DOI: 10.7910/dvn/tj6rmq
title: Replication Data for: Alternative Datasets for Credit Scoring of Thin File Consumers
title: Non-Traditional Data Sources to Enhance Creditworthiness
title: Innovative Data Approaches for Assessing Credit Risk in Limited Credit History Consumers

description: Credit scoring is essential in financial services, allowing institutions to assess consumers' creditworthiness. Traditional credit scoring models heavily rely on extensive transaction history, which often poses a significant challenge for thin-file consumers—individuals with limited credit history. This comprehensive review aims to explore and evaluate various alternative datasets that can be utilised to improve credit scoring for thin-file consumers. By moving beyond traditional transaction profiles, alternative datasets such as social media data, web browsing behaviour, digital footprints, and telecom data offer new dimensions to assess consumer credit risk. Additionally, the review compares the effectiveness of various machine learning algorithms, including support vector machines, neural networks, decision trees, random forests, and hybrid models, in leveraging these datasets for credit scoring. The findings indicate that integrating multiple alternative data sources with advanced machine learning algorithms can significantly improve the accuracy and reliability of credit risk assessments. The comparative analysis of machine learning algorithms used in credit scoring highlights the strengths and limitations of different approaches. Support vector machines (SVM), neural networks, decision trees, random forests, and hybrid models have all shown varying degrees of success in utilising alternative datasets for credit scoring. Hybrid models combine multiple machine learning techniques and are particularly effective in leveraging diverse data sources to provide a robust credit risk assessment. This review underscores the potential of alternative datasets in revolutionizing credit scoring for thin-file consumers. By incorporating new data dimensions and advanced machine learning algorithms, researchers can improve their ability to assess credit risk accurately. Future researchers may continue to refine these models and explore new alternative datasets to enhance credit scoring models using machine learning algorithms.
description: Python, 3
description: Dataset Overview - It contains information about various research papers related to alternative data sources for credit scoring.
1. Content of the Dataset:

Authors and Year: Lists the authors of the papers and the year of publication.
Title: Title of each research paper.
Research Objectives: The goals or aims of the research described in each paper.
Datasets Used: Types of datasets utilized in the research.
Machine Learning Algorithms Employed: The algorithms used in the study.
Key Findings: Major conclusions or results of the research.
Citations: Links or references to the research papers.
2. Data Categories:

The dataset includes a variety of alternative data categories used in credit scoring, such as social media data, web browsing behavior, digital footprints, telecom data, and hybrid data approaches.
3. Purpose of the Data:

To categorize and assess the usage of different alternative datasets in credit scoring research for thin-file consumers. This data is used to analyze trends and prevalence of specific data sources in the context of credit scoring.
4. Visualization:

The bar chart visualizes the distribution of these alternative data categories across the research papers. It helps to understand which data types are most frequently explored and their relevance in credit scoring models.
5. Analysis of Data:

The dataset is organized to show the frequency of different categories of alternative data used in various studies. This analysis helps identify popular data sources and trends in research related to credit scoring.
6. Structure of Data:

The data is structured into a table format with columns for serial number, authors and year, title, research objectives, datasets used, machine learning algorithms employed, key findings, and citations.
7. Potential Uses:

This dataset can be used for literature reviews, identifying research gaps, and understanding the impact of various alternative datasets on credit scoring models.
8. Insights:

The dataset provides insights into how different types of alternative data are used and evaluated in credit scoring research. It highlights the increasing interest in integrating diverse data sources to improve the accuracy and effectiveness of credit scoring models.