{"id":"W4414056112","doi":"10.2139/ssrn.5402503","title":"AI Agents for Cash Management in Payment Systems","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Digital Platforms and Economics","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Payment; Market liquidity; Cash; Cash management; Liquidity risk; Cash flow forecasting; Payment system; Database transaction","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001576694,0.0003500135,0.0004507776,0.0006179769,0.0001420521,0.001818498,0.0006301628,0.0001786587,0.00001390702],"category_scores_gemma":[0.00001542255,0.0003346767,0.0002621908,0.0001476662,0.00001440694,0.001276507,0.0006817211,0.001541189,0.00005958421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001732136,"about_ca_system_score_gemma":0.0004882355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004788513,"about_ca_topic_score_gemma":0.001440715,"domain_scores_codex":[0.9966118,0.00000228247,0.0007009343,0.0004228701,0.0001904901,0.002071679],"domain_scores_gemma":[0.9991266,0.00001514882,0.0004685067,0.0002820264,0.00009062765,0.00001714602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008661616,0.0001309869,0.0007383388,0.0009247217,0.0005586165,0.00001238921,0.00001306258,0.01139017,2.249493e-7,0.9496385,0.00479978,0.03170658],"study_design_scores_gemma":[0.001431817,0.00002284642,0.0002324245,0.0004750272,0.0001681958,0.00001323468,0.001532896,0.01192474,4.7669e-7,0.8477091,0.1359747,0.0005144562],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7438554,0.009180966,0.0324115,0.004856262,0.02077134,0.009188412,0.00009637041,0.0003556435,0.1792841],"genre_scores_gemma":[0.9764444,0.00328232,0.00005221142,0.002082167,0.002126772,0.0002726302,0.0001896667,0.00006440005,0.0154854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2325891,"threshold_uncertainty_score":0.9999105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01709390069032156,"score_gpt":0.24309467030273,"score_spread":0.2260007696124085,"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."}}