As of the first quarter of 2025, personal loans in Thailand grew by *** percent compared to other types of consumer loans. Auto loans, on the other hand, contracted by over ** percent in that same period.

Graph and download economic data for Consumer Loans, All Commercial Banks (CONSUMER) from Jan 1947 to Jun 2025 about commercial, loans, consumer, banks, depository institutions, and USA.

In November of 2024, the volume of consumer loans in the Euro area was over three percent higher than in the same month of the previous year. The year-on-year change in consumer loans fluctuated significantly since January 2006. In early 2020, the growth in consumer loans decreased sharply due to the start of the global coronavirus (COVID-19) pandemic.

Consumer Credit in the United States decreased to 5.10 USD Billion in May from 16.87 USD Billion in April of 2025. This dataset provides the latest reported value for - United States Consumer Credit Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Total Consumer Credit Owned and Securitized (TOTALSL) from Jan 1943 to May 2025 about securitized, owned, consumer credit, loans, consumer, and USA.

In December 2024, consumer loans increased the most in Malta at a 15 percent rate, whereas it showed its worst performance in Greece. In all southern European countries here included, the volume of consumer loans has fluctuated significantly from January 2006 until now.

In October 2024, the value of consumer loans in the balance sheet of German banks was 1.8 percent higher than a year earlier. Consumer lending had negative growth rates between December 2020 and December 2021, as well as during 2013. Other than during those periods, the value of consumer loans increased since 2006. The periods of negative growth rates of consumer lending in southern Europe were more prolonged.

Graph and download economic data for Percent Change of Total Consumer Credit (TOTALSLAR) from Feb 1943 to May 2025 about consumer credit, loans, consumer, rate, and USA.

Key information about Romania Total Loans Growth

• Romania Total Loans Growth was reported at 5.78 % in Jan 2025
• This records an increase from the previous number of 5.46 % for Dec 2024
• Romania Total Loans Growth data is updated monthly, averaging 5.28 % from Jan 2008 to Jan 2025, with 205 observations
• The data reached an all-time high of 55.34 % in Apr 2008 and a record low of -7.85 % in Feb 2010
• Romania Total Loans Growth data remains active status in CEIC and is reported by CEIC Data
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Total Loans: Y-o-Y Growth: Monthly

CEIC calculates monthly Total Loans Growth from monthly Total Loans. Total Loans are calculated as the sum of loans to Domestic and Foreign Customers. The National Bank of Romania provides loans to Domestic and Foreign Customers in local currency. Total Loans Growth covers lenders as Other MFIs. Total Loans Growth includes interbank loans.

The value of loans in Sweden increased 2.40 percent in June of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Sweden Household Lending Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks (CCLACBW027SBOG) from 2000-06-28 to 2025-07-16 about revolving, credit cards, loans, consumer, banks, depository institutions, and USA.

China Consumer Loan: Residential Housing Mortgage Loan data was reported at 25,750.000 RMB bn in 2018. This records an increase from the previous number of 21,860.500 RMB bn for 2017. China Consumer Loan: Residential Housing Mortgage Loan data is updated yearly, averaging 2,473.416 RMB bn from Dec 1997 (Median) to 2018, with 20 observations. The data reached an all-time high of 25,750.000 RMB bn in 2018 and a record low of 13.100 RMB bn in 1997. China Consumer Loan: Residential Housing Mortgage Loan data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s Money and Banking – Table CN.KB: Loan: Consumer Loan.

