1) Data Introduction • The (Cleaned) Credit Score Dataset for Classification Dataset is a structured dataset designed for training machine learning models to classify individuals into credit score categories based on various credit-related attributes.

2) Data Utilization (1) Characteristics of the (Cleaned) Credit Score Dataset for Classification Dataset: • The dataset includes key financial variables that influence credit scoring, such as delinquency history, credit limit, credit utilization ratio, and repayment records. The credit score category serves as the multiclass classification label.

(2) Applications of the (Cleaned) Credit Score Dataset for Classification Dataset: • Credit score classification model training: The dataset can be used to train machine learning models that predict an individual’s credit score category based on financial indicators. • Financial risk assessment and customer segmentation: It can support tasks such as loan approval decision-making, interest rate setting, and personalized financial product recommendations by identifying a customer’s credit level in advance.

Dataset can be used to train models for credit scoring.

Different files can help you with different kinds of information about clients. Check descriptions in .xlsx file to prepare your dataset. Also check dependencies in .jpg file.

This statistic presents the distribution of credit scores in the United States in 2015, by age. In that year, 28 percent of Americans, aged 30 or below, had an average credit score less than 621.

Credit Score, Credit Report, And Credit Check Services Market size was valued at USD 20.02 Billion in 2024 and is projected to reach USD 158.12Billion by 2031, growing at a CAGR of 25.13% during the forecast period 2024-2031.

Credit Score, Credit Report, And Credit Check Services Market Drivers

Increasing Consumer Awareness: Growing awareness among consumers about the importance of credit scores and reports in financial decisions drives the demand for these services.

Regulatory Environment: Changes in regulatory frameworks and compliance requirements influence the market dynamics for credit reporting agencies and related service providers.

Lending Practices: Evolution in lending practices, including the rise of digital lending platforms, increases the need for accurate and timely credit information.

Financial Inclusion Initiatives: Efforts to promote financial inclusion globally contribute to the expansion of credit reporting and scoring services into underserved markets.

Technological Advancements: Adoption of advanced analytics, machine learning, and AI technologies improves the accuracy and efficiency of credit scoring models and reporting processes.

Risk Management: Enhanced focus on risk management by financial institutions and businesses fuels the demand for comprehensive credit assessment services.

Consumer Credit Behavior: Changes in consumer credit behavior and spending patterns influence the demand for credit monitoring and reporting services.

Emerging Markets: Increasing penetration of credit reporting services in emerging markets due to economic growth and rising consumer credit activities.

Identity Theft and Fraud Prevention: Heightened concerns about identity theft and fraud drive the demand for monitoring and alert services associated with credit reports.

Strategic Partnerships and Mergers: Partnerships between credit bureaus, financial institutions, and technology companies to enhance service offerings and market reach.

As of March 2020, *** percent of Americans lived in the Midwest and had a score value of A, which corresponds to traditional credit score of at least ***. The most frequently observed score values were C in the Midwest, Northeast, and West, while it was F in the South. This proprietary data from Infutor shows the credit-worthiness of consumers. They utilized ***** proprietary demographic, psychographic, attitudinal, econometric and summarized credit attributes to build the GeoCredit Score database. GeoCredit scores ranges from A (highest traditional score value) to T (lowest traditional score value).

Graph and download economic data for Large Bank Consumer Mortgage Balances: Current Credit Score: 25th Percentile (RCMFLBSCOREPCT25) from Q3 2012 to Q1 2025 about score, FR Y-14M, large, percentile, balance, credits, mortgage, consumer, banks, depository institutions, and USA.

The rapid evolution of machine learning (ML) offers transformative potential for the credit scoring industry, especially in addressing the challenges faced by "thin-file" consumers who lack substantial credit histories. Traditional credit scoring models often fail to accurately assess these consumers due to insufficient data, leading to potential exclusion from crucial credit services. This research leverages a synthetically created dataset, generated using advanced Python libraries like Pandas, NumPy, and Faker, to develop and refine ML algorithms capable of evaluating such underserved consumer segments. The synthetic nature of the dataset ensures compliance with privacy norms while allowing the simulation of diverse consumer behaviors—from stable to erratic financial activities—typical of thin-file profiles. This initiative not only drives innovation in algorithmic credit scoring but also aligns with broader objectives of financial inclusivity, aiming to bridge service gaps by equipping the financial industry with tools to fairly evaluate creditworthiness across all consumer segments. Thus, this dataset forms a critical cornerstone for advancing research that enhances technical capabilities and fosters societal progress through improved financial inclusion.

As of August 2019, 42 percent of Americans aged 55 to 59 had a geo-credit score in bucket 1, which corresponds to traditional credit score of at least 740. The share in this bucket tends to increase with age, suggesting that aging and increases in credit scores are correlated. This proprietary data from Infutor shows the credit-worthiness of consumers. They utilized 1,500 proprietary demographic, psychographic, attitudinal, econometric and summarized credit attributes to build the GeoCredit Score database. GeoCredit scores ranges from A (higest traditional score value) to T (lowest traditional score value).

