Graph and download economic data for Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan (TERMCBPER24NS) from Feb 1972 to Aug 2025 about financing, consumer credit, loans, personal, consumer, interest rate, banks, interest, depository institutions, rate, and USA.

This dataset contains the customer's data from a loan company known as Prosper. This dataset comprises of 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others.

Definition of Variables:

ListingKey: Unique key for each listing, same value as the 'key' used in the listing object in the API. ListingNumber: The number that uniquely identifies the listing to the public as displayed on the website. ListingCreationDate: The date the listing was created. CreditGrade: The Credit rating that was assigned at the time the listing went live. Applicable for listings pre-2009 period and will only be populated for those listings. Term: The length of the loan expressed in months. LoanStatus: The current status of the loan: Cancelled, Chargedoff, Completed, Current, Defaulted, FinalPaymentInProgress, PastDue. The PastDue status will be accompanied by a delinquency bucket. ClosedDate: Closed date is applicable for Cancelled, Completed, Chargedoff and Defaulted loan statuses. BorrowerAPR: The Borrower's Annual Percentage Rate (APR) for the loan. BorrowerRate: The Borrower's interest rate for this loan. LenderYield: The Lender yield on the loan. Lender yield is equal to the interest rate on the loan less the servicing fee. EstimatedEffectiveYield: Effective yield is equal to the borrower interest rate (i) minus the servicing fee rate, (ii) minus estimated uncollected interest on charge-offs, (iii) plus estimated collected late fees. Applicable for loans originated after July 2009. EstimatedLoss: Estimated loss is the estimated principal loss on charge-offs. Applicable for loans originated after July 2009. EstimatedReturn: The estimated return assigned to the listing at the time it was created. Estimated return is the difference between the Estimated Effective Yield and the Estimated Loss Rate. Applicable for loans originated after July 2009. ProsperRating (numeric): The Prosper Rating assigned at the time the listing was created: 0 - N/A, 1 - HR, 2 - E, 3 - D, 4 - C, 5 - B, 6 - A, 7 - AA. Applicable for loans originated after July 2009. ProsperRating (Alpha): The Prosper Rating assigned at the time the listing was created between AA - HR. Applicable for loans originated after July 2009. ProsperScore: A custom risk score built using historical Prosper data. The score ranges from 1-10, with 10 being the best, or lowest risk score. Applicable for loans originated after July 2009. ListingCategory: The category of the listing that the borrower selected when posting their listing: 0 - Not Available, 1 - Debt Consolidation, 2 - Home Improvement, 3 - Business, 4 - Personal Loan, 5 - Student Use, 6 - Auto, 7- Other, 8 - Baby&Adoption, 9 - Boat, 10 - Cosmetic Procedure, 11 - Engagement Ring, 12 - Green Loans, 13 - Household Expenses, 14 - Large Purchases, 15 - Medical/Dental, 16 - Motorcycle, 17 - RV, 18 - Taxes, 19 - Vacation, 20 - Wedding Loans BorrowerState: The two letter abbreviation of the state of the address of the borrower at the time the Listing was created. Occupation: The Occupation selected by the Borrower at the time they created the listing. EmploymentStatus: The employment status of the borrower at the time they posted the listing. EmploymentStatusDuration: The length in months of the employment status at the time the listing was created. IsBorrowerHomeowner: A Borrower will be classified as a homowner if they have a mortgage on their credit profile or provide documentation confirming they are a homeowner. CurrentlyInGroup: Specifies whether or not the Borrower was in a group at the time the listing was created. GroupKey: The Key of the group in which the Borrower is a member of. Value will be null if the borrower does not have a group affiliation. DateCreditPulled: The date the credit profile was pulled. CreditScoreRangeLower: The lower value representing the range of the borrower's credit score as provided by a consumer credit rating agency. CreditScoreRangeUpper: The upper value representing the range of the borrower's credit score as provided by a consumer credit rating agency. FirstRecordedCreditLine: The date the first credit line was opened. CurrentCreditLines: Number of current credit lines at the time the credit profile was pulled. OpenCreditLines: Number of open credit lines at the time the credit profile was pulled. TotalCreditLinespast7years: Number of credit lines in the past seven years at the time the credit profile was pulled. OpenRevolvingAccounts: Number of open revolving accounts at the time the credit profile was pulled. OpenRevolvingMonthlyPayment: Monthly payment on revolving accounts at the time the credit profile was pulled. InquiriesLast6Months: Number of inquiries in the past six months at the time the cre...