MortgagesThis dashboard provides access to data about mortgages, which are closed-end loans used to purchase or refinance a primary residence, vacation home, or investment property. Junior liens and home equity lines of credit (HELOCs) are excluded.Origination activityLending levels - The number and volume of mortgages originated each month.Year-over-year changes - Year-over-year changes in the number and volume of mortgages originated by month.Geographic changes - Geographic distribution of the year-over-year change in the volume of mortgages originated.Inquiry activityInquiry Index - The number of consumers with mortgage inquiries (hard credit pulls) each month indexed to January 2010 levels.Credit tightness index - The number of consumers with mortgage inquiries and no subsequent loan opening each month indexed to January 2010 levels.Borrower risk profilesVolume of mortgages by credit scoreExploring the origination of mortgages to consumers at different credit score levels. Year-over-year changes by credit scoreDetailing the year-over-year changes in origination activity for mortgages by credit score.Lending to low-to-moderate income neighborhoodsVolume of mortgages by neighborhood income levelExamining the origination of mortgages to consumers based on the income level of the neighborhood in which they reside. Year-over-year changes by neighborhood income levelDetailing the year-over-year changes in origination activity for mortgages by neighborhood income level.Lending by borrower ageVolume of mortgages by age groupExploring how lending activity is changing for borrowers by age. Year-over-year changes by borrower ageDetailing the year-over-year changes in origination activity for mortgages by borrower age.

Context

This dataset belongs to a Hackathon organized by "Univ.AI"!!

Content

All values were provided at the time of the loan application. | Column | Description | Type | | --- | --- | |income | Income of the user | int| |age | Age of the user | int| |experience | Professional experience of the user in years | int| |profession | Profession | string| |married | Whether married or single | string| |house_ownership | Owned or rented or neither | string| |car_ownership | Does the person own a car | string| |risk_flag | Defaulted on a loan | string| |current_job_years | Years of experience in the current job | int| |current_house_years | Number of years in the current residence | int| |city | City of residence | string| |state | State of residence | string|

The risk_flag indicates whether there has been a default in the past or not.

Thank you

Please do UPVOTE it if you find it useful 😊 Currently it has 95k+ views and 12k+ downloads. Help it reach out to more users!!

Acknowledgements

Thanks "Univ.AI" for this dataset.

Inspiration

An organization wants to predict who possible defaulters are for the consumer loans product. They have data about historic customer behavior based on what they have observed. Hence when they acquire new customers they want to predict who is riskier and who is not.

The total consumer credit outstanding in the United States increased year-on-year from 2000 to 2024, except in 2009 and 2010 when slight declines were observed. In 2024, the consumer credit outstanding in the U.S. amounted to approximately 5.06 trillion U.S. dollars - a significant increase from the previous year. At the beginning of the time period under observation, the total consumer credit outstanding in the U.S. amounted to a value of 1.62 trillion U.S. dollars.

Key information about Bolivia Total Loans Growth

• Bolivia Total Loans Growth was reported at 4.30 % in Dec 2024
• This records a decrease from the previous number of 4.50 % for Sep 2024
• Bolivia Total Loans Growth data is updated quarterly, averaging 6.07 % from Sep 2015 to Dec 2024, with 38 observations
• The data reached an all-time high of 16.84 % in Mar 2017 and a record low of 0.34 % in Mar 2021
• Bolivia Total Loans Growth data remains active status in CEIC and is reported by CEIC Data
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Total Loans: Y-o-Y Growth: Quarterly

CEIC calculates quarterly Total Loans Growth from quarterly Total Loans. Total Loans are calculated as the sum of Mortgage Loans, Consumer Loans, Secured Consumer Loans and Collateralized Consumer Loans. Financial System Supervisory Authority provides Mortgage Loans, Consumer Loans, Secured Consumer Loans and Collateralized Consumer Loans in local currency. Total Loans Growth covers lenders as Commercial Banks.

Graph and download economic data for Revolving Consumer Credit Owned and Securitized (REVOLSL) from Jan 1968 to Apr 2025 about securitized, owned, revolving, consumer credit, loans, consumer, and USA.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases:

360-Degree Customer View:Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment:Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing:Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori Consumer Data graph you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_desc...

