The average credit score of Americans - as measured by the FICO score - increased for the first time in about two years in early 2023. The average score in April 2024 stood at ***. The score as displayed ranges from *** to *** and is based on three different consumer reporting agencies (CRAs) in the United States, namely Equifax, TransUnion, and Experian. The source adds that the score was especially impacted by slowing inflation, lower unemployment figures and changes to certain consumer credit data.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Current Credit Score: 25th Percentile (RCCCBSCOREPCT25) from Q3 2012 to Q1 2025 about score, FR Y-14M, consumer credit, credit cards, large, percentile, balance, credits, loans, consumer, banks, depository institutions, and USA.

In 2019, the average credit score for consumers in the United States was ***. Meanwhile, it was *** points among consumers over 60 years of age, which was ** points higher than the average of those in their twenties.

This statistic shows the average credit scores in the United States in 2017, by state. In 2017, the average credit rating in Alabama was *** whereas it was *** in Vermont.

Graph and download economic data for Large Bank Consumer Mortgage Originations: Original Credit Score: 25th Percentile (RCMFLOSCOREPCT25) from Q3 2012 to Q1 2025 about score, FR Y-14M, origination, large, percentile, credits, mortgage, consumer, banks, depository institutions, and USA.

Graph and download economic data for Large Bank Consumer Mortgage Balances: Current Credit Score: 50th Percentile (RCMFLBSCOREPCT50) from Q3 2012 to Q1 2025 about score, FR Y-14M, large, percentile, balance, credits, mortgage, consumer, banks, depository institutions, and USA.

This statistic shows the average credit scores in the United States in 2017, by generation. In 2017, Millennials (or Gen Yers) had an average credit rating of *** in the U.S.

Primary and Secondary data from chit fund companies for Credit Scoring project in India

Graph and download economic data for Large Bank Consumer Credit Card Originations: Original Credit Score: 50th Percentile (RCCCOSCOREPCT50) from Q3 2012 to Q1 2025 about score, FR Y-14M, origination, consumer credit, credit cards, large, percentile, credits, loans, consumer, banks, depository institutions, and USA.

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.

The average credit score of borrowers with new car loans in the United States increased by ** points from 2020 to 2025. In the first quarter of 2025, the average credit score of borrowers of new car loans was ***, while the credit score of people with new leased cars was *** points. Leases had on average higher risk scores than loans throughout the timeline.

Graph and download economic data for Large Bank Consumer Mortgage Originations: Original Credit Score: 10th Percentile (RCMFLOSCOREPCT10) from Q3 2012 to Q1 2025 about score, FR Y-14M, origination, large, percentile, credits, mortgage, consumer, banks, depository institutions, and USA.

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

Graph and download economic data for Large Bank Consumer Credit Card Originations: Original Credit Score: 25th Percentile (RCCCOSCOREPCT25) from Q3 2012 to Q4 2024 about score, FR Y-14M, origination, consumer credit, credit cards, large, percentile, credits, loans, consumer, banks, depository institutions, and USA.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Average Purchase Volume by Credit Score Group: 660-719 Credit Score (RCCCBPURCHASEAS660T719) from Q3 2012 to Q1 2025 about volume, score, FR Y-14M, purchase, consumer credit, credit cards, large, balance, credits, average, loans, consumer, banks, depository institutions, and USA.

The global market for credit scores, credit reports, and credit check services is a substantial and rapidly expanding sector, projected to reach $13.92 billion in 2025 and maintain a robust Compound Annual Growth Rate (CAGR) of 5.4% from 2025 to 2033. This growth is fueled by several key drivers. The increasing adoption of digital technologies and online financial services is significantly boosting the demand for efficient and readily accessible credit assessment tools. Furthermore, stricter regulatory compliance requirements across various financial markets are driving the need for comprehensive and accurate credit information, benefiting established players and fostering innovation within the industry. The rising penetration of credit cards and other forms of consumer credit, particularly in emerging economies, adds another layer to this expanding market. Segment-wise, the enterprise credit segment holds a larger share due to the high volume of transactions and credit assessments involved in business lending and financial transactions. However, individual credit checks, driven by growing consumer awareness of their credit scores and the expanding use of credit for personal purposes, also present a significant growth opportunity. Key players like Experian, Equifax, and TransUnion are dominating the market, leveraging their extensive data networks and established brand recognition. However, the emergence of fintech companies and innovative credit scoring models based on alternative data sources is introducing competition and fostering innovation. The market's geographical distribution is likely skewed towards developed economies with robust financial infrastructures and high credit penetration. However, rapid economic growth and increasing financial inclusion in emerging markets like China present substantial untapped potential. While data privacy concerns and regulatory changes pose potential restraints, the overall industry outlook remains positive, driven by increasing digitalization, stringent regulatory requirements, and the growing reliance on credit in both personal and business contexts. The continuous development of advanced analytical techniques for more accurate credit risk assessment will continue to drive market growth and reshape the competitive landscape. The expansion of open banking initiatives further accelerates innovation by providing access to broader datasets for more nuanced credit scoring and risk analysis.

Dataset

This dataset was created by MXNXV-ERR

Released under Apache 2.0

Contents

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.

This dataset presents a social trust index based on average credit scores at the zip code level.

Dataset

This dataset was created by Akriti Upadhyay

Contents