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1. WO2020065400 - BELOW-THE-LINE THRESHOLDS TUNING WITH MACHINE LEARNING

Publication Number WO/2020/065400
Publication Date 02.04.2020
International Application No. PCT/IB2019/001055
International Filing Date 27.09.2019
IPC
G06Q 20/40 2012.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
38Payment protocols; Details thereof
40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
G06Q 40/00 2012.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
40Finance; Insurance; Tax strategies; Processing of corporate or income taxes
CPC
G06K 9/6262
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
6262Validation, performance evaluation or active pattern learning techniques
G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G06Q 20/4016
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
38Payment protocols; Details thereof
40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
401Transaction verification
4016involving fraud or risk level assessment in transaction processing
G06Q 40/00
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
40Finance; Insurance; Tax strategies; Processing of corporate or income taxes
G06Q 40/02
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
40Finance; Insurance; Tax strategies; Processing of corporate or income taxes
02Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking
Applicants
  • ORACLE FINANCIAL SERVICES SOFTWARE LIMITED [IN]/[IN]
Inventors
  • CAI, Jian
  • MATHEW, Sunil, J.
Priority Data
16/145,95228.09.2018US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) BELOW-THE-LINE THRESHOLDS TUNING WITH MACHINE LEARNING
(FR) RÉGLAGE DE SEUILS EN DESSOUS DE LA LIGNE AVEC APPRENTISSAGE AUTOMATIQUE
Abstract
(EN)
Systems, methods, and other embodiments associated with applying machine learning to below-the-line threshold tuning are described. In one embodiment, a method includes selecting a set of sampled events and labeling each event in the set of sampled events as either suspicious or not suspicious. Then, a machine learning model to calculate for a given event a probability that the given event is suspicious is built based on the set of sampled events. The machine learning model is trained, and its calibration validated. Based on probabilities calculated by the machine learning model, a scenario and segment combination to be tuned is determined. A tuned threshold value is generated, and an alerting engine is adjusted with the tuned parameter to reduce errors by the alerting engine in classifying events as not suspicious.
(FR)
La présente invention concerne des systèmes, des procédés et d'autres modes de réalisation associés à l'application d'un apprentissage automatique à un réglage de seuil en dessous de la ligne. Dans un mode de réalisation, un procédé consiste à sélectionner un ensemble d'événements échantillonnés et à marquer chaque événement dans l'ensemble d'événements échantillonnés comme suspect ou non suspect. Ensuite, un modèle d'apprentissage automatique permettant de calculer, pour un événement donné, une probabilité que l'événement donné soit suspect, est construit sur la base de l'ensemble des événements échantillonnés. Le modèle d'apprentissage automatique est entraîné, et son étalonnage est validé. Sur la base de probabilités calculées par le modèle d'apprentissage automatique, un scénario et une combinaison de segments à régler sont déterminés. Une valeur de seuil réglée est générée, et un moteur d'alerte est ajusté à l'aide du paramètre réglé pour réduire les erreurs par le moteur d'alerte dans la classification d'événements comme non suspects.
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