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Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017

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Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention:
PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, I’ll present how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. I’ll elaborate on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. I’ll explain how we employ sophisticated machine learning tools – open source and in-house developed.
I will also present results from experiments conducted on a very large graph data set containing millions of edges and vertices.

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Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017

  1. 1. Large Scale Graph Processing & Machine Learning for Fraud Prevention 1 Disclaimer: views expressed here are my own and do not necessarily represent the views of PayPal, its affiliates or subsidiaries.
  2. 2. © 2016 PayPal Inc. Confidential and proprietary. Agenda • Graph Processing • Machine Learning 2
  3. 3. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing - Motivation • (Big) Data is not flat • Data is multi-relational, spatial, temporal, multi-modal • Machine learning algorithms assume i.i.d (independent & identically distributed) • Need Data Science for Graphs 3 Software (Java/JVM) Source: Lise Getoor, UCSC
  4. 4. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing - Motivation • Much harder to get performance lift on our flagship models • Need to re-look at all aspects of traditional model building • Need out-of-the-box thinking 4 Area we are missing (AUC 0.98)
  5. 5. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing With Giraph (Current) • Pregel (Google’2010) • Vertex Centric • Giraph (Apache Open Source) 5
  6. 6. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing With Giraph (Current) • Used to compute 2-step neighbhors – IP,VID • Data • 5 events (login, logout, transaction, fso, signup) • Stampy HDFS • 1 year • Workflow • Series of (10) map-reduce jobs to join/pre-process construct graph • Neighorhood computation uses Giraph • Output pushed out online as RADD • Issues • 2 days for end-to-end process • Lots of intermediate read/write • Hadoop cluster stability • Unsupported by Hortonworks 6 Hadoop HDFS (Stampy) RADD M/R Jobs M/R Jobs Giraph Jobs Giraph Job Online Offline
  7. 7. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing (Future) • Leverage graph representation to understand, analyze, visualize relationships – their strengths, direction & influence and to predict future relationships and interactions between entities (consumers, merchant, IP address, device IDs, nodes in a network…). • Algorithmic convergence of graph theory & machine learning (statistical relations learning & probabilistic graph modeling) • Multi-layered full fledged specialized stack, hardware to software. 7 GPU, FPGA, TPU., SSD (specialized hardware) Graph DB (specialized software) Graph Algorithms (Deterministic & Probabilistic) Graph Analytics & Visualization Graph Modeling (Prediction)
  8. 8. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing (Future) • Solve new problems that traditional approaches can not solve: • What is the likelihood that particular consumer (node in graph) colluding (hidden relationship in graph) with a particular merchant (node in graph)? • How fraudsters (series of nodes & edges) move money through our network i.e., anti-money laundering? • If a consumer (node) is buying (relation) product X (node) from merchant (node), what else can we recommend to the consumer? • If a server (node) in a data center goes down/unhealthy, what would would be the impact to other critical components? • Who (what) are the central influential people (assets) in our network? • Graph theory & algorithms (link analysis, page rank, belief propagation, markov random field) can help to uncover influencing nodes & hidden relationships. • Graph databases are much more efficient in unearthing graph properties, have flexible schema that is amenable to changing data, scale naturally to large data sets as they do not require expensive join operations. 8
  9. 9. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing POC • POC with new stack • Single powerful machine in-lieu of 1200+ node stampy cluster • 160 core • State-of-the-art NVidia Pascal GPU (4 P100) • 1 TB RAM • 2 TB SSD • Graph DB optimized for scale-up architecture (Neo4j) • Graph computing frameworks optimized for single server/GPUs 9 Hadoop HDFS (Stampy) RADD Graph DB (Neo4J) Online Offline Hardware P100 GPU; SSD Graph Algorithms (Neo4J) Graph Algorithms (GunRock)
  10. 10. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing POC • Graph • 1.5 billion edges; 0.5 billion vertices • Static graph (1 year); No time dimension • Vertices - Customer ID, Merchant ID, IP; Edges – payment transaction 10 Degree 2^0: 58104040 (58.10%) Degree 2^1: 23034683 (23.03%) Degree 2^2: 11711920 (11.71%) Degree 2^3: 5098768 (5.10%) Degree 2^4: 1575787 (1.58%) Degree 2^5: 366774 (0.37%) Degree 2^6: 79654 (0.08%) Degree 2^7: 19796 (0.02%) Degree 2^8: 5861 (0.01%) Degree 2^9: 1886 (0.00%) Degree 2^10: 581 (0.00%) Degree 2^11: 179 (0.00%) Degree 2^12: 52 (0.00%) Degree 2^13: 12 (0.00%) Degree 2^14: 4 (0.00%) Degree 2^15: 1 (0.00%) Degree Distribution
  11. 11. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing POC • GraphAlgorithms • Page Rank • Connected Components • find sets of connected nodes • Each node is reachable from any other node in the set • Community Detection • Label Propagation – Group nodes with similar properties 11
  12. 12. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing POC • Results • Neo4j • GunRock, NvGraph, SNAP – in progress 12 Algorithm Compute Time (ms) Write Time (ms) Page Rank 77 124 Connected Components 156 337 Community Detection 384 361
  13. 13. © 2016 PayPal Inc. Confidential and proprietary. Graph Processing - Key Learnings • Scale-up showing promise • 1 year graph ~70GB • Can fit entire graph in 1 machine’s memory • Significant reduction in processing time • ~2 days -> ~2 hours • Compute ~77 milliseconds • Significant value in Graph DB – sub-graph creation, interactive exploration, visualization, savings in computation 13 Source: Lise Getoor, UCSC
  14. 14. Machine Learning - Current Research Workflow Hardware - Research, Procure, Provision Modeling Algorithm Research Tool - Evaluate, Benchmar k Data & Variable Collection and Selection Model Training - Developm ent, Performan ce Eval, Spec (offline) Scoring Engine Developm ent (online) Deployme nt - One Box & Load test Auditing - Variable & Score Go Live ©2015 PayPal Inc. Confidential and proprietary. 14 • Manual Development (Spec -> Code) • ML Tool Specific Software Stack • Lack of research infrastructure/self-service tools • Serial pipeline; No early involvement of relevant teams
  15. 15. © 2016 PayPal Inc. Confidential and proprietary. Machine Learning - Future Modeling/Prediction Stack • Algorithm • Deep Learning • Offline to Online Learning • Human to Auto Feature Engineering • Model Spec • Language Agnostic Platform agnostic Spec (ex: protobufs) • Tools • Scalable & Flexible (ex: TensorFlow) • Software • Language Agnostic Software Stack • Hardware • Accelerators (GPUs, TPUs,..) 15 SS Hardware CPU + GPU/TPU Model Spec (protobufs) Tools (TensorFlow) Algorithm (Deep Learning) Software (Language Agnostic)
  16. 16. © 2016 PayPal Inc. Confidential and proprietary. Conclusions • Graph algorithms & Deep Learning shows promising analytical results • Scale-up architecture shows promise for our use cases • Probabilistic Graph Modeling + Deep Learning => Interpretable Models • Scratched surface; lot left to accomplish 16 Software (Java/JVM) Source: Lise Getoor, UCSC

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