Abstract: A fraud detection system including at least one processor configured to: calculate a fraud level of a user who uses a service based on a behavior of the user; obtain a determination result as to whether the behavior is actually fraudulent; and calculate accuracy of fraud detection for each user based on the fraud level and the determination result.

Abstract: Provided is a call subject determination system including at least one processor configured to: predict, for each of a plurality of call candidates, a return call probability, which is a probability of the call candidate returning a phone call instead of answering the phone call; acquire a call taker constraint, which is a constraint in terms of capacity of a call taker who takes the returned phone call; and determine, from the plurality of call candidates, a plurality of call subjects to whom phone calls are to be made so that the call taker constraint is satisfied, based on the return call probability of the each of the plurality of call candidates.

Abstract: Provided is a call subject determination system including at least one processor configured to: predict, for each of a plurality of call candidates, a talk probability, which is a probability of succeeding in holding a phone talk with the call candidate; predict, for each of the plurality of call candidates, an advantageous result to be gained from the phone talk with the call candidate; acquire a talker constraint, which is a constraint in terms of capacity of a talker; and determine, from among the plurality of call candidates, a plurality of call subjects to whom phone calls are to be made so that the talker constraint is satisfied and great advantageous results are gained from the plurality of call subjects on the whole, based on the talk probability and the advantageous result of the each of the plurality of call candidates.

Abstract: Relevance information obtaining means of a fraud estimation system is configured to obtain relevance information about relevance between one service and another service. Comparison result obtaining means is configured to obtain a comparison result of a comparison between user information of a target user in the one service and user information of a fraudulent user or an authentic user in the another service. Estimation means is configured to estimate fraudulence of the target user based on the relevance information and the comparison result.

Abstract: A fraud detection system for executing predetermined processing when a detection target is detected by using a detection device, the fraud detection system comprising at least one processor which determines, before the detection target is detected, whether a predetermined action has been performed by a user having a user terminal; executes, when it is determined that the predetermined action has been performed, fraud detection on the user based on identification information stored in the user terminal; and executes, when the detection target is detected, the predetermined processing based on an execution result of the fraud detection.

Abstract: Disclosed herein is an information processing apparatus, comprising: a memory configured to store program code; and electric circuitry including a processor, the processor being configured to read and operate according to the program code, the electronic circuitry configured to: set a plurality of candidate values of a parameter tunable in a computer system; trial each of the plurality of set candidate values of the parameter to the computer system for a predetermined period of time or a predetermined number of times and to calculate a reward in the computer system; select, when a trial satisfies a predetermined termination criterion, a candidate value of the parameter for which the maximum reward is calculated among the candidate values of the parameter and apply the selected candidate value of the parameter to the computer system; and re-set candidate values of the parameter based on the selected candidate value of the parameter to repeat.

Abstract: A fraud detection system including at least one processor configured to: calculate a fraud level of a user who uses a service based on a behavior of the user; obtain a determination result as to whether the behavior is actually fraudulent and calculate accuracy of fraud detection for each user based on the fraud level and the determination result.

Abstract: A prediction system, with at least one processor configured to: acquire a plurality of candidates of information influencing an ease of obtaining a desired result in a predetermined service; predict, for each of the plurality of candidates, based on a predetermined prediction method, whether the desired result is obtained; and identify the candidates predicted to obtain the desired result.

Abstract: A fraud detection system includes at least one processor configured to acquire, based on an action performed by each of a plurality of users, a score relating to a fraud level of the user, determine, based on the score of each of the plurality of users, an acquisition method for a feature amount of the user such that an acquisition time of the feature amount becomes shorter as the fraud level becomes lower, acquire the feature amount of each of the plurality of users based on the acquisition method determined for the user, and detect fraud made by each of the plurality of users based on the feature amount of the user.

Abstract: Provided is an estimation system including at least one processor configured to: acquire position information on a position of another location visited by a user who has visited an estimation target location which is a location for which an attribute is to be estimated; acquire a positional relationship between a position of the estimation target location and the position indicated by the position information; and estimate the attribute of the estimation target location based on the positional relationship.

Abstract: A learning model applying system including at least one processor configured to: calculate a plurality of feature amounts based on data for training used for training a learning model; select at least one of the plurality of feature amounts based on an impact of each feature amount on the learning model; apply a trained learning model that is trained based on the selected feature amount, and perform a setting related to calculation of a feature amount to be entered In the trained learning model based an the selected feature amount.

Abstract: Disclosed herein is an information processing apparatus, comprising: a memory configured to store program code; and electric circuitry including a processor, the processor being configured to read and operate according to the program code, the electronic circuitry configured to: set a plurality of candidate values of a parameter tunable in a computer system; trial each of the plurality of set candidate values of the parameter to the computer system for a predetermined period of time or a predetermined number of times and to calculate a reward in the computer system; select, when a trial satisfies a predetermined termination criterion, a candidate value of the parameter for which the maximum reward is calculated among the candidate values of the parameter and apply the selected candidate value of the parameter to the computer system; and re-set candidate values of the parameter based on the selected candidate value of the parameter to repeat.

Abstract: Storage means of a fraud estimation system stores a learning model that has learned a relationship between a comparison result that is a result of comparing user information of a user in one service to user information of a fraudulent user or an authentic user in another service and presence or absence of fraudulence in the one service. Comparison result obtaining means obtains a comparison result that is a result of comparing user information of a target user in the one service and user information of a fraudulent user or an authentic user in the another service. Output obtaining means obtains output from the learning model based on the comparison result. Estimation means estimates fraudulence of the target user based on the output from the learning model.

Abstract: Relevance information obtaining means of a fraud estimation system is configured to obtain relevance information about relevance between one service and another service. Comparison result obtaining means is configured to obtain a comparison result of a comparison between user information of a target user in the one service and user information of a fraudulent user or an authentic user in the another service. Estimation means is configured to estimate fraudulence of the target user based on the relevance information and the comparison result.

Abstract: First classifying means of a learning system performs classification relating to data based on a machine learning model. Second classifying means performs classification relating to the data based on a predetermined rule. Determining means determines whether the rule is to be learned based on statistical information relating to data satisfying the rule. Training means executes training processing of the rule that is determined to be learned by the determining means.

Abstract: Provided are a determination system, a determination method, and a program, which are capable of determining a determination validity of a machine learning model in operation in a timely manner. An unauthorized order determination device executes estimation processing relating to the input data, based on an output produced when a plurality of elements included in input data, on which the estimation processing is to be performed, are input to a first machine learning model. A model generation device executes learning of a second machine learning model based on a part of the plurality of elements included in the input data and known result data of the input data. The model generation device determines a determination validity of the first machine learning model based on an output of the second machine learning model after execution of the learning.

Abstract: Provided are an estimating system, an estimating method, and a program, which are capable of adapting to differences between a situation during learning of a machine learning model and a situation during estimation using the machine learning model. A score value determination device determines a score value of input data based on output produced when the input data is input to a learned model. An evaluation data generation device generates evaluation data of the score value based on known result data. A fraudulent order determination device generates estimation result data indicating a result of the estimation relating to the input data to be estimated, based on a score value determined based on the output produced when the input data to be estimated is input to the learned model, and on the evaluation data.