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Profile of the Fraudulent Customer

100%
Matuszyk A., Ptak-Chmielewska A.
Bezpieczny Bank
|
2015
|
issue 2 (59)
7-24
EN
When there is an economic downturn, financial crime proliferates and people are more likely to commit fraud. One of the most common frauds is when a loan is secured without any intention of repaying it. Credit crime is a significant risk to financial institutions and has recently led to increased interest in fraud prevention systems. The most important features of such systems are the determinants (warning signals) that allow you to identify potentially fraudulent transactions. The purpose of this paper is to identify warning signals using the following data mining techniques - logistic regression, decision trees and neural networks. Proper identification of the determinants of a fraudulent transaction can be useful in further analysis, i.e. in the segmentation process or assignment of fraud likelihood. Data obtained in this way allows profiles to be defined for fraudulent and non-fraudulent applicants. Various fraud-scoring models have been created and presented.
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O pewnym modelu pozwalającym identyfikować k najbardziej podejrzanych rekordów w zbiorze danych księgowych w procesie wykrywania oszustw finansowych

63%
Baryła M.
Studia Ekonomiczne
|
2014
|
vol. 203
11-18,
EN
Financial frauds lead to the disturbance of a normal development of stock markets. When they appear, the funds are not properly allocated, which has a negative impact on economic growth. In most cases, investors make decisions taking into consideration economic information presented by companies. Financial frauds substantially affect data included in financial statements. For this reason, it seems important to undertake steps aiming at fraud detection. A key role in this issue may be assumed by auditors who are responsible for identifying significant financial irregularities.
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