Evidential reasoning system and method

A computerized method for making decisions based on evidential reasoning, said decisions used for risk and credit analysis of financial service applications, said method comprising:

• providing a hierarchical model structure for performing risk and credit analysis of financial service applications, said model structure including at least one input layer of processing nodes, said model further including an output layer having a processing node coupled to each of the processing nodes in the input layer;
• evaluating a respective attribute indicative of a risk factor assigned to each of the plurality of processing nodes;
• specifying a number of possible linguistic evidential values for each of said attributes and wherein some of said evidential values comprise unknown financial information;
• and
• combining the outputs from said processing nodes to reach a decision regarding a given financial service application even in the presence of said unknown information, said combining configured to emulate expert data, wherein said expert data is collected during a learning stage from a plurality of examples for each of the processing nodes, each of the plurality of examples having a set of inputs, including some indicative of unknown financial information, and a corresponding output indicative of an expert opinion.

A computer-readable medium encoded with computer program code for making decisions based on evidential reasoning, said decisions used for risk and credit analysis of financial service applications, the program code causing a computer to execute a method comprising:

• running a hierarchical model structure for performing risk and credit analysis of financial service applications, said model structure including at least one input layer of processing nodes, said model further including an output layer having a processing node coupled to each of the processing nodes in the input layer;
• evaluating a respective attribute indicative of a risk factor assigned to each of the plurality of processing nodes;
• specifying a number of possible linguistic evidential values for each of said attributes and wherein some of said evidential values comprise unknown financial information;
• and
• combining the outputs from said processing nodes to reach a decision regarding a given financial service application even in the presence of said unknown information, said combining configured to emulate expert data, wherein said expert data is collected during a learning stage from a plurality of examples for each of the processing nodes, each of the plurality of examples having a set of inputs, including some indicative of unknown financial information, and a corresponding output indicative of an expert opinion, said model structure configured to provide a plurality of non-overlapping intervals over its possible output space, said model structure being further configured to optimize separation between any adjacent intervals of said possible output space so that its final output unambiguously maps into a single interval of said output space.

A computerized system for making decisions based on evidential reasoning, said decisions used for risk and credit analysis of financial service applications, said system comprising:

• memory configured to store a hierarchical model structure for performing risk and credit analysis of financial service applications, said model structure including at least one input layer of processing nodes, said model further including an output layer having a processing node coupled to each of the processing nodes in the input layer;
• an evaluator module configured to evaluate a respective attribute indicative of a risk factor assigned to each of the plurality of processing nodes;
• a data entry module configured to specify a number of possible linguistic evidential values for each of said attributes and wherein some of said evidential values comprise unknown financial information;
• and
• a processor configured to combine the outputs from said processing nodes to reach a decision regarding a given financial service application even in the presence of said unknown information, said processor configured to emulate expert data, wherein said expert data is collected during a learning stage from a plurality of examples for each of the processing nodes, each of the plurality of examples having a set of inputs, including some indicative of unknown financial information, and a corresponding output indicative of an expert opinion.