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Personal Loans Market Size 2025-2029

The personal loans market size is forecast to increase by USD 803.4 billion, at a CAGR of 15.2% between 2024 and 2029.

The market is witnessing significant advancements, driven by the increasing adoption of technology in loan processing. Innovations such as artificial intelligence and machine learning are streamlining application processes, enhancing underwriting capabilities, and improving customer experiences. Moreover, the shift towards cloud-based personal loan servicing software is gaining momentum, offering flexibility, scalability, and cost savings for lenders. However, the market is not without challenges. Compliance and regulatory hurdles pose significant obstacles, with stringent regulations governing data privacy, consumer protection, and fair lending practices. Lenders must invest in robust compliance frameworks and stay updated with regulatory changes to mitigate risks and maintain a competitive edge.
Additionally, managing the increasing volume and complexity of loan applications while ensuring accuracy and efficiency remains a pressing concern. Addressing these challenges through technological innovations and strategic partnerships will be crucial for companies seeking to capitalize on the market's growth potential and navigate the competitive landscape effectively.

What will be the Size of the Personal Loans Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by advancements in technology and shifting consumer preferences. Digital lending platforms enable online applications, automated underwriting, and instant loan disbursement. APIs integrate various financial planning tools, such as FICO score analysis and retirement planning, ensuring a comprehensive borrowing experience. Unsecured loans, including personal installment loans and lines of credit, dominate the market. Credit history, interest rates, and borrower eligibility are critical factors in determining loan terms. Predictive modeling and machine learning algorithms enhance risk assessment and fraud detection. Consumer protection remains a priority, with regulations addressing identity theft and fintech literacy.

Credit utilization and debt management are essential components of loan origination and debt consolidation. Repayment schedules and debt management plans help borrowers navigate their financial obligations. Market dynamics extend to sectors like student loans, auto loans, and mortgage loans. Loan servicing, collection agencies, and loan application processes ensure efficient loan administration. Open banking and data analytics facilitate seamless financial transactions and improve loan approval processes. Small business loans and secured loans also contribute to the market's growth. Continuous innovation in digital lending, credit scoring, and loan origination shapes the future of the market.

How is this Personal Loans Industry segmented?

The personal loans industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Short term loans
  Medium term loans
  Long term loans


Type

  P2P marketplace lending
  Balance sheet lending


Channel

  Banks
  Credit union
  Online lenders


Purpose

  Debt Consolidation
  Home Improvement
  Medical Expenses
  Education


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The short term loans segment is estimated to witness significant growth during the forecast period.

Personal loans continue to gain traction in the US market, driven by the convenience of online applications and the increasing adoption of digital lending. Unsecured loans, such as personal installment loans and lines of credit, allow borrowers to access funds quickly for various personal expenses, including debt consolidation and unexpected expenses. Short-term loans, including payday loans and auto title loans, provide immediate financial relief with quick approval and flexible repayment schedules. Predictive modeling and machine learning enable automated underwriting, streamlining the loan origination process and improving borrower eligibility assessment. Credit scoring, FICO scores, and debt-to-income ratios (DTIs) are essential components of the credit evaluation process, ensuring responsible lending practices.

Digital lending platforms offer customer service through various channels, including mobile banking and open banking, enhancing the borrower experie

Most of the lending to individuals written-off by financial institutions in the United Kingdom (UK) in the first quarter of 2025 were unsecured loans. Mortgage write-offs only amounted to 15 million British pounds, a fraction of the values for credit cards and other personal loans. Nevertheless, the outstanding value of personal loans secured on dwellings was much higher than that of consumer credit.

