---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- finance
size_categories:
- 1K<n<10K
---
# Dataset Card for Financial Fraud Labeled Dataset

<!-- Provide a quick summary of the dataset. -->

## Dataset Details

This dataset collects financial filings from various companies submitted to the U.S. Securities and Exchange Commission (SEC). The dataset consists of 85 companies involved in fraudulent cases and an equal number of companies not involved in fraudulent activities. The Fillings column includes information such as the company's MD&A, and financial statement over the years the company stated on the SEC website.

This dataset was used for research in detecting financial fraud using multiple LLMs and traditional machine-learning models.

- **Curated by:** [Amit Kedia](https://www.linkedin.com/in/theamitkedia/)
- **Language(s) (NLP):** English
- **License:** Apache 2.0

### Dataset Sources

- **Repository:** [GitHub](https://github.com/amitkedia007/Financial-Fraud-Detection-Using-LLMs)
- **Thesis:** [Financial Fraud Detection using LLMs](https://github.com/amitkedia007/Financial-Fraud-Detection-Using-LLMs/blob/main/Detailed_Report_on_financial_fraud_detection.pdf)




### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

#### Code to Directly use the dataset:

from datasets import load_dataset

dataset = load_dataset("amitkedia/Financial-Fraud-Dataset")



### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
There are some limitations of the dataset:
1. This dataset is designed for acedemic research 
2. The text needs to be cleaned for further process
3. The dataset does not cover all the fradulent cases and are limited to Securities and Exchange Commision of USA (SEC) that means the fradulent and non fradulent cases are the companies of USA

## Dataset Structure

For the structure of the dataset look into the dataset viewer. 

## Dataset Creation

Check out the Thesis

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

To help the financial industry develop the best model to detect fraudulent activities which can save billions of dollars for government and banks 

#### Data Collection and Processing

Please Refer to the Thesis


## Dataset Card Authors

[Amit Kedia](https://www.linkedin.com/in/theamitkedia/)