# Correspondence Analysis of Government Expenditure Patterns

## TL;DR

This paper introduces a new dataset and neural network approach to analyze and visualize government expenditure patterns, aiming to enhance transparency and inspire ML methods in governance, especially in developing countries.

## Contribution

It provides a novel dataset benchmark and a neural network-based method for analyzing government expenses, addressing a gap in ML applications for transparency.

## Key findings

- Created a large, publicly available expense dataset
- Developed a neural network approach for outlier detection
- Enhanced visualization of expenditure patterns

## Abstract

We analyze expenditure patterns of discretionary funds by Brazilian congress members. This analysis is based on a large dataset containing over $7$ million expenses made publicly available by the Brazilian government. This dataset has, up to now, remained widely untouched by machine learning methods. Our main contributions are two-fold: (i) we provide a novel dataset benchmark for machine learning-based efforts for government transparency to the broader research community, and (ii) introduce a neural network-based approach for analyzing and visualizing outlying expense patterns. Our hope is that the approach presented here can inspire new machine learning methodologies for government transparency applicable to other developing nations.