Graph and download economic data for Large Bank Consumer Mortgage Originations: Original Credit Score: 10th Percentile (RCMFLOSCOREPCT10) from Q3 2012 to Q4 2024 about score, FR Y-14M, origination, large, percentile, credits, mortgage, consumer, banks, depository institutions, and USA.

To determine the creditworthiness of a borrower, financial institutions often use credit scores. Credit scores are based on the credit report of the individual: the higher the score, the lower the risk that debtors fall into arrears. In Canada, a fair credit score is between *** and ***, whereas if it's below ***, it is considered poor. As of the third quarter of 2023, approximately *** percent of mortgage holders in Canada had a fair or poor credit score.

Graph and download economic data for Net Percentage of Large Domestic Banks Increasing the Minimum Required Credit Score for Credit Card Loans (SUBLPDCLCTRLGNQ) from Q2 2001 to Q2 2025 about score, credit cards, large, credits, domestic, Net, percent, loans, banks, depository institutions, and USA.

The global credit risk rating software market size was valued at USD 1.5 billion in 2025 and is projected to grow at a CAGR of 12.5% from 2025 to 2033, reaching USD 4.2 billion by 2033. The market growth is attributed to the increasing adoption of cloud-based solutions, the need for accurate and timely credit risk assessments, and the growing demand for automation and efficiency in credit risk management processes. The market for credit risk rating software is segmented by type, application, and region. By type, the on-premise segment is expected to hold the largest market share during the forecast period. However, the cloud segment is projected to grow at the highest CAGR during the forecast period. By application, the large enterprise segment is expected to hold the largest market share during the forecast period. However, the small business segment is projected to grow at the highest CAGR during the forecast period. By region, North America is expected to hold the largest market share during the forecast period. However, the Asia Pacific region is projected to grow at the highest CAGR during the forecast period. Credit risk rating software is a software tool that helps financial institutions assess the creditworthiness of potential borrowers. The software uses a variety of factors to calculate a credit score, which can then be used to make lending decisions.

Blockchain Credit Scoring Market Outlook

According to our latest research, the global blockchain credit scoring market size in 2024 stands at USD 687 million, with a robust year-over-year growth trajectory. The market is projected to expand at a CAGR of 38.2% during the forecast period, reaching a forecasted value of USD 8.3 billion by 2033. This exponential growth is primarily driven by the increasing demand for transparent, secure, and decentralized credit scoring solutions among financial institutions, fintech companies, and emerging digital lenders. As per our analysis, the adoption of blockchain technology is fundamentally transforming the credit assessment landscape, enabling more accurate risk profiling, enhanced fraud prevention, and greater access to credit for underbanked populations worldwide.

One of the primary growth factors for the blockchain credit scoring market is the increasing need for enhanced transparency and security in credit risk assessment. Traditional credit scoring systems are often plagued by data inconsistencies, lack of real-time updates, and vulnerabilities to data breaches. Blockchain technology addresses these challenges by providing an immutable and distributed ledger system, which ensures that all credit-related data is securely stored, verifiable, and accessible only to authorized parties. This not only reduces the risk of fraud and identity theft but also promotes trust among lenders and borrowers. Furthermore, regulatory pressures for more robust data governance and compliance are compelling financial institutions to adopt blockchain-based solutions, further accelerating market growth.

Another significant driver is the rapidly expanding digital lending ecosystem, especially in emerging economies where traditional credit infrastructure is either underdeveloped or inaccessible to large segments of the population. Blockchain credit scoring platforms enable lenders to incorporate alternative data sources, such as utility payments, mobile phone usage, and social media activity, into their credit assessment models. This approach democratizes access to credit by providing more accurate risk profiles for individuals and small businesses with limited or no formal credit history. The integration of smart contracts and decentralized identity verification mechanisms further streamlines the lending process, reducing operational costs and turnaround times for credit approvals.

The growing collaboration between fintech companies, banks, and technology providers is also fueling the adoption of blockchain credit scoring solutions. These partnerships are facilitating the development of interoperable platforms that can seamlessly integrate with existing financial systems, enhancing scalability and user experience. Additionally, the rise of decentralized finance (DeFi) and peer-to-peer lending platforms is creating new opportunities for blockchain-based credit scoring models, which can operate without the need for centralized intermediaries. This shift toward decentralized credit infrastructure is expected to drive innovation and competition, ultimately benefiting both lenders and borrowers through improved efficiency and reduced costs.

From a regional perspective, North America currently leads the global blockchain credit scoring market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of advanced financial infrastructure, high digital adoption rates, and a favorable regulatory environment are key factors supporting market growth in these regions. Asia Pacific is anticipated to witness the highest CAGR during the forecast period, driven by the rapid digitalization of financial services in countries such as China, India, and Singapore. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, propelled by increasing fintech investments and efforts to enhance financial inclusion.

