This statistics illustrates the default rate (DR) on corporate loans in Central and Eastern Europe (CEE) as of the first quarter of 2020, by country. Default rates generally displays the percentage of loans that have been charged off by a bank after a prolonged period of missed payments by the loans receiver. Banking sectors within a country preferably want a low default rate. The default rate of Romanian banks as of the first quarter of 2020 was approximately **** percent, while Croatia and Slovakia had default rates of **** percent and **** percent respectively.

Provides the statistics about Student Loan Default Statistics

In 2022, the student loan default rate in the United States was highest for Black borrowers, at **** percent. In comparison, Asian borrowers were least likely to default on their student loans.

Graph and download economic data for Delinquency Rate on All Loans, All Commercial Banks (DRALACBN) from Q1 1985 to Q1 2025 about delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

This statistics illustrates the probability of default (PD) on retail loans in Central and Eastern Europe (CEE) as of the first quarter of 2020, by country. As of the first quarter of 2020, Hungary displayed the highest probability of a retail loan defaulting with 3.29 percent. Latvia had the second highest probability of default on retail loans in CEE with 2.84 percent.

Brazil Loans: Consumer Default data was reported at 63,205,420.000 Unit in Apr 2019. This records an increase from the previous number of 62,960,186.000 Unit for Mar 2019. Brazil Loans: Consumer Default data is updated monthly, averaging 60,479,500.000 Unit from Mar 2016 (Median) to Apr 2019, with 38 observations. The data reached an all-time high of 63,205,420.000 Unit in Apr 2019 and a record low of 59,269,000.000 Unit in Aug 2016. Brazil Loans: Consumer Default data remains active status in CEIC and is reported by Serasa Experian. The data is categorized under Brazil Premium Database’s Monetary – Table BR.KAB034: Loans: Consumer Default.

Graph and download economic data for Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks (DRSFRMACBS) from Q1 1991 to Q1 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, commercial, domestic, banks, depository institutions, rate, and USA.

Graph and download economic data for Number of Domestic Banks That Eased and Reported That Reduction in Defaults by Borrowers in Public Debt Markets Was a Somewhat Important Reason (SUBLPDCIREDSNQ) from Q3 2000 to Q1 2011 about ease, borrowings, public, debt, domestic, banks, depository institutions, and USA.

Data Dictionary:

1. Title: Credit data

2. Source: Credit One Bank

3. Number of Instances: 5000

4. Name of Dataset: Analysis_of_Default

5. Number of Attributes: 20 (7 numerical, 13 categorical)

6. Attribute description

Attribute 1: (Qualitative / Categorical) Status of existing checking account A11: ... < 0 USD A12: 0 <= ... < 10000 USD A13: ... >= 10000 USD A14: no checking account

Attribute 2: (numerical) Duration in month

Attribute 3: (Qualitative / Categorical) Credit history A30: no credits taken/all credits paid back duly A31: all credits at this bank paid back duly A32: existing credits paid back duly till now A33: delay in paying off in the past A34:critical account/other credits existing(not at this bank)

Attribute 4: (Qualitative / Categorical) Purpose A40: car (new) A41: car (used) A42: furniture/equipment A43: radio/television A44: domestic appliances A45: repairs A46: education A47: (vacation - does not exist?) A48: retraining A49: business A410: others

Attribute 5: (numerical) Credit amount

Attribute 6: (Qualitative / Categorical) Savings account/bonds A61: ... < 1000 USD A62: 1000 <= ... < 5000 USD A63: 5000 <= ... < 10000 USD A64: .. >= 10000 USD A65: unknown/ no savings account

Attribute 7: (Qualitative / Categorical) Present employment since A71: unemployed A72: ... < 1 year A73: 1 <= ... < 4 years
A74: 4 <= ... < 7 years A75: .. >= 7 years

Attribute 8: (numerical) Installment rate in percentage of disposable income

Attribute 9: (Qualitative / Categorical) Personal status and sex A91: male : divorced/separated A92: female: divorced/separated/married A93: male : single A94: male : married/widowed A95: female: single

