Abstract

The paper explores how statistical analysis and machine learning can be used to identify the fraud patterns in the police reports. The study aims at establishing the most important predictive factors and indicators distinguishing fraudulent and valid cases with the use of structured data of police databases. The work was done in the background of the increase in financial fraud instances and the rising necessity of the introduction of automated detection systems in police departments. Police reports of the pastwere mined down to data and analyzed on SPSS 1, to carry out statistical operations. The sample was structured data which covered the financial transactions, demographic and behavioral pattern. It was based on the use of descriptive statistics, chi-square, ANOVA, and regression analysis as well as machine learning predictive models. Itwas found that financial indicators, especially, the amount and frequency of transactions, proved to be the most important predictors of fraud. Categorical variables such as transaction method and geographical location demonstrated that they were strongly associated with the fraud activities. The best classification level was accepted to be 0.6 probability where F1-score was at 0.8612 with a precision of 80.5% and recall of 92.7%. The paper finds that conventional statistical tools and machine learning present a solid framework of detecting fraud within the law enforcement setting. The results indicate that structured information itself has moderate predictive capabilities, saying that their functionality could be improved significantly by combining it with unstructured source of data. As a practice recommendation, the study suggests using automated monitoring protocols that target the specified key indicators and optimizing the best probability level to designate cases. Future studies are advised to examine how to combine the use of Natural Language Processing to analyze unstructured reports text in a way to assess the temporal variability of fraudulent behavior as well as to involve cross-jurisdictional comparative research to establish more empirically generalized detection frameworks.

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Campus

RIT Dubai

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