The benchmark interest rate in the United States was last recorded at 4 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.

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.

The benchmark interest rate in Sweden was last recorded at 1.75 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data was reported at 6.566 % in Mar 2025. This records an increase from the previous number of 6.446 % for Dec 2024. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data is updated quarterly, averaging 6.330 % from Mar 2012 (Median) to Mar 2025, with 53 observations. The data reached an all-time high of 6.961 % in Sep 2023 and a record low of 4.454 % in Mar 2022. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Business and Economic Survey – Table ID.SE003: Banking Survey: Interest Rate. [COVID-19-IMPACT]

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Aug 2025 about savings, personal, rate, and USA.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Monthly and long-term Mexico Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term Japan Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term India Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Nepal NP: Real Interest Rate data was reported at -6.207 % pa in 2010. This records an increase from the previous number of -6.823 % pa for 2009. Nepal NP: Real Interest Rate data is updated yearly, averaging 3.657 % pa from Dec 1975 (Median) to 2010, with 29 observations. The data reached an all-time high of 18.214 % pa in 1977 and a record low of -12.173 % pa in 1975. Nepal NP: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nepal – Table NP.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.; ;

The benchmark interest rate in Turkey was last recorded at 39.50 percent. This dataset provides the latest reported value for - Turkey Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table contains 38 series, with data starting from 1957 (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), Rates (38 items: Bank rate; Chartered bank administered interest rates - prime business; Chartered bank - consumer loan rate; Forward premium or discount (-), United States dollars in Canada: 1 month; ...).

United States US: Lending Interest Rate data was reported at 3.512 % pa in 2016. This records an increase from the previous number of 3.260 % pa for 2015. United States US: Lending Interest Rate data is updated yearly, averaging 6.922 % pa from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 18.870 % pa in 1981 and a record low of 3.250 % pa in 2014. United States US: Lending Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Interest Rates. Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files.; ;

Concept: For the sake of time series organization, exchange rates have been grouped in two segments: I – Administered or free rates, covering the whole period since 1899, and II – Floating rates, which have been in place in the period of January 1989 to January 1999 and coexisted with the first segment. I – Administered or free exchange rates Available since 1899. In this period covered by the time series a great diversity of foreign exchange policies have been adopted. During some times, exchange rates were fixed (i.e. administered) by the monetary authorities, whereas in other times rates were freely agreed by market participants (i.e. they were free) and there were even times when both administered and free rates have existed at the same time. It should also be emphasized that between 1953 and 1961 a system of multiple exchange rates have been in place. For these time series the following kinds of exchange rates have been considered: - From January 1899 to January 1953 – administered rates; - From February 1953 to October 1961 – free rates, coming from the Exchange Portfolio of the Banco do Brasil. In this period administered rates have also been in place, with sell rates fixed on: CR$ 18,72, from Feb/1953 to Jul/1953; CR$ 18,82, from Aug/1953 to Dec/1958; and CR$ 18,92, from Jan/1959 to Feb/1961. In the beginning of the period most transactions were channeled through the administered rates system. As time went by, the number of transactions going through the free rates system grew. - From November 1961 to February 1990 – administered rates; and - From March 1990 onwards, free rates (Resolution 1.690 from 18.3.1990). The corresponding time series are the following ones: - Commercial dollar (sell and buy) – daily rates Available from 28.11.1984 onwards, refers to administered rates up to March 13th 1990 and to free rates from this date on (Resolution 1.690 from 18.3.1990). Administered rates are the ones fixed by the Central Bank. Free rates are the average of the rates of transactions effectively closed in the interbank market, weighted by the volume of sell transactions in the day. Outliers and rates presenting evidence of manipulation or other violations of the generally accepted market practices are excluded from the calculation. From March 1992 on, this rate was named PTAX. The series “American dollar – buy and sell – end of period” and “American dollar – buy and sell – period average” are derived respectively from these buy and sell daily rates. - American dollar – end of period Refers to the dollar administered rates expressed in Mil-réis for the period 1899-1941. The Mil-réis/dollar rates for the period 1899-1921 were computed from the pound/dollar parity. Discontinued in 1941. - American dollar (buy and sell) – end of period Annual rates are available from 1942 on and monthly rates from January 1953 on. End of period values correspond to the daily rate of the reference period´s last day. - American dollar (buy and sell) – period average Annual rates are available from 1942 on and monthly rates from January 1953 on. Buy and sell average rates are computed from the reference period daily rates. Monthly and annual rates were computed based on the running days of the reference up until December 1973. From January 1974 on, rates were weighted by the working days. II – Floating exchange rates Created by the Resolution 1.552 from 22.12.1988, this segment of the exchange market allowed markets participants to freely agree on the price of the foreign currency being negotiated. It initially covered only transactions related to international travel motivated by tourism, business, education and health. Later, other kinds of transactions were incorporated in the segment, such as gold, Brazilian investments abroad, unilateral transfers and some services. On 31.1.1999 this segment was terminated and the free and floating rates were merged. Series related to this segment are the following: - Tourism dollar (sell) Daily rates in the floating rate segment, available for the period between 27.5.1993 to 29.1.1999. The computation of this rate takes into account transactions in the interbank market weighted by the volume of sell transactions. Outliers and rates presenting evidence of manipulation or other violations of the generally accepted market practices are excluded from the computation. The series “American dollar – buy and sell – end of period” and “American dollar – buy and sell – period average” are derived respectively from these buy and sell daily rates. - American dollar (buy and sell) – end of period Rates for the last day of the reference period, computed for both buy and sell transactions. - American dollar (buy and sell) – period average Average of the daily rates of the reference period (month or year), computed for buy and sell transactions, weighted by the number of working days. Source: Central Bank Information System – PTAX800 transaction ee2d2d33-2788-458c-9b1b-506150cfd4d1 10813-exchange-rate---free---united-states-dollar-buy