Attribute 10: (Qualitative / Categorical) Other debtors / guarantors A101: none A102: co-applicant A103: guarantor

Attribute 11: (numerical) Present residence since

Attribute 12: (Qualitative / Categorical) Property A121: real estate A122: if not A121: building society savings agreement/ life insurance A123: if not A121/A122: car or other, not in attribute 6 A124: unknown / no property

Attribute 13: (numerical) Age in years

Attribute 14: (Qualitative / Categorical) Other installment plans A141: bank A142: stores A143: none

Attribute 15: (Qualitative / Categorical) Housing A151: rent A152: own A153: for free

Attribute 16: (numerical) Number of existing credits at this bank

Attribute 17: (Qualitative / Categorical) Job A171: unemployed/ unskilled - non-resident A172: unskilled - resident A173: skilled employee / official A174: management/ self-employed/ highly qualified employee/ officer

Attribute 18: (numerical) Number of people being liable to provide maintenance for

Attribute 19: (Qualitative / Categorical) Telephone A191: none A192: yes, registered under the customer’s name

Attribute 20: (Qualitative / Categorical) foreign worker A201: yes A202: no

1. Default on Payment due

1 (Defaulted) 0 (No Default)

Brazil Loans: Consumer Default: Negative Debt: Value data was reported at 246,600,762,083.000 BRL in Apr 2019. This records a decrease from the previous number of 247,364,436,780.000 BRL for Mar 2019. Brazil Loans: Consumer Default: Negative Debt: Value data is updated monthly, averaging 268,543,258,276.000 BRL from Mar 2016 (Median) to Apr 2019, with 38 observations. The data reached an all-time high of 274,600,092,903.000 BRL in May 2017 and a record low of 237,022,238,842.000 BRL in Oct 2018. Brazil Loans: Consumer Default: Negative Debt: Value data remains active status in CEIC and is reported by Serasa Experian. The data is categorized under Brazil Premium Database’s Monetary – Table BR.KAB034: Loans: Consumer Default.

Personal Loan product is an unsecured loan therefore it is vital to assess the risk of the customers by checking their credit worthiness. This must be done to prevent loan defaults.

The objective is to build a Risk model using the dataset which will assess the risk of a customer defaulting after cross-selling the Personal Loan.

Column Descriptions: V1: Customer ID V2: If a customer has bounced in first EMI (1 : Bounced, 0 : Not bounced) V3: Number of times bounced in recent 12 months V4: Maximum MOB (Month of business with TVS Credit) V5: Number of times bounced while repaying the loan V6: EMI V7: Loan Amount V8: Tenure V9: Dealer codes from where customer has purchased the Two wheeler V10: Product code of Two wheeler (MC : Motorcycle , MO : Moped, SC : Scooter) V11: No of advance EMI paid V12: Rate of interest V13: Gender (Male/Female) V14: Employment type (HOUSEWIFE : housewife, SELF : Self-employed, SAL : Salaried, PENS : Pensioner, STUDENT : Student) V15: Resident type of customer V16: Date of birth V17: Age at which customer has taken the loan V18: Number of loans V19: Number of secured loans V20: Number of unsecured loans V21: Maximum amount sanctioned in the Live loans V22: Number of new loans in last 3 months V23: Total sanctioned amount in the secured Loans which are Live V24: Total sanctioned amount in the unsecured Loans which are Live V25: Maximum amount sanctioned for any Two wheeler loan V26: Time since last Personal loan taken (in months) V27: Time since first consumer durables loan taken (in months) V28: Number of times 30 days past due in last 6 months V29: Number of times 60 days past due in last 6 months V30: Number of times 90 days past due in last 3 months V31: Tier ; (Customer’s geographical location) V32: Target variable ( 1: Defaulters / 0: Non-Defaulters)

Graph and download economic data for Number of Other Domestic Banks That Tightened and Reported That Increase in Defaults by Borrowers in Public Debt Markets Was Not an Important Reason (SUBLPDCIRTDNOTHNQ) from Q3 2000 to Q1 2011 about borrowings, public, debt, domestic, banks, depository institutions, and USA.

