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Although health care providers have more than 47 million health insurance clients in Brazil, these companies still have processes that are manual. One of these processes is that of the medical audit that performs the analysis of each account with expenses that were incurred by the operator's beneficiary in any hospital, clinic or laboratory. Each account is reviewed to make sure that there is nothing outside the context of a surgery, examination or consultation. The costs of these audits are high and the outcome is varied and according to the auditor's individual knowledge. In addition to being a lengthy process, these bills will only be paid to those who provided the service after the auditors' review which brings dissatisfaction to those awaiting payment. Manual execution of this process is susceptible to failure and fraud. In this context, Machine Learning algorithms are used to assist in the audit process in various areas of knowledge. Thus, this paper presents a survey on the application of Machine Learning algorithms in medical audits, analyzing which algorithms are most used. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to analyze if, in a small capital market such as the Portuguese, the audit expectation gap exits and which are its major components. We have enquired auditors and University accounting and auditing professors regarding several subjects that influence and increase the audit expectation gap. We have also questioned the Portuguese users of financial information regarding their perception of the role of today's auditing, to assay their opinion with studies made in Anglo-Saxon countries. We have reached the conclusion that Portuguese financial statements users' expectations are similar to the ones of bigger capital markets. As a matter of fact, fraud is the main cause for the existence of the audit expectation gap, nevertheless, Portuguese financial statements users also indicate that the actual auditor's report model in Portugal is clearly insufficient to comprehend the objectives and work that auditors have done to reach their conclusions. [ABSTRACT FROM AUTHOR]