Abstract: A system and method for initiating a probabilistic rule engine (PRE) is presented. The method includes: receiving a plurality of data records, including a data input and output; assigning a weight to each of a plurality of rules implemented using a Boolean logic operator; applying each rule to each data input to generate a plurality of results, each result corresponding to a rule; generating for each data input a second output, based on the plurality of results corresponding to applying the plurality of rules on the data input; determining an objective function; generating an error value based on each output and each second output; adjusting a weight value of a first rule in response to determining that the error value is above a predetermined threshold; and processing a new data input on the PRE, in response to determining that the error value is below the predetermined threshold.

Abstract: A method and system for detecting and correcting of a statistical bias are provided. The method comprising generating a plurality of outputs by a probabilistic rule engine (PRE), each of the plurality of outputs corresponding to an input vector; selecting a group of outputs from the plurality of outputs; determining by the PRE at least one rule, the at least one rule having an impact as measured by a weight of the at least one rule on the output of the group of outputs; and generating a list of the at least one rule sorted by the weight of the at least one rule.

Abstract: Decision engines are deployed in a variety of fields, from medical diagnostics to financial applications such as lending. Typically, solutions involve rule engines or artificial intelligence (AI) to assist in making a decision based on transactional data. However, rules can be complicated to maintain and conflicting, and AI does not offer transparency which is required for example, in many applications of the financial industry. The proposed solution discussed herein includes a rule engine which processes transactions based on weighted rules. The system is trained from a training transaction set. In some embodiments, the system may mine the training transaction set for rules, while in other embodiments the rules may be predefined and assigned weights by the system.