INFORMATION PROCESSING METHOD, LEARNING MODEL GENERATION METHOD, INFORMATION PROCESSING DEVICE, AND COMPUTER PROGRAM

• Main Authors: WAKI TAKASHI, SUDO KENTARO
• Format: Patent
• Language: English; Japanese
• Published: 11.04.2022
• Subjects: CALCULATING; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

To provide an information processing method, a learning model generation method, an information processing device, and a computer program that can be expected to accurately determine the presence or absence of an abnormality related to a monetary transaction of a person to be watched over.SOLUTION: The information processing method according to the present embodiment is an information processing method for determining the presence or absence of an abnormality related to a subject person's monetary transaction, acquires subject person's monetary transaction information using a trained learning model in which an information processing device accepts time-series monetary transaction information as an input and outputs a feature amount related to a monetary transaction, inputs the acquired time-series monetary transaction information to the learning model, acquires the feature amount output by the learning model, calculates an evaluation value related to the acquired feature amount based on a distribution of the feature amount acquired in the past, and determines the presence or absence of any abnormality related to the monetary transaction of the subject person based on the calculated evaluation value.SELECTED DRAWING: Figure 1 【課題】見守り対象者の金銭取引に係る異常性の有無を精度よく判定することが期待できる情報処理方法、学習モデルの生成方法、情報処理装置及びコンピュータプログラムを提供する。【解決手段】本実施の形態に係る情報処理方法は、対象者の金銭取引に係る異常性の有無を判定する情報処理方法であって、情報処理装置が、時系列の金銭取引情報を入力として受け付けて金銭取引に係る特徴量を出力するよう訓練された学習モデルを用いて、前記対象者の金銭取引情報を取得し、取得した時系列の前記金銭取引情報を前記学習モデルへ入力し、当該学習モデルが出力する特徴量を取得し、取得した特徴量に関する評価値を、過去に取得した特徴量の分布に基づいて算出し、算出した評価値に基づいて、前記対象者の金銭取引に係る異常性の有無を判定する。【選択図】図１