Hong Kong HK: Real Interest Rate data was reported at 3.288 % pa in 2016. This records an increase from the previous number of 1.309 % pa for 2015. Hong Kong HK: Real Interest Rate data is updated yearly, averaging 3.551 % pa from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 13.347 % pa in 2000 and a record low of -3.093 % pa in 1992. Hong Kong HK: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong SAR – Table HK.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.; ;

Predicting Portuguese Bank Term Deposit Subscriptions

Identifying Likely Customers for Conversion Optimization

By Huggingface Hub [source]

About this dataset

This innovative dataset is gathered from a Portuguese banking institution's direct marketing campaigns to identify customers who are likely to subscribe to a term deposit, with the ultimate goal of maximizing their conversion rates. With the utilization of telephonic marketing campaigns, this bank has sought out information on individual selection characteristics such as age, job type, marital status, educational level, default history and banking balances that could potentially bring insight into what renders somebody more or less likely to subscribe. The dataset produced provides detailed data on customer contact day and duration in order to answer questions surrounding customer inclination towards the term deposit offers made in these telemarketing campaigns. Furthermore it also considers previous outcomes from similar calls with the same customer as part of its featureset. With all this knowledge at hand we are thus presented with an opportunity to drastically augment conversion success rate through learning which factors yield positive results when attempting to attract new customers for this particular product offering

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to gain insights into the direct marketing campaigns of a Portuguese banking institution, and to better understand customer subscription behaviors to their term deposits. The objective is to use this data to predict which customers are most likely to subscribe a term deposit, as well as maximize conversion through this information.

In order to take advantage of the dataset provided, we suggest using tools such as classification and predictive modeling methods. These methods will enable you to perform an assessment of the likelihood of customer subscription, given certain parameters in regards to their self-reported information and interactions with the bank itself. For example, you may run regression models in order determine which charactersitics are significantly correlated with subscription rates so that you can appropriately targeted your effective marketing strategies more accurately. Furthermore, various machine learning techniques could also be employed for building predictive models that scale over time so that customer trends can be identified on a continual basis.