Component Analysis

The component segment of the blockchain credit scoring market

The credit repair market is experiencing robust growth, driven by increasing consumer debt, stricter lending criteria, and a growing awareness of the importance of credit scores for financial well-being. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $6.5 billion by 2033. This growth is fueled by several key factors. Firstly, the rising prevalence of personal and business debt necessitates credit repair services for individuals and businesses aiming to improve their financial standing. Secondly, the increasing complexity of credit reporting systems and the various factors impacting credit scores make professional assistance highly sought after. Finally, the proliferation of online resources and marketing efforts has significantly heightened consumer awareness of credit repair solutions, leading to increased demand. The market is segmented by application (personal vs. business credit) and type of service (credit improvement vs. credit coaching). While personal credit repair currently dominates, the business credit repair segment is projected to witness accelerated growth due to the increasing reliance of businesses on credit scores for securing loans and investments. The competitive landscape is characterized by a mix of established players and emerging firms. Key players like Credit Saint, Lexington Law, and CreditRepair.com benefit from brand recognition and established client bases. However, the market also witnesses the emergence of niche players focusing on specific demographics or service offerings. Geographic expansion is another key trend. While North America currently holds the largest market share, regions like Asia-Pacific and Europe are expected to witness significant growth in the coming years, driven by rising disposable incomes and increased adoption of credit-based financial products. However, regulatory scrutiny and potential legal challenges represent significant restraints, impacting market expansion and service offerings. Companies are continuously adapting their strategies to navigate these regulatory landscapes while ensuring ethical and compliant service delivery.

Credit scorecards are essential tools for banks to assess the creditworthiness of loan applicants. While advanced machine learning models like XGBoost and random forest often outperform traditional logistic regression in predictive accuracy, their lack of interpretability hinders their adoption in practice. This study bridges the gap between research and practice by developing a novel framework for constructing interpretable credit scorecards using Shapley values. We apply this framework to two credit datasets, discretizing numerical variables and utilizing one-hot encoding to facilitate model development. Shapley values are then employed to derive credit scores for each predictor variable group in XGBoost, random forest, LightGBM, and CatBoost models. Our results demonstrate that this approach yields credit scorecards with interpretability comparable to logistic regression while maintaining superior predictive accuracy. This framework offers a practical and effective solution for credit practitioners seeking to leverage the power of advanced models without sacrificing transparency and regulatory compliance.

Dataset

This dataset was created by Madoka Guo

Released under Database: Open Database, Contents: Database Contents

Contents

It contains the following files:

This statistic presents the value of credit score among Hispanics in the United States in 2016. The results of the survey revealed that ** percent of the respondents had credit score over ***.

The report offers Credit Score Tracking Service Market Dynamics, Comprises Industry development drivers, challenges, opportunities, threats and limitations. A report also incorporates Cost Trend of products, Mergers & Acquisitions, Expansion, Crucial Suppliers of products, Concentration Rate of Steel Coupling Economy. Global Credit Score Tracking Service Market Research Report covers Market Effect Factors investigation chiefly included Technology Progress, Consumer Requires Trend, External Environmental Change.

The global credit monitoring service market is experiencing robust growth, driven by increasing cybercrime, data breaches, and a rising awareness of identity theft. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. Consumers are increasingly adopting credit monitoring services to protect their financial well-being in the face of sophisticated online threats. The proliferation of digital platforms and mobile applications offering convenient and affordable access to credit monitoring further boosts market penetration. Furthermore, stringent government regulations concerning data privacy and security are encouraging both individuals and businesses to invest in comprehensive credit monitoring solutions. The market is segmented by service type (credit card monitoring, loan monitoring, and others) and application (personal and enterprise). The personal segment currently dominates, driven by individual concerns about identity theft and financial fraud, but the enterprise segment is projected to experience strong growth as businesses increasingly recognize the importance of protecting their employees’ and clients’ data. Competitive landscape analysis reveals a mix of established players like Experian and Equifax and emerging innovative companies, leading to continuous service enhancement and a wider range of offerings. Geographic expansion is anticipated across North America, Europe, and the Asia-Pacific region, with North America retaining a significant market share due to high consumer awareness and adoption rates. The growth trajectory of the credit monitoring service market is expected to continue its upward trend throughout the forecast period (2025-2033), reaching an estimated market size of $45 billion by 2033. This sustained growth will be influenced by evolving technological advancements, such as AI-powered fraud detection and enhanced data analytics capabilities. Furthermore, strategic partnerships between credit bureaus, technology providers, and financial institutions are expected to facilitate the development of more integrated and comprehensive credit monitoring solutions. Despite the positive outlook, certain challenges, such as data privacy concerns and the need to constantly adapt to evolving cyber threats, could potentially impact market growth. However, the prevailing focus on data security and the rising incidence of financial fraud are expected to outweigh these challenges, ensuring continued market expansion in the long term.

Dataset

This dataset was created by Shyama Tripathi

Released under Apache 2.0

Contents