In recent years, the personal loan industry has undergone a significant transformation, driven by the need for accessible credit, rising consumer demand, and advancements in digital lending. Personal loans have become a crucial financial tool for many, enabling individuals to meet various needs, from debt consolidation to major purchases. Understanding...

Explore consumer and credit card loans data in Saudi Arabia, including information on maturity terms, categories such as tourism, vehicles, education, health care, and more. Access quarterly and annual data on total credit card loans, with a focus on medium, long, and short-term personal loan options.

Consumer Loans, Tourism, Maturity Terms, Medium Term, Education, Health Care, Vehicles, Bank, SAMA Quarterly

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..Author Notes: The data from Q3 2017 to Q2 2019 have been updated.The dataset excludes real estate financing, financial leasing, and margin lending financing against shares."Total Credit Card Loans" Includes Visa, Master Card, American Express, and Others."Maturity Terms Of Personal Loans" represents loans granted by commercial banks to natural persons for financing personal, consumer and non-commercial purposes.For the data before 2014, the items of Furniture & Durable Goods, Education, Health care, Tourism and travel were included under 'Others'. "Short Term" : Less than one year"Medium Term" : 1 - 3 Years"Long Term" : Over 3 Years Loaans granted by commercial banks to natural persons for financing personal and consumer needs and for non-commercial purposes.

In the financial year 2024, the portfolio outstanding or the value of personal loans in India was around ** trillion, an increase from last year. The value of personal loans outstanding has continuously increased since the financial year 2020.

SCB: Credit Outstanding: Non Food: Personal Loans: Advances to Individuals against Share, Bonds, etc data was reported at 100,057.143 INR mn in Oct 2025. This records an increase from the previous number of 98,345.998 INR mn for Sep 2025. SCB: Credit Outstanding: Non Food: Personal Loans: Advances to Individuals against Share, Bonds, etc data is updated monthly, averaging 34,650.000 INR mn from Mar 2003 (Median) to Oct 2025, with 272 observations. The data reached an all-time high of 104,877.367 INR mn in Apr 2025 and a record low of 14,630.000 INR mn in Mar 2003. SCB: Credit Outstanding: Non Food: Personal Loans: Advances to Individuals against Share, Bonds, etc data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAH: Scheduled Commercial Banks: Credit: Gross Outstanding: by Sector.

View quarterly updates and historical trends for US Finance Rate on Personal Loans at Commercial Banks. from United States. Source: Federal Reserve. Track…

Explore Thera Bank's customer dataset (Bank.xls) with 5000 entries, revealing insights into demographics and past personal loan campaign responses. Dive into the challenge of optimizing personal loan conversions with a focus on retaining depositors. Kaggle your way through data-driven strategies for Thera Bank's success.

Data Description:

Age: Customer's age in completed years. Experience:Number of years of professional experience. Income: Annual income of the customer in thousands ($000). ZIPCode:Home Address ZIP code. Family:Family size of the customer. CCAvg: Average spending on credit cards per month in thousands ($000). Education: Education Level - 1: Undergrad; 2: Graduate; 3: Advanced/Professional. Mortgage: Value of the house mortgage if any in thousands ($000). Personal Loan: Binary variable indicating whether the customer accepted the personal loan offered in the last campaign. Securities Account: Binary variable indicating whether the customer has a securities account with the bank. CD Account: Binary variable indicating whether the customer has a certificate of deposit (CD) account with the bank. Online: Binary variable indicating whether the customer uses internet banking facilities. CreditCard:Binary variable indicating whether the customer uses a credit card issued by TheraBank.

Commercial bank interest rates on personal loans with a maturity of 24 months in the United States were significantly higher in February 2024 than a year earlier. That month, that finance rate amounted to ***** percent. Since the year 2000, there have only been a few occasions in which the finance rate was ** percent or higher.

