The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Concept: Average interest rate of credit operations with prefixed interest rates by source of funds - small-sized enterprise - nonearmarked credit - foreign onlending Source: Credit Information System 26457-average-interest-rate-by-source-of-funds---small-sized-enterprise---nonearmarked-credit---for 26457-average-interest-rate-by-source-of-funds---small-sized-enterprise---nonearmarked-credit---for 0 0 Feedback Thank you! Close Feedback. Sorry. Tell us what happen. The data is out of date. I was unable to access the dataset (specify the resource). Insufficient documentation to understand the data set. The data contains error or inconsistency. Describe Your assessment will be sent to the e-Ouv system as a complaint. Click here if you want to track your progress. Name Email Your statement was sent to the e-Ouv system. Click here for details. Protocol number: Access code: Send to e-Ouv system Close

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Concept: Average interest rate of credit operations with prefixed interest rates by source of funds - small-sized enterprise - earmarked credit - savings deposits for real estate financing operations Source: Credit Information System 26453-average-interest-rate-by-source-of-funds---small-sized-enterprise---earmarked-credit---saving 26453-average-interest-rate-by-source-of-funds---small-sized-enterprise---earmarked-credit---saving 0 0 Feedback Thank you! Close Feedback. Sorry. Tell us what happen. The data is out of date. I was unable to access the dataset (specify the resource). Insufficient documentation to understand the data set. The data contains error or inconsistency. Describe Your assessment will be sent to the e-Ouv system as a complaint. Click here if you want to track your progress. Name Email Your statement was sent to the e-Ouv system. Click here for details. Protocol number: Access code: Send to e-Ouv system Close

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

Concept: Average interest rate of credit operations with prefixed interest rates by source of funds and type of credit - small-sized enterprise - earmarked credit - Honored guarantees Source: Credit Information System 27286-average-interest-rate-by-source-of-funds-and-type-of-credit---small-sized-enterprise---earmar 27286-average-interest-rate-by-source-of-funds-and-type-of-credit---small-sized-enterprise---earmar 0 0 Feedback Thank you! Close Feedback. Sorry. Tell us what happen. The data is out of date. I was unable to access the dataset (specify the resource). Insufficient documentation to understand the data set. The data contains error or inconsistency. Describe Your assessment will be sent to the e-Ouv system as a complaint. Click here if you want to track your progress. Name Email Your statement was sent to the e-Ouv system. Click here for details. Protocol number: Access code: Send to e-Ouv system Close

This paper investigates both the effects of domestic monetary policy and external shocks on fundamental macroeconomic variables in six fast growing emerging economies: Brazil, Russia, India, China, South Africa and Turkey—denoted hereafter as BRICS_T. The authors adopt a structural VAR model with a block exogeneity procedure to identify domestic monetary policy shocks and external shocks. Their research reveals that a contractionary monetary policy in most countries appreciates the domestic currency, increases interest rates, effectively controls inflation rates and reduces output. They do not find any evidence of the price, output, exchange rates and trade puzzles that are usually found in VAR studies. Their findings imply that the exchange rate is the main transmission mechanism in BRICS_T economies. The authors also find that that there are inverse J-curves in five of the six fast growing emerging economies and there are deviations from UIP (Uncovered Interest Parity) in response to a contractionary monetary policy in those countries. Moreover, world output shocks are not a dominant source of fluctuations in those economies.

The benchmark interest rate in the United Kingdom was last recorded at 4.25 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table contains 14 series, with data starting from 1925 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: United States ...), Rate (14 items: 91-day treasury bill yield; Treasury bills at Monday tender; adjusted: 3 month (average);United States treasuries constant maturity: 5 year; United States treasuries constant maturity: long term ...).

Brazil Lending Rate: per Month: Pre-Fixed: Individuals: Mortgages with Market Rates: Portoseg S.A. Credito Financiamento e Investimento data was reported at 0.000 % per Month in 03 Jul 2019. This stayed constant from the previous number of 0.000 % per Month for 02 Jul 2019. Brazil Lending Rate: per Month: Pre-Fixed: Individuals: Mortgages with Market Rates: Portoseg S.A. Credito Financiamento e Investimento data is updated daily, averaging 0.000 % per Month from Jan 2012 (Median) to 03 Jul 2019, with 1817 observations. The data reached an all-time high of 0.000 % per Month in 03 Jul 2019 and a record low of 0.000 % per Month in 03 Jul 2019. Brazil Lending Rate: per Month: Pre-Fixed: Individuals: Mortgages with Market Rates: Portoseg S.A. Credito Financiamento e Investimento data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Interest and Foreign Exchange Rates – Table BR.MB011: Lending Rate: per Month: by Banks: Pre-Fixed: Individuals: Mortgages with Market Rates. Lending Rate: Daily: Interest rates disclosed represent the total cost of the transaction to the client, also including taxes and operating. These rates correspond to the average fees in the period indicated in the tables. There are presented only institutions that had granted during the period determined. In general, institutions practicing different rates within the same type of credit. Thus, the rate charged to a customer may differ from the average. Several factors such as the time and volume of the transaction, as well as the guarantees offered, explain the differences between interest rates. Certain institutions grant allowance of the use of the term overdraft. However, this is not considered in the calculation of rates of this type. It should be noted that the overdraft is a modality that has high interest rates. Thus, its use should be restricted to short periods. If the customer needs resources for a longer period, should find ways to offer lower rates. The Brazilian Central Bank publishes these data with a delay about 20 days with relation to the reference period, thus allowing sufficient time for all Financial Institutions to deliver the relevant information. Interest rates presented in this set of tables correspond to averages weighted by the values of transactions conducted in the five working days specified in each table. These rates represent the average effective cost of loans to customers, consisting of the interest rates actually charged by financial institutions in their lending operations, increased tax burdens and operational incidents on the operations. The interest rates shown are the average of the rates charged in the various operations performed by financial institutions, in each modality. In one discipline, interest rates may differ between customers of the same financial institution. Interest rates vary according to several factors, such as the value and quality of collateral provided in the operation, the proportion of down payment operation, the history and the registration status of each client, the term of the transaction, among others . Institutions with “zero” did not operate on modalities for those periods or did not provide information to the Central Bank of Brazil. The Central Bank of Brazil assumes no responsibility for delay, error or other deficiency of information provided for purposes of calculating average rates presented in this