In the fiscal year of 2019, around 4.1 percent of students who went to private, for-profit public 2-year institutions in the United States were in default on their loans. The default rate for students in the FY 2019 cohort was 1.9 percent at 4-year degree-granting postsecondary institutions, and 3.8 percent at 2-year degree-granting postsecondary institutions.

This statistics illustrates the probability of default (PD) on loans to corporations in Central and Eastern Europe (CEE) as of the first quarter of 2020, by country. As of the first quarter of 2020, Slovenia displayed the highest probability of a corporate loan defaulting with approximately 4.03 percent. Estonia on the other hand during the same period had a probability of default (PD) rate of almost one percent.

International Indicators: Malaysia 5-Year Credit Default Swap (CDS) data was reported at 46.575 Basis Point in Feb 2025. This records a decrease from the previous number of 46.706 Basis Point for Jan 2025. International Indicators: Malaysia 5-Year Credit Default Swap (CDS) data is updated monthly, averaging 72.975 Basis Point from Jan 2012 (Median) to Feb 2025, with 156 observations. The data reached an all-time high of 238.823 Basis Point in Sep 2015 and a record low of 34.758 Basis Point in Dec 2019. International Indicators: Malaysia 5-Year Credit Default Swap (CDS) data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI002: Financial System Statistics: Macroeconomic Indicator.

Graph and download economic data for Delinquency Rate on Credit Card Loans, All Commercial Banks (DRCCLACBS) from Q1 1991 to Q1 2025 about credit cards, delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

The indicator reports the number of credits in progress during the year (without taking into account the year of signature of the contract) to the population aged 18 and over. All loans are registered with the National Bank (including credit openings of less than 1250 euros and repayable within 3 months, which mainly concern overdraft possibilities on bank account). Having credit is therefore not necessarily an indicator of "over-indebtedness risk". At the end of 2013, only 7.3% of Walloons with outstanding credits are in default of payment for credit. Note: the data at contract level are disseminated by postal code on the website of the personal credit centre. They have been aggregated at the municipal level by IWEPS. It is possible that this aggregation leads to some double counting. When a credit is taken out by several people who do not live in the same postal code, the data are included in the file for each of the postal codes concerned. If two contractors live in the same municipality but not in the same postal code, there will be duplication in the information related to the credit (amount, number, ...). These cases are probably rare because loans to several borrowers most often concern people domiciled at the same address. See also: - the website of the National Bank of Belgium (NBB), "\2".

Context

A retail bank would like to hire you to build a credit default model for their credit card portfolio. The bank expects the model to identify the consumers who are likely to default on their credit card payments over the next 12 months. This model will be used to reduce the bank’s future losses. The bank is willing to provide you with some sample datathat they can currently extract from their systems. This data set (credit_data.csv) consists of 13,444 observations with 14 variables.

Content

Based on the bank’s experience, the number of derogatory reports is a strong indicator of default. This is all that the information you are able to get from the bank at the moment. Currently, they do not have the expertise to provide any clarification on this data and are also unsure about other variables captured by their systems

The ratio of non-performing loans (NLP) to total gross loans in Finland decreased by 0.1 percentage points (-6.8 percent) in 2022 in comparison to the previous year. Nevertheless, the last two years recorded a significantly higher ratio than the preceding years.A nonperforming loan is a loan that is in default because the borrower has not made the scheduled payments for a specified period. A bank loan is considered non-performing when more than 90 days pass without the borrower paying the agreed instalments or interest.Find more statistics on other topics about Finland with key insights such as number of commercial bank branches, number of automated teller machines (ATMs), and ratio of bank capital and reserves to total assets.

In 2022, the student loan default rate in the United States was highest for borrowers in the bottom ** percent of the family income bracket, at ** percent. In comparison, borrowers in the top 25 percent were least likely to default on their student loans.