Anomaly detection in networks with application to financial transaction networks
This paper is motivated by the task of detecting anomalies in networks of financial transactions, with accounts as nodes and a directed weighted edge between two nodes denoting a money transfer. The weight of the edge is the transaction amount. Examples of anomalies in networks include long paths of...
| Main Authors: | , , , , |
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| Format: | Working paper |
| Published: |
2019
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| _version_ | 1797085292602064896 |
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| author | Elliott, A Cucuringu, M Luaces, M Reidy, P Reinert, G |
| author_facet | Elliott, A Cucuringu, M Luaces, M Reidy, P Reinert, G |
| author_sort | Elliott, A |
| collection | OXFORD |
| description | This paper is motivated by the task of detecting anomalies in networks of financial transactions, with accounts as nodes and a directed weighted edge between two nodes denoting a money transfer. The weight of the edge is the transaction amount. Examples of anomalies in networks include long paths of large transaction amounts, rings of large payments, and cliques of accounts. There are many methods available which detect such specific structures in networks. Here we introduce a method which is able to detect previously unspecified anomalies in networks. The method is based on a combination of features from network comparison and spectral analysis as well as local statistics, yielding 140 main features. We then use a simple feature sum method, as well as a random forest method, in order to classify nodes as normal or anomalous. We test the method first on synthetic networks which we generated, and second on a set of synthetic networks which were generated without the methods team having access to the ground truth. The first set of synthetic networks was split in a training set of 70 percent of the networks, and a test set of 30 percent of the networks. The resulting classifier was then applied to the second set of synthetic networks. We compare our method with Oddball, a widely used method for anomaly detection in networks, as well as to random classification. While Oddball outperforms random classification, both our feature sum method and our random forest method outperform Oddball. On the test set, the random forest outperforms feature sum, whereas on the second synthetic data set, initially feature sum tends to pick up more anomalies than random forest, with this behaviour reversing for lower-scoring anomalies. In all cases, the top 2 percent of flagged anomalies contained on average over 90 percent of the planted anomalies. |
| first_indexed | 2024-03-07T02:06:51Z |
| format | Working paper |
| id | oxford-uuid:9f4b63b2-7ea3-4b8a-af7d-d021306d2041 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:06:51Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | oxford-uuid:9f4b63b2-7ea3-4b8a-af7d-d021306d20412022-03-27T00:56:25ZAnomaly detection in networks with application to financial transaction networksWorking paperhttp://purl.org/coar/resource_type/c_8042uuid:9f4b63b2-7ea3-4b8a-af7d-d021306d2041Symplectic Elements at Oxford2019Elliott, ACucuringu, MLuaces, MReidy, PReinert, GThis paper is motivated by the task of detecting anomalies in networks of financial transactions, with accounts as nodes and a directed weighted edge between two nodes denoting a money transfer. The weight of the edge is the transaction amount. Examples of anomalies in networks include long paths of large transaction amounts, rings of large payments, and cliques of accounts. There are many methods available which detect such specific structures in networks. Here we introduce a method which is able to detect previously unspecified anomalies in networks. The method is based on a combination of features from network comparison and spectral analysis as well as local statistics, yielding 140 main features. We then use a simple feature sum method, as well as a random forest method, in order to classify nodes as normal or anomalous. We test the method first on synthetic networks which we generated, and second on a set of synthetic networks which were generated without the methods team having access to the ground truth. The first set of synthetic networks was split in a training set of 70 percent of the networks, and a test set of 30 percent of the networks. The resulting classifier was then applied to the second set of synthetic networks. We compare our method with Oddball, a widely used method for anomaly detection in networks, as well as to random classification. While Oddball outperforms random classification, both our feature sum method and our random forest method outperform Oddball. On the test set, the random forest outperforms feature sum, whereas on the second synthetic data set, initially feature sum tends to pick up more anomalies than random forest, with this behaviour reversing for lower-scoring anomalies. In all cases, the top 2 percent of flagged anomalies contained on average over 90 percent of the planted anomalies. |
| spellingShingle | Elliott, A Cucuringu, M Luaces, M Reidy, P Reinert, G Anomaly detection in networks with application to financial transaction networks |
| title | Anomaly detection in networks with application to financial transaction networks |
| title_full | Anomaly detection in networks with application to financial transaction networks |
| title_fullStr | Anomaly detection in networks with application to financial transaction networks |
| title_full_unstemmed | Anomaly detection in networks with application to financial transaction networks |
| title_short | Anomaly detection in networks with application to financial transaction networks |
| title_sort | anomaly detection in networks with application to financial transaction networks |
| work_keys_str_mv | AT elliotta anomalydetectioninnetworkswithapplicationtofinancialtransactionnetworks AT cucuringum anomalydetectioninnetworkswithapplicationtofinancialtransactionnetworks AT luacesm anomalydetectioninnetworkswithapplicationtofinancialtransactionnetworks AT reidyp anomalydetectioninnetworkswithapplicationtofinancialtransactionnetworks AT reinertg anomalydetectioninnetworkswithapplicationtofinancialtransactionnetworks |