Average Interest Rates: Ordinary Deposits data was reported at 0.182 % pa in Apr 2025. This records an increase from the previous number of 0.162 % pa for Mar 2025. Average Interest Rates: Ordinary Deposits data is updated monthly, averaging 0.020 % pa from Oct 1994 (Median) to Apr 2025, with 367 observations. The data reached an all-time high of 0.257 % pa in Oct 1994 and a record low of 0.001 % pa in Feb 2024. Average Interest Rates: Ordinary Deposits data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.M017: Average Interest Rates Posted at Financial Institutions by Type of Deposit. Survey targets is 500 during Oct-1994 to Sep-2007. The targets was narrowed down to 400 during Oct-2007 to Mar-2022. The latest survey targets is around 380 from Apr-2022 till present.

Concept: Average interest rate of credit operations with prefixed interest rates by source of funds and type of credit - small-sized enterprise - nonearmarked credit - Acquired receivables Source: Credit Information System 26578-average-interest-rate-by-source-of-funds-and-type-of-credit---small-sized-enterprise---nonear 26578-average-interest-rate-by-source-of-funds-and-type-of-credit---small-sized-enterprise---nonear 0 0 Feedback Thank you! Close Feedback. Sorry. Tell us what happen. The data is out of date. I was unable to access the dataset (specify the resource). Insufficient documentation to understand the data set. The data contains error or inconsistency. Describe Your assessment will be sent to the e-Ouv system as a complaint. Click here if you want to track your progress. Name Email Your statement was sent to the e-Ouv system. Click here for details. Protocol number: Access code: Send to e-Ouv system Close

DESCRIPTION

Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement:

For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance

Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Content:

Dataset columns and definition:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.

purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

installment: The monthly installments owed by the borrower if the loan is funded.

log.annual.inc: The natural log of the self-reported annual income of the borrower.

dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

fico: The FICO credit score of the borrower.

days.with.cr.line: The number of days the borrower has had a credit line.

revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.

delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Steps to perform:

Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.

Tasks:

1. Feature Transformation

Transform categorical values into numerical values (discrete)

1. Exploratory data analysis of different factors of the dataset.

2. Additional Feature Engineering

You will check the correlation between features and will drop those features which have a strong correlation

This will help reduce the number of features and will leave you with the most relevant features

1. Modeling

After applying EDA and feature engineering, you are now ready to build the predictive models

In this part, you will create a deep learning model using Keras with Tensorflow backend

The benchmark interest rate in Australia was last recorded at 3.85 percent. This dataset provides - Australia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Indonesia was last recorded at 5.50 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table contains 39 series, with data for starting from 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Financial market statistics (39 items: Government of Canada Treasury Bills, 1-month (composite rates); Government of Canada Treasury Bills, 2-month (composite rates); Government of Canada Treasury Bills, 3-month (composite rates);Government of Canada Treasury Bills, 6-month (composite rates); ...).

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Treasury Loans to Government Agencies table represents the specific maturities for the Treasury Loans to Government Agencies rate changes from the previous month to the current month for a set maturity time frame.

Microcredit institutions spend billions of dollars fighting poverty by making small loans primarily to female entrepreneurs. Proponents argue that microcredit mitigates market failures, spurs micro-enterprise growth, and boosts borrowers’ well-being. We tested these hypotheses with the use of an innovative, replicable experimental design that randomly assigned individual liability microloans (of $225 on average) to 1601 individuals in the Philippines through credit scoring. After 11 to 22 months, we found evidence consistent with unmet demand at the current price (a roughly 60% annualized interest rate): Net borrowing increased in the treatment group relative to controls. However, the number of business activities and employees in the treatment group decreased relative to controls, and subjective well-being declined slightly. We also found little evidence that treatment effects were more pronounced for women. However, we did find that microloans increase ability to cope with risk, strengthen community ties, and increase access to informal credit. Thus, microcredit here may work, but through channels different from those often hypothesized by its proponents.