It is essential that Visa organizations can distinguish false Mastercard exchanges so clients are not
charged for things that they didn't buy. Such issues can be handled with Data Science and its significance,
alongside Machine Learning, couldn't be more important. This undertaking expects to outline the
demonstrating of an informational collection utilizing AI with Credit Card Fraud Detection. The Credit Card
Fraud Detection Problem incorporates demonstrating past Visa exchanges with the information of the ones
that ended up being extortion. This model is then used to perceive if another exchange is fake. Our target here
is to identify 100% of the fake exchanges while limiting the off base misrepresentation arrangements. Charge
card Fraud Detection is an average example of arrangement. In this cycle, we have zeroed in on examining
and pre- preparing informational indexes just as the sending of numerous irregularity discovery calculations,
for example, Local Outlier Factor and Isolation Forest calculation on the PCA changed Credit Card
Transaction
SummaryThis paper focuses on fraud detection in surveys using Socio-Economic Panel (SOEP) data as an example for testing newly methods proposed here. A statistical theorem referred to as Benford’s Law states that in many sets of numerical data, the significant digits are not uniformly distributed, as one might expect, but adhere to a certain logarithmic probability function. In order to detect fraud, we derive several requirements that should, according to this law, be fulfilled in the case of survey data.We show that in several SOEP subsamples, Benford’s Law holds for the available continuous data. For this analysis, we developed a measure that reflects the plausibility of the digit distribution in interviewer clusters. We are thus able to demonstrate that several interviews that were known to have been fabricated and therefore deleted in the original user data set can now be detected using this method. Furthermore, in one subsample, we use this method to identify a case of an interviewer falsifying ten interviews not previously detected by the fieldwork organization.
Abstract Is Benford’s law a good instrument to detect fraud in reports of statistical and scientific data? For a valid test, the probability of ‘false positives’ and ‘false negatives’ has to be low. However, it is very doubtful whether the Benford distribution is an appropriate tool to discriminate between manipulated and non-manipulated estimates. Further research should focus more on the validity of the test and test results should be interpreted more carefully.
Benford’s Law relies on a recently proven mathematical distribution about the frequencies of naturally occurring numbers that can be efficiently applied to the detection of financial fraud. Despite the value of Benford’s Law for detecting fraud, most financial professionals are often unaware of its existence and how to best utilise the method for fraud detection. The purpose of this paper is therefore to present a systematic methodology for incorporating Benford’s Law for detecting and flagging potentially fraudulent financial transactions, that can be further investigated. This paper describes the development of Benford’s Law and demonstrates how it can be implemented systematically through a spreadsheet program to detect potential fraud. Given that the cost of financial fraud is significant with firms losing up to a tenth of their revenues, the methodology presented in this paper for implementing Benford’s Law can be a valuable tool for auditors and other financial professionals for detecting fraud.
In this article, we study the applicability of Benford’s law and Zipf’s law to national COVID-19 case figures with the aim of establishing guidelines upon which methods of fraud detection in epidemiology, based on formal statistical analysis, can be developed. Moreover, these approaches may also be used in evaluating the performance of public health surveillance systems. We provide theoretical arguments for why the empirical laws should hold in the early stages of an epidemic, along with preliminary empirical evidence in support of these claims. Based on data published by the World Health Organization and various national governments, we find empirical evidence that suggests that both Benford’s law and Zipf’s law largely hold across countries, and deviations can be readily explained. To the best of our knowledge, this paper is among the first to present a practical application of Zipf’s law to fraud detection.
Aim: The explosion of data based technology has accelerated pattern mining. However, it is clear that quality and bias of data impacts all machine learning and modeling. Results & methodology: A technique is presented for using the distribution of first significant digits of medicinal chemistry features: log P, log S, and p Ka. experimental and predicted, to assess their following of Benford's law as seen in many natural phenomena. Conclusion: Quality of data depends on the dataset sizes, diversity, and magnitudes. Profiling based on drugs may be too small or narrow; using larger sets of experimentally determined or predicted values recovers the distribution seen in other natural phenomena. This technique may be used to improve profiling, machine learning, large dataset assessment and other data based methods for better (automated) data generation and designing compounds.
Benford's Law as an Instrument for Fraud Detection in Surveys Using the Data of the Socio-Economic Panel (SOEP)