{"id":"W4412586016","doi":"10.5772/intechopen.1011529","title":"Innovative Approaches to Counterparty Credit Risk Management: Machine Learning Solutions for Robust Backtesting","year":2025,"lang":"en","type":"book-chapter","venue":"Business, management and economics","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Credit risk; Counterparty; Credit valuation adjustment; Computer science; Business; Risk analysis (engineering); Actuarial science; Credit reference","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003578226,0.001296748,0.001054767,0.001291431,0.0005223864,0.003821816,0.002308658,0.001912025,0.005541483],"category_scores_gemma":[0.008867951,0.0006273836,0.0009135585,0.001623316,0.002193318,0.003987219,0.002166431,0.003924911,0.001575412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001603862,"about_ca_system_score_gemma":0.001277535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002028996,"about_ca_topic_score_gemma":0.001900496,"domain_scores_codex":[0.9984564,0.0005250544,0.00008276403,0.0002793358,0.0005747382,0.00008179808],"domain_scores_gemma":[0.9965233,0.002260723,0.0002570992,0.0004279464,0.000447227,0.00008366426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004535788,0.0000969976,0.001316613,0.0003162884,0.0001125614,0.0001330129,0.0002884898,0.2253081,0.001300798,0.330551,0.01440885,0.4261219],"study_design_scores_gemma":[0.000009847636,0.00005186928,0.0005399375,0.0001876734,0.00002568952,0.0001087045,0.00009402001,0.5101723,0.001327905,0.4536993,0.03374094,0.00004178065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00310353,0.007092749,0.9700744,0.002613705,0.0002663244,0.00004839506,0.0000876486,0.0005944267,0.01611887],"genre_scores_gemma":[0.3070591,0.02035304,0.6340804,0.001780432,0.001769779,0.0002939948,0.0004969321,0.0005556226,0.03361083],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005541483,"threshold_uncertainty_score":0.0189237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1083704960324038,"score_gpt":0.198086077165084,"score_spread":0.08971558113268023,"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."}}