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One of the important functions realized by a credit scoring system is information processing. Every information processing system includes:
input information
module of processing information
output information
In the case of credit scoring system input, information consists of:
registry information
financial information (financial statements)
benchmarking information
inner information of the bank on creditability of the customer
The outcome of the processing information is a symbol of credit risk class relevant to the level of risk of the transaction.
The information on the risk level can be helpful in such decision problems as:
granting or denying a credit
defining terms of credit
scope and frequency of monitoring a customer's financial position
As the scope of input information is defined, the credibility of the outcome results, and costs of the processing (e.g., time, involved resources) are closely related to the processing technique.
In this chapter, we present two techniques of information processing in the credit scoring system:
traditional credit-scoring method ("The Scope of the Economic-Financial Analysis of an Enterprise")
using Bayesian belief network ("Credit Scoring System—The Bayesian Network Implementation")
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