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.

The dataset comprises information on 1000 customers, with 84 features derived from their financial transactions and current financial standing. The primary objective is to leverage this dataset for credit risk estimation and predicting potential defaults.

• CUST_ID: Unique customer identifier

Key Target Variables: - CREDIT_SCORE: Numerical target variable representing the customer's credit score (integer) - DEFAULT: Binary target variable indicating if the customer has defaulted (1) or not (0)

Description of Features: - INCOME: Total income in the last 12 months - SAVINGS: Total savings in the last 12 months - DEBT: Total existing debt - R_SAVINGS_INCOME: Ratio of savings to income - R_DEBT_INCOME: Ratio of debt to income - R_DEBT_SAVINGS: Ratio of debt to savings

Transaction groups (GROCERIES, CLOTHING, HOUSING, EDUCATION, HEALTH, TRAVEL, ENTERTAINMENT, GAMBLING, UTILITIES, TAX, FINES) are categorized. - T_{GROUP}_6: Total expenditure in that group in the last 6 months - T_GROUP_12: Total expenditure in that group in the last 12 months - R_[GROUP]: Ratio of T_[GROUP]_6 to T_[GROUP]_12 - R_[GROUP]_INCOME: Ratio of T_[GROUP]_12 to INCOME - R_[GROUP]_SAVINGS: Ratio of T_[GROUP]_12 to SAVINGS - R_[GROUP]_DEBT: Ratio of T_[GROUP]_12 to DEBT

Categorical Features: - CAT_GAMBLING: Gambling category (none, low, high) - CAT_DEBT: 1 if the customer has debt; 0 otherwise - CAT_CREDIT_CARD: 1 if the customer has a credit card; 0 otherwise - CAT_MORTGAGE: 1 if the customer has a mortgage; 0 otherwise - CAT_SAVINGS_ACCOUNT: 1 if the customer has a savings account; 0 otherwise - CAT_DEPENDENTS: 1 if the customer has any dependents; 0 otherwise

See XAI course based on this dataset: https://adataodyssey.com/courses/xai-with-python/

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.

Introduction:

This dataset analysis aims to explore and analyze a Credit Score dataset to gain insights into customer creditworthiness and segmentation. The dataset contains information on various factors that influence credit scores, such as payment history, credit utilization ratio, number of credit accounts, education level, and employment status. The analysis will utilize the k-means algorithm to perform clustering and identify distinct groups of customers based on their credit scores.

The Credit Score dataset comprises a collection of records, each representing an individual's credit profile. The features included in the dataset are as follows:

The data set Contains following all features:

**Description of All features **:

(1). Age: This feature represents the age of the individual.

(2). Gender: This feature captures the gender of the individual.

(3). Marital Status: This feature denotes the marital status of the individual.

(4). Education Level: This feature represents the highest level of education attained by the individual.

(5). Employment Status: This feature indicates the current employment status of the individual.

(6). Credit Utilization Ratio: This feature reflects the ratio of credit used by the individual compared to their total available credit limit.

(7). Payment History: It represents the monthly net payment behaviour of each customer, taking into account factors such as on-time payments, late payments, missed payments, and defaults.

(8). Number of Credit Accounts: It represents the count of active credit accounts the person holds.

(9). Loan Amount: It indicates the monetary value of the loan.

(10). Interest Rate: This feature represents the interest rate associated with the loan.

(11). Loan Term: This feature denotes the duration or term of the loan.

(12). Type of Loan: It includes categories like “Personal Loan,” “Auto Loan,” or potentially other types of loans.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Current Credit Score: 50th Percentile (RCCCBSCOREPCT50) from Q3 2012 to Q2 2025 about score, FR Y-14M, credit cards, consumer credit, large, balance, credits, percentile, 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.

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

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.

In the first quarter of 2025, the average credit score of used car loan originations in the United States was *** points. That credit score was somewhat lower than that of new car loans: *** points at that same period. In both cases, however, the average credit score has been increasing slightly during the past years.

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 ***.

As of September 2020, approximately three percent of Americans aged 55 to 59 had a geo-credit score of at least ***. This age group has the highest share of every bucket except the lowest, suggesting that it simply has the most members in the sample. 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).

As of September 2020, over five percent of Americans had a geo-credit score value of A, which corresponds to traditional credit score of at least ***. The lowest score values (up to ***), from O to T, accounted for just over ten percent of the population. 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).

