{'fraction_non_alphanumeric': 0.01644736842105263, 'fraction_numerical': 0.003289473684210526, 'mean_word_length': 5.024752475247524, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The belief‐function approach to aggregating audit evidence

In this article, we present the belief‐function approach to aggregating audit evidence. the approach uses an evidential network to represent the structure of audit evidence. In turn, it allows us to treat all types of dependencies and relationships among accounts and items of evidence, and thus the approach should help the auditor conduct an efficient and effective audit. Aggregation of evidence is equivalent to propagation of beliefs in an evidential network. the article describes in detail the three major steps involved in the propagation process. the first step deals with drawing the evidential network representing the connections among variables and items of evidence, based on the experience and judgment of the auditor. We then use the evidential network to determine the clusters of variables over which we have belief functions. the second step deals with constructing a Markov tree from the clusters of variables determined in step one. the third step deals with the propagation of belief functions in the Markov tree. We use a moderately complex example to illustrate the details of the aggregation process. © 1995 John Wiley & Sons, Inc.