Graphs for Finance - AML with Neo4j Graph Data Science
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This document discusses using graph data science and graph algorithms to detect fraud. It explains that graph data science uses relationships in data to power predictions. It provides examples of how graph algorithms like Louvain clustering, PageRank, connected components, and Jaccard similarity can be used to identify communities that frequently interact, measure influence, identify accounts sharing identifiers, and measure account similarity to detect fraud in applications like banking and financial services. The document also discusses using graph embeddings and feature engineering with graph networks to improve machine learning models for fraud detection by basing predictions on influential entities and their relationships.
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Graphs for Finance - AML with Neo4j Graph Data Science
- 1. AML with Graph Data Science Håkan Löfqvist, Neo4j hakan.lofqvist@neo4j.com
- 2. “Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.” - Wikipedia 2 Data scientists use data to answer questions.
- 3. Graph Data Science is a science-driven approach to gain knowledge from the relationships and structures in data, typically to power predictions. 3 Data scientists use relationships to answer questions.
- 4. 4 Fraud Marketing Customer Journey in Financial Services and Banking • First party & synthetic identity fraud • Fraud rings • Anti money laundering • Disambiguation • Recommendations • Customer segmentation • Churn prediction
- 6. Graph Feature Engineering Graph Embeddings Graph Networks 6 Graph AnalyticsKnowledge Graphs Graph search and queries Support domain experts
- 7. Deceptively Simple Queries How many flagged accounts are in the applicant’s network 4+ hops out? How many login / account variables in common? Add these metrics to your approval process Queries in Financ Improving existing pipelines to identify fraud via heuristics 7
- 8. Graph Feature Engineering Graph Embeddings Graph Networks 8 Knowledge Graphs Graph Analytics Graph queries & algorithms for offline analysis Understanding Structures
- 9. • Degree Centrality • Closeness Centrality • Harmonic Centrality • Betweenness Centrality & Approx. • PageRank • Personalized PageRank • ArticleRank • Eigenvector Centrality • Triangle Count • Clustering Coefficients • Connected Components (Union Find) • Strongly Connected Components • Label Propagation • Louvain Modularity • Balanced Triad (identification) • Shortest Path • Single-Source Shortest Path • All Pairs Shortest Path • A* Shortest Path • Yen’s K Shortest Path • Minimum Weight Spanning Tree • K-Spanning Tree (MST) • Random Walk • Breadth & Depth First Search • Triangle Count • Local Clustering Coefficient • Connected Components (Union Find) • Strongly Connected Components • Label Propagation • Louvain Modularity • K-1 Coloring • Modularity Optimization • Euclidean Distance • Cosine Similarity • Node Similarity (Jaccard) • Overlap Similarity • Pearson Similarity • Approximate KNN Pathfinding & Search Centrality / Importance Community Detection Similarity Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors Embeddings • Node2Vec • FastRP • GraphSAGE
- 10. Graph Algorithms for Detecting Fraud Graph algorithms enable reasoning about network structure Louvain to identify communities that frequently interact PageRank to measure influence and transaction volumes Connected components identify disjointed group sharing identifiers Jaccard to measure account similarity 10
- 11. Graph Embeddings Graph Networks 11 Knowledge Graphs Graph Analytics Graph Feature Engineering Graph algorithms & queries for machine learning Improve Prediction Accuracy
- 12. Feature Engineering is how we combine and process the data to create new, more meaningful features. Using graphs we can base ML on influential people that share identifiers. 12 Financial Transaction Data
- 13. Graph Feature Engineering 13 Knowledge Graphs Graph Analytics Graph Embeddings Graph embeddings for dimensionality reduction Predictions on complex structures Graph Networks
- 15. 15 Demo
- 16. 1 6 Graph Model
- 17. 17 What we will cover in the demo Knowledge Graph Queries Graph Analytics Graph Embedding + ML