Data Dictionary

The Data contains 1 file :

• loan.csv: In this file there are 18 columns:
  1. loanId: This is a unique loan identifier. Use this for joins with the payment.csv file
  2. anon_ssn: This is a hash based on a client’s SSN (Anonymous ssn). You can use this as if it is a SSN to compare if a loan belongs to a previous customer.
  3. payFrequency: This column represents repayment frequency of the loan:
     • B is biweekly payments
     • I is irregular
     • M is monthly
     • S is semi monthly
     • W is weekly
  4. apr: Annual Percentage Rate of the loan (%)
  5. applicationDate: Date of application (start date)
  6. originated: Indicates if the loan has been initiated (underwriting process started).
  7. originatedDate: Date of origination, day the loan was originated
  8. nPaidOff: Number of MoneyLion loans previously paid off by the client.
  9. approved: Indicates if the loan has been approved (final step of underwriting).
  10. isFunded: Whether or not a loan is ultimately funded. a loan can be voided by a customer shortly after it is approved, so not all approved loans are ultimately funded.
  11. loanStatus: Current loan status (this column is used for prediction). Most are selfexplanatory. Below are the statuses which need clarification:
      • Withdrawn Application: The applicant has withdrawn their loan application before it was approved or funded.
      • Paid Off Loan: The loan has been fully paid off by the borrower according to the repayment terms.
      • Rejected: The loan application was rejected, typically due to failure to meet underwriting criteria.
      • New Loan: A newly approved loan that has not yet been funded.
      • Internal Collection: The loan is being managed and collected internally by MoneyLion due to missed payments or delinquency.
      • CSR Voided New Loan: A new loan application was voided by a customer service representative (CSR) before funding.
      • External Collection: The loan has been transferred to an external collection agency for management and collection.
      • Returned Item: A payment on the loan has been returned due to insufficient funds in the borrower's account.
      • Customer Voided New Loan: The borrower voided a new loan application before funding.
      • Credit Return Void: The loan was voided due to a credit return, typically related to a refunded transaction.
      • Pending Paid Off: The loan is in the process of being paid off, but the process is pending completion.
      • Charged Off Paid Off: The loan has been charged off as a loss by MoneyLion but has also been paid off by the borrower.
      • Settled Bankruptcy: The loan has been settled as part of a bankruptcy proceeding.
      • Settlement Paid Off: The loan has been paid off through a settlement agreement.
      • Charged Off: The loan has been charged off as a loss by MoneyLion due to nonpayment.
      • Pending Rescind: The loan is pending rescission, meaning it may be canceled or reversed.
      • Customver Voided New Loan: Typo: Likely should be "Customer Voided New Loan". Similar to "Customer Voided New Loan", indicating the borrower voided a new loan application before funding.
      • Pending Application: The loan application is pending review and approval.
      • Voided New Loan: The loan application was voided before funding.• Pending Application Fee: The loan application is pending due to the application fee not being paid.
      • Settlement Pending Paid Off: The loan is pending being paid off through a settlement agreement.
  12. loanAmount: Principal amount of the loan ('Dollars') (for non-funded loans this will be the principal in the loan application)
  13. originallyScheduledPaymentAmount: This is the Initialy scheduled repayment amount ('Dollars') (if a customer pays off all his scheduled payments, this is the amount we should receive)
  14. state: State of the client
  15. Lead type: The lead type determines the underwriting rules for a lead.
      • bvMandatory: leads that are bought from the ping tree – required to perform bank verification before loan approval
      • lead: very similar to bvMandatory, except bank verification is optional for loan approval
      • california: similar to lead, but optimized for California lending rules
      • organic: customers that came through the MoneyLion website
      • rc_returning: customers who have at least 1 paid off loan in another loan portfolio. (The first paid off loan is not in this data set).
      • prescreen: preselected customers who have been offered a loan through direct mail campaigns
      • express: promotional “express” loans
      • repeat: promotional loans offered through ...

Graph and download economic data for Households and Nonprofit Organizations; Consumer Credit; Liability, Level (HCCSDODNS) from Q4 1945 to Q1 2025 about consumer credit, nonfinancial, sector, debt, domestic, loans, households, consumer, and USA.