AI-Powered Data Processing for Advanced Case Investigation Technology

Abstract

Investigative technology has advanced significantly across industries with the introduction of AI-
powered data processing into case investigations. The purpose of this study is to determine how
predictive analysis and dataset analysis enabled by artificial intelligence can improve the
effectiveness and accuracy of investigative processes. Investigators may concentrate on
complicated tasks and strategic decision-making by using AI's ability to swiftly evaluate large
datasets and find minor correlations, which helps in the detection of fraudulent or illegal activity.
This research attempts to determine the best accurate and dependable models for forecasting the
outcomes of crimes by analyzing and contrasting the performance of machine learning models
such as Gaussian Naive Bayes, Decision Tree Classifier, and Random Forest Classifier.
Furthermore, by using methods like cross-validation and hyperparameter adjustment, the research
minimizes overfitting problems and improves model performance. In addition to highlighting the
advantages and difficulties of using AI in case investigations, the study also emphasizes how
speed, accuracy, and resource allocation can all be improved. The research is heavily reliant on
ethical factors, such as data privacy and bias reduction. The results highlight a paradigm change
in the methods of investigation, improving the legal, corporate security, and law enforcement
domains' capacity to deal with complicated cases. By offering a thorough analysis of machine
learning models and showcasing the revolutionary potential of AI in case investigation technology,
this study advances the discipline.