{"id":"W2333568743","doi":"10.1016/j.jfds.2016.03.001","title":"Auto insurance fraud detection using unsupervised spectral ranking for anomaly","year":2016,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ranking (information retrieval); Pattern recognition (psychology); Anomaly detection; Computer science; Spectral clustering; Data mining; Outlier; Laplacian matrix; Rank (graph theory); Artificial intelligence; Categorical variable; Similarity (geometry); Ranking SVM; Mathematics; Machine learning; Cluster analysis; Graph; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001617016,0.00006891025,0.0001006375,0.0000958591,0.0005248105,0.0001037696,0.001809547,0.00002086351,0.000001024058],"category_scores_gemma":[0.00007860635,0.00003764835,0.00002452202,0.0005483102,0.0002695329,0.002534723,0.0002499683,0.00007038962,8.31329e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003414636,"about_ca_system_score_gemma":0.0001342318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001543502,"about_ca_topic_score_gemma":0.00000577812,"domain_scores_codex":[0.9991457,0.00002089012,0.00023165,0.0002079707,0.0002092982,0.0001844407],"domain_scores_gemma":[0.9988354,0.00010608,0.000255289,0.0006110588,0.0001532982,0.00003882502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003817562,0.00002432143,0.000868844,0.000006546131,0.000004628955,0.000001306524,0.0001790471,0.00004400243,0.5044346,0.008847532,0.0000429686,0.4855081],"study_design_scores_gemma":[0.001810756,0.0009722893,0.1831984,0.0003718423,0.00004714077,0.001403015,0.00009423672,0.2930793,0.4666704,0.02805566,0.02369092,0.0006059645],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3686124,0.000152257,0.6305982,0.0004475866,0.0000817084,0.00007076423,0.000009424985,0.00001283834,0.00001484425],"genre_scores_gemma":[0.943011,0.0004087977,0.05640476,0.0000816153,0.00007636134,0.000001730157,5.824994e-8,0.000002779751,0.00001293703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5743986,"threshold_uncertainty_score":0.4036471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04567491940861173,"score_gpt":0.3052709651566672,"score_spread":0.2595960457480554,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}