g. multivariate outliers) or predictive patterns(e.g. predictive analytics) in massive amounts of structured data

Multivariate Outlier DetectionMultivariate Outlier DetectionMultivariate Outlier DetectionMultivariate Outlier Detection Predictive AnalyticsPredictive AnalyticsPredictive AnalyticsPredictive Analytics

• State-of-the-art insights grounded in social sciences:

• Fraud is “socially” contagious.

- If Bart and Peter are both fraudsters, and Véronique is friends of Bart and Peter, what would you expect of Véronique’s behavior?

• Extension of traditional detection approaches by including social interactions among fraudsters (and other people).

• Data issue: networked data is unstructured.

Network Analytics! Say what?!Credit Card Transaction Fraud

Network Analytics! Say what?!Social Security Fraud

• Networked data? Where to find?

• Much more than data on social media channels.

• Call behavior data

• Review data

• Transactional data

• Employee data

• Financial data

• Sales data (e.g. Ebay)

• ...

• Networked data? Where to find?

• International agro-food trade network

- Network of food suppliers and nations

- Detection of faulty food production

- Impact of food contamination

- How to quickly shortcut a potential safety breach?

• Food supply network

- Network of raw material suppliers, food processors andretail

• Chemical networks

- Network of OTU’s

• Main analytical question in fraud:

Given the current network, who shall be the next one that commits fraud?

• Main analytical solutions

• Featurization of the network

• Collective inference algorithms (incl. behavioral propagation)

Featurization

Network AnalysisFeaturization

• Featurization is the process in which the unstructured network is transformed to a structured form

unstructured dataunstructured dataunstructured dataunstructured data structured datastructured datastructured datastructured data predictive modelpredictive modelpredictive modelpredictive model

• Featurization is the process in which the unstructured network is transformed to a structured form

unstructured dataunstructured dataunstructured dataunstructured data structured datastructured datastructured datastructured data predictive modelpredictive modelpredictive modelpredictive model

FEATURIZATIONFEATURIZATIONFEATURIZATIONFEATURIZATION

Network AnalyticsNetwork Representation

• Sociogram:

• Matrix representation:

• Network featurization processNetwork featurization processNetwork featurization processNetwork featurization process based on

• the first-order neighborhood or egonet of each entity

- How many churners/fraudsters/adopters are connected to node (i.e., degree)?

- Density of the egonet?

- Number of suppliers/addresses/customers from a black list in the egonet?

- Velocity of the network (time-based network analysis)?

• the n-order neighborhood

- Betweenness, closeness, community detection…

Network AnalyticsFeature Engineering

• Network featurization processNetwork featurization processNetwork featurization processNetwork featurization process examples:

• the n-order neighborhood

Network AnalyticsFeature Engineering

BetweennessBetweennessBetweennessBetweenness ClosenessClosenessClosenessCloseness

Collective Inference Algorithms

• Collective inference algorithms

• The label of a node is said to dependent on the labels of the neighboring nodes.

• Chicken-egg problem:

- The label of node A depends on the label of node B, and

- The label of node B depends on the label of node A.

• In general: iterative procedure with random ordering

RULE: RULE: RULE: RULE: IF MORE THAN HALF OF IF MORE THAN HALF OF IF MORE THAN HALF OF IF MORE THAN HALF OF NEIGHBORS IS FRAUDULENT, NEIGHBORS IS FRAUDULENT, NEIGHBORS IS FRAUDULENT, NEIGHBORS IS FRAUDULENT, NODE IS FRAUDULENTNODE IS FRAUDULENTNODE IS FRAUDULENTNODE IS FRAUDULENT

• Influence propagation through the network

• E.g. Gotcha!, based on Google’s famous PageRank algorithm

• Influence propagation through the network

• E.g. Gotcha!, based on Google’s famous PageRank algorithm

Conclusion: Fraud DetectionA Hybrid Approach

Hybrid Approach for Detection

Capability

Manual Detection

Predictive Models

Fraud Network

Analysis

Anomaly Detection

HYBRID ANALYTICAL METHODSHYBRID ANALYTICAL METHODS

Questions? Feedback? Comments?veronique.van.vlasselaer@sas.com

gotcha! network analytics to augment fraud detection · gotcha! network analytics to augment fraud...

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