SAFARI. Future Work Ideas. Alberto Garcia-Robledo, Abel Sanchez, Rongsha Li, Juan-Carlos Murillo-Torres, John Williams and Sascha Boheme

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1 SAFARI Future Work Ideas Alberto Garcia-Robledo, Abel Sanchez, Rongsha Li, Juan-Carlos Murillo-Torres, John Williams and Sascha Boheme Massachusetts Institute of Technology z 1

2 Situational Awareness for Fraud Detection SAFARI introduces the concept of Situational Awareness to enable the detection of fraud on large volumes of payments where ground truth is not available, by integrating different perspectives of CoM data. 2

3 Current Work: 1 and 2-Level SA Approach Si tu at io na la wa r en es s SA Risk Managment and Prediction Level 3: Projection Predictive Analytics Level 2: Comprehension Visual Analytics and Data Integration Level 1: Data Collection and Anomaly Detection Perception RFNet modelling and Web dashboard visualizations RF raising at different perspectives of data 3

4 Next Steps: 3-Level Situational Awareness SA Risk Managment and Prediction Level 3: ss en e Aw ar at io na l Si tu Risk assessment, Risk anticipation, Risk mitigation Projection Machine learning, Risk learning/prediction, Case-based reasoning Predictive Analytics Level 2: Comprehension Visual Analytics and Data Integration Level 1: Data Collection and Anomaly Detection Perception 4

5 Invoice Number Anomaly Detection By exploiting Bioinformatics and ML algorithms An important fraud scenario is the detection of changes in invoice number structure. Current ad hoc method is hampered by thousands of false positives and lack of proper data visualization. New technique based on multiple sequence DNA alignment, dimensionality reduction and anomaly detection ML algorithms. 5

6 Scenario Fingerprinting and Simplification By exploiting complex network K-core decomposition The K-core decomposition of a network allow us to discover the importance of vertices, in terms of their closeness to the core of the graph. Applications: - RFNet fingerprinting - RFNet simplification for visual analytics - Discovery of hierarchical structure of potential criminal patterns 6

7 Risk Ranking Learning Through affinity analysis and recommendation algorithms Think of how Amazon and Netflix allow users to provide valuable feedback. In the absence of ground truth, SAFARI could exploit SME feedback to provide better and more accurate scores. Suggest similar entities/scenarios just like Amazon suggests similar products. 7

8 Black Swan Event Modeling Rara avis in terris nigroque simillima cygno There is no enough historical data to overcome statistical biases when it comes to exploit ML for fraud prediction. Better understanding of rare events can enable SAFARI to accurately model and predict rare occurrences of fraud. Adjusted statistical models and data collection strategies can be useful to overcome the disproportionate role Black Swan events. 8

9 PI Dataset Enrichment To overcome the lack of labeled data A holistic view of financial fraud should focus on human fraudsters by exploiting data from a variety of public sources. Web data (e.g. Web personal pages, blogs), social media (e.g. Facebook, Twitter, Linkedin), and mobile data. 9

10 SAFARI: Future Work To develop a predictive platform on top of the SAFARI framework, to provide SMEs with an advanced software framework for data-driven risk ranking that integrates the RF and RFNet concepts, rich Web-based Visual Analytics and PA, all in a unified platform, enabling the anticipation of future fraud risk events. SA Risk Managment and Prediction Level 3: Projection Predictive Analytics Level 2: Comprehension Visual Analytics and Data Integration Data Collection and Anomaly Detection Level 1: Perception 10

11 Benefits of the 3-Level SAFARI Platform Innovative fraud detection platform with increasing levels of intelligence and automation of fraud risk management. RF anomaly detection + RFNet integration + Visual Analytics + Prediction Analytics approach that discover and learn from fraud patterns in large volumes of unlabeled financial data. Open platform that can be extended in the future to take advantage of new advances and add more capabilities in data and visual analytics for fraud detection. 11

12 SAFARI Open Architecture Applications Scale risk assessment and prediction Cybersecurity monitoring and attack detection Insurance, bank and other kinds of financial fraud 12