The training dataset includes all addresses that had undertaken at least one borrow transaction on Aave v2 Ethereum or Compound v2 Ethereum any time between 7 May 2019 and 31 August 2023, inclusive (called the observation window). Data Structure & Shape There are almost 0.5 million observations with each representing a single borrow event. Therefore, all feature values are calculated as at the timestamp of a borrow event and represent the cumulative positions just before the borrow event's… See the full description on the dataset page: https://huggingface.co/datasets/spectrallabs/credit-scoring-training-dataset.

Graph and download economic data for Large Bank Consumer Credit Card Originations: Original Credit Score: 50th Percentile (RCCCOSCOREPCT50) from Q3 2012 to Q2 2025 about score, origination, FR Y-14M, credit cards, consumer credit, large, credits, percentile, loans, consumer, banks, depository institutions, and USA.

Graph and download economic data for Large Bank Consumer Mortgage Originations: Original Credit Score: 25th Percentile (RCMFLOSCOREPCT25) from Q3 2012 to Q2 2025 about score, origination, FR Y-14M, large, credits, mortgage, percentile, consumer, banks, depository institutions, and USA.

Credit Rating History Dataset

Credit Rating History

By Center for Municipal Finance [source]

About this dataset

The project that led to the creation of this dataset received funding from the Center for Corporate and Securities Law at the University of San Diego School of Law. The dataset itself can be accessed through a GitHub repository or on its dedicated website.

In terms of columns contained in this dataset, it encompasses a range of variables relevant to analyzing credit ratings. However, specific details about these columns are not provided in the given information. To acquire a more accurate understanding of the column labels and their corresponding attributes or measurements present in this dataset, further exploration or referencing additional resources may be required

How to use the dataset

• Understanding the Data

  The dataset consists of several columns that provide essential information about credit ratings and fixed income securities. Familiarize yourself with the column names and their meanings to better understand the data:

  • Column 1: [Credit Agency]
  • Column 2: [Issuer Name]
  • Column 3: [CUSIP/ISIN]
  • Column 4: [Rating Type]
  • Column 5: [Rating Source]
  • Column 6: [Rating Date]

• Exploratory Data Analysis (EDA)

  Before diving into detailed analysis, start by performing exploratory data analysis to get an overview of the dataset.

  • Identify Unique Values: Explore each column's unique values to understand rating agencies, issuers, rating types, sources, etc.

  • Frequency Distribution: Analyze the frequency distribution of various attributes like credit agencies or rating types to identify any imbalances or biases in the data.

• Data Visualization

  Visualizing your data can provide insights that are difficult to derive from tabular representation alone. Utilize various visualization techniques such as bar charts, pie charts, histograms, or line graphs based on your specific objectives.

  For example:

  • Plotting a histogram of each credit agency's ratings can help you understand their distribution across different categories.
  • A time-series line graph can show how ratings have evolved over time for specific issuers or industries.

• Analyzing Ratings Performance

  One of the main objectives of using credit rating datasets is to assess the performance and accuracy of different credit agencies. Conducting a thorough analysis can help you understand how ratings have changed over time and evaluate the consistency of each agency's ratings.

  • Rating Changes Over Time: Analyze how ratings for specific issuers or industries have changed over different periods.

  • Comparing Rating Agencies: Compare ratings from different agencies to identify any discrepancies or trends. Are there consistent differences in their assessments?

• Detecting Rating Trends

  The dataset allows you to detect trends and correlations between various factors related to

Research Ideas

• Credit Rating Analysis: This dataset can be used for analyzing credit ratings and trends of various fixed income securities. It provides historical credit rating data from different rating agencies, allowing researchers to study the performance, accuracy, and consistency of these ratings over time.
• Comparative Analysis: The dataset allows for comparative analysis between different agencies' credit ratings for a specific security or issuer. Researchers can compare the ratings assigned by different agencies and identify any discrepancies or differences in their assessments. This analysis can help in understanding variations in methodologies and improving the transparency of credit rating processes

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all ...

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.

Asian and White mortgage applicants had, on average, higher credit score than Black and Hispanic applicants. Overall, credit scores declined between the first quarter of 2021 and the second quarter of 2022. Asian mortgage applicants had a credit score of *** in the second quarter of 2022.

As of August 2019, ** percent of Americans aged 55 to 59 had a geo-credit score in bucket *, which corresponds to traditional credit score of at least ***. 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 ***** 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).

Fintechs, credit bureaus, and financial institutions are collecting alternative data to develop new scoring models that supplement traditional credit reports. Studies, providers, and pilot programs suggest that these alternative data can improve credit reporting and thereby expand access to fair credit. However, use of alternative data is still low due to both uncertainty about the benefits relative to the cost and consumer concerns about use and privacy.