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  ## Dataset Details
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- ### Dataset Description
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  - **Curated by:** [Amit Kedia](https://www.linkedin.com/in/theamitkedia/)
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  - **Language(s) (NLP):** English
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  - **License:** Apache 2.0
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- ### Dataset Sources [optional]
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- <!-- Provide the basic links for the dataset. -->
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  - **Repository:** [GitHub](https://github.com/amitkedia007/Financial-Fraud-Detection-Using-LLMs)
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  - **Thesis:** [Financial Fraud Detection using LLMs](https://github.com/amitkedia007/Financial-Fraud-Detection-Using-LLMs/blob/main/Detailed_Report_on_financial_fraud_detection.pdf)
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- ## Uses
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- <!-- Address questions around how the dataset is intended to be used. -->
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  1. This dataset is designed for acedemic research
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  2. The text needs to be cleaned for further process
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  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
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  ## Dataset Structure
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  For the structure of the dataset look into the dataset viewer.
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  <!-- Motivation for the creation of this dataset. -->
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- To help the financial industry to develop best model to detect the fraudulent activities which can save billions of dollars of government and banks
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  #### Data Collection and Processing
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- #### Who are the source data producers?
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- <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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- [More Information Needed]
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- ### Annotations [optional]
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- <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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- #### Annotation process
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- <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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- [More Information Needed]
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- #### Who are the annotators?
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- <!-- This section describes the people or systems who created the annotations. -->
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- [More Information Needed]
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- #### Personal and Sensitive Information
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- <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
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- ## Dataset Card Authors [optional]
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- ## Dataset Card Contact
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- [More Information Needed]
 
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  ## Dataset Details
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+ 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.
 
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+ This dataset was used for research in detecting financial fraud using multiple LLMs and traditional machine-learning models.
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  - **Curated by:** [Amit Kedia](https://www.linkedin.com/in/theamitkedia/)
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  - **Language(s) (NLP):** English
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  - **License:** Apache 2.0
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+ ### Dataset Sources
 
 
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  - **Repository:** [GitHub](https://github.com/amitkedia007/Financial-Fraud-Detection-Using-LLMs)
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  - **Thesis:** [Financial Fraud Detection using LLMs](https://github.com/amitkedia007/Financial-Fraud-Detection-Using-LLMs/blob/main/Detailed_Report_on_financial_fraud_detection.pdf)
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  1. This dataset is designed for acedemic research
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  2. The text needs to be cleaned for further process
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  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
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  ## Dataset Structure
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  For the structure of the dataset look into the dataset viewer.
 
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  <!-- Motivation for the creation of this dataset. -->
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+ To help the financial industry develop the best model to detect fraudulent activities which can save billions of dollars for government and banks
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  #### Data Collection and Processing
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+ Please Refer to the Thesis
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+ ## Dataset Card Authors
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+ [Amit Kedia](https://www.linkedin.com/in/theamitkedia/)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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