SCB: Credit Outstanding: Non Food: Personal Loans data was reported at 64,559,459.412 INR mn in Oct 2025. This records an increase from the previous number of 62,542,739.610 INR mn for Sep 2025. SCB: Credit Outstanding: Non Food: Personal Loans data is updated monthly, averaging 12,546,710.000 INR mn from Sep 2005 (Median) to Oct 2025, with 242 observations. The data reached an all-time high of 64,559,459.412 INR mn in Oct 2025 and a record low of 2,934,410.000 INR mn in Sep 2005. SCB: Credit Outstanding: Non Food: Personal Loans data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAH: Scheduled Commercial Banks: Credit: Gross Outstanding: by Sector. Data since July 2023, include the impact of the merger of a non-bank with a bank. [COVID-19-IMPACT]

The value of outstanding loans extended to private individuals in France has been slowly increasing in the years leading up to 2023. As of **************, the value of outstanding loans to private individuals reached approximately **** trillion euros. In ************, the value of the outstanding loans to individuals amounted to less than a trillion euros.

SCB: Credit Outstanding: Non Food: Personal Loans: Others data was reported at 16,170,886.048 INR mn in Oct 2025. This records an increase from the previous number of 15,548,671.340 INR mn for Sep 2025. SCB: Credit Outstanding: Non Food: Personal Loans: Others data is updated monthly, averaging 2,556,840.000 INR mn from Sep 2005 (Median) to Oct 2025, with 242 observations. The data reached an all-time high of 16,170,886.048 INR mn in Oct 2025 and a record low of 779,870.000 INR mn in Oct 2005. SCB: Credit Outstanding: Non Food: Personal Loans: Others data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAH: Scheduled Commercial Banks: Credit: Gross Outstanding: by Sector. Data since July 2023, include the impact of the merger of a non-bank with a bank.

Over ** percent of households in Great Britain had at least a personal loan in 2023. Most of those households had one personal loan. However, over five percent of all households in Great Britain had at least *** personal loans.

As of October 2024, monetary financial institutions (MFI) granted most of the lending to individuals in the United Kingdom (UK). Meanwhile, other non-bank lenders gave approximately *** million British pounds worth of loans just in March 2024. During the past years, non-bank lenders have been increasing their market share. Non-MFI lenders also had a growing market share of the new consumer lending market in the UK.

Loans to Private Sector in the United States increased to 2696.18 USD Billion in October from 2692.57 USD Billion in September of 2025. This dataset provides - United States Loans to Private Sector - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States - Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan was 11.14% in August of 2025, according to the United States Federal Reserve. Historically, United States - Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan reached a record high of 19.21 in November of 1981 and a record low of 8.73 in May of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan - last updated from the United States Federal Reserve on December of 2025.

SCB: Credit Outstanding: Non Food: Personal Loans: Credit Card Outstanding data was reported at 3,030,727.833 INR mn in Oct 2025. This records an increase from the previous number of 2,818,226.511 INR mn for Sep 2025. SCB: Credit Outstanding: Non Food: Personal Loans: Credit Card Outstanding data is updated monthly, averaging 337,370.000 INR mn from Sep 2005 (Median) to Oct 2025, with 242 observations. The data reached an all-time high of 3,030,727.833 INR mn in Oct 2025 and a record low of 75,060.000 INR mn in Sep 2005. SCB: Credit Outstanding: Non Food: Personal Loans: Credit Card Outstanding data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAH: Scheduled Commercial Banks: Credit: Gross Outstanding: by Sector. [COVID-19-IMPACT]

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Description:

Banks earn a major revenue from lending loans. But it is often associated with risk. The borrower's may default on the loan. To mitigate this issue, the banks have decided to use Machine Learning to overcome this issue. They have collected past data on the loan borrowers & would like you to develop a strong ML Model to classify if any new borrower is likely to default or not.

The dataset is enormous & consists of multiple deteministic factors like borrowe's income, gender, loan pupose etc. The dataset is subject to strong multicollinearity & empty values. Can you overcome these factors & build a strong classifier to predict defaulters?

Acknowledgements:

This dataset has been referred from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build classification model to predict weather the loan borrower will default or not.
• Also fine-tune the hyperparameters & compare the evaluation metrics of vaious classification algorithms.