Gotcha! Network Analytics to augment Fraud Detection Big Data in the Food Chain: the un(der)explored goldmine?

Transcription

1 Gotcha! Network Analytics to augment Fraud Detection Big Data in the Food Chain: the un(der)explored goldmine? December 4th, 2018 Author: Véronique Van Vlasselaer SAS Pre-Sales Analytical Consultant

2 Introduction Fraud Analytics Using Descriptive, Predictive and Social Network Techniques: A Guide to Data Science for Fraud Detection

3 Network Analytics! Say what?! Main analytical question in fraud: Given the current network, who shall be the next one that commits fraud?

4 Network Analytics! Say what?! 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

5 Network Analytics! Say what?! 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 Detection Predictive Analytics

6 Network Analytics! Say what?! 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.

7 Network Analytics! Say what?! Credit Card Transaction Fraud

8 Network Analytics! Say what?! Social Security Fraud

9 Network Analytics! Say what?! 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)...

10 Network Analytics! Say what?! 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 and retail Chemical networks - Network of OTU s

11 Network Analytics! Say what?! 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)

12 Featurization

13 Network Analysis Featurization Featurizationis the process in which the unstructured network is transformed to a structured form unstructured data structured data predictive model

14 Network Analysis Featurization Featurizationis the process in which the unstructured network is transformed to a structured form FEATURIZATION unstructured data structured data predictive model

15 Sociogram: Network Analytics Network Representation Matrix representation:

16 Network featurization processbased 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)? - Network Analytics Feature Engineering the n-order neighborhood - Betweenness, closeness, community detection

17 Network featurization process examples: the n-order neighborhood Network Analytics Feature Engineering Betweenness Closeness

18 Network Analysis Featurization

19 Collective Inference Algorithms

20 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

21 Collective Inference Algorithms RULE: IF MORE THAN HALF OF NEIGHBORS IS FRAUDULENT, NODE IS FRAUDULENT

22 Collective Inference Algorithms Influence propagation through the network E.g. Gotcha!, based on Google s famous PageRank algorithm

23 Collective Inference Algorithms Influence propagation through the network E.g. Gotcha!, based on Google s famous PageRank algorithm

24 Conclusion: Fraud Detection A Hybrid Approach

25 Hybrid Approach for Detection HYBRID ANALYTICAL METHODS Predictive Models Fraud Network Analysis Anomaly Detection Value Rules Manual Detection Capability

26 Questions? Feedback? Comments?