A Statistical Method for Corrupt Agents Detection

TL;DR

This paper introduces a statistical approach combining PCA, linear regression, and Shannon information to identify hidden corrupt agents from financial activity data, providing a new tool for corruption detection.

Contribution

It develops a novel statistical method that integrates PCA, regression, and information theory to detect covert corrupt agents in financial datasets.

Findings

Effective identification of hidden corrupt agents demonstrated

Algorithm for covariance matrix with missing data proposed

Application to real financial data shows promising results

Abstract

The statistical method is used to identify the hidden leaders of the corruption structure. The method is based on principal component analysis (PCA), linear regression, and Shannon information. It is applied to study the time series data of corrupt financial activity. Shannon's quantity of information is specified as a function of two arguments: a vector of hidden corruption factors and a subset of corrupt agents. Several optimization problems are solved to determine the contribution of corresponding corrupt agents to the total illegal behavior. An illustrative example is given. A convenient algorithm for computing the covariance matrix with missing data is proposed.