Overall, this dataset is useful for understanding customers’ engagement and how they interact with banking services- it provides valuable insights for predicting whom among potential customers have a tendency towards subscribing terms deposits at banks institutions- For example from exploring duration or day parameters about phone calls or from balance , age or job parameter combo. by doing categorizing potential customers under respective segments according them regarding education level or marital status; This provides ample opportunity con develop custom tailored solutions based on real world data gathering methods and application scenarios like feature scaling etc.. Moreover it is equally applicable large scale cost optimization measures such search engine campaigns – because different categories population demographics exhibit certain trend patterns related offers pertaining interest areas based on past analytics enables instant decision making process at disposal marketers what segment needs more promotion etc.. Welcome Every one And Explore Dataset further !! maximize your profit strategy tailor made real word scenarios…

Research Ideas

• Developing customer segments with similar profiles, and developing targeted campaigns to reach them. This could include gathering customer behaviors, preferences, and other demographic information into categories to help identify the most specific target markets for successful conversion.
• Utilizing machine learning algorithms such as Random Forest or Decision Tree models to create a predictive model that can forecast which customers are likely to subscribe. This could be utilized as a tool when evaluating potential prospects and only targeting those with the highest probability of conversion rate optimization.
• Creating a goal-based system by setting up milestones in an effort to increase customer retention rate or offer existing subscribers further incentives might prove beneficial for better converting campaigns in lower cost marketing efforts over time

Acknowledgements

If you use this dataset in your research, please credit the original a...

Vietnam VN: Interest Rate Spread data was reported at 2.620 % pa in 2017. This records an increase from the previous number of 2.160 % pa for 2016. Vietnam VN: Interest Rate Spread data is updated yearly, averaging 3.147 % pa from Dec 1993 (Median) to 2017, with 22 observations. The data reached an all-time high of 10.143 % pa in 1993 and a record low of 1.942 % pa in 2010. Vietnam VN: Interest Rate Spread data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Vietnam – Table VN.World Bank.WDI: Interest Rates. Interest rate spread is the interest rate charged by banks on loans to private sector customers minus the interest rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files.; Median;

The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Bank A issues Credit Cards to eligible customers. The Bank deploys advanced ML models and frameworks to decide on eligibility, limit, and interest rate assignment. The models and frameworks are optimized to manage early risk and ensure profitability. The Bank has now decided to build a robust risk management framework for its existing Credit Card customers, irrespective of when they were acquired. To enable this, the Bank has decided to create a “Behaviour Score”. A Behaviour Score is a predictive model. It is developed on a base of customers whose Credit Cards are open and are not past due. The model predicts the probability of customers defaulting on the Credit Cards going forward. This model will then be used for several portfolio risk management activities.

Problem statement

Your objective is to develop the Behaviour Score for Bank A.

Datasets

You have been provided with a random sample of 96,806 Credit Card details in “Dev_data_to_be_shared.zip”, along with a flag (bad_flag) – henceforth known as “development data”. This is a historical snapshot of the Credit Card portfolio of Bank A. Credit Cards that have actually defaulted have bad_flag = 1. You have also been provided with several independent variables. These include: • On us attributes like credit limit (varables with names starting with onus_attributes) • Transaction level attributes like number of transactions / rupee value transactions on various kinds of merchants (variables with names starting with transaction_attribute) • Bureau tradeline level attributes (like product holdings, historical delinquencies) – variables starting with bureau • Bureau enquiry level attributes (like PL enquiries in the last 3 months etc) – variables starting with bureau_enquiry You have also been provided with another random sample of 41,792 Credit Card details in “validation_data_to_be_shared.zip” with the same set of input variables, but without “bad_flag”. This will be referred to going forward as “validation data”.

Requirements

Using the data provided, you will have to come up with a way to predict the probability that a given Credit Card customer will default. You can use the development data for this purpose. You are then required to use the same logic to predict the probability of all the Credit Cards which are a part of the validation data. Your submission should contain two columns – the Primary key from the validation data (account_number), and the predicted probability against that account. You are also required to submit a detailed documentation of this exercise. A good document should contain details about your approach. In this section, you should include a write up on any algorithms that you use. You should then cover each of the steps that you have followed in as much detail as you can. You should then move on to any key insights or observations that you have come across in the data provided to you. Finally, you should write about what metrics you have used to measure the effectiveness of the approach that you have followed.

Evaluation

As detailed in the previous section, you are required to submit the Primary key and predicted probabilities of all the accounts provided to you in the validation data, as well as a documentation. We will only evaluate submissions that are complete and pass sanity checks (probability values should be between 0 and 1 for example). Submissions will be evaluated basis how close the predicted probabilities are to the actual outcome. We will also evaluate the documentation basis it’s completeness and accuracy. Extra points will be granted to submissions that include interesting insights / observations on the data provided.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

This Data consists of some world statistics published by the World Bank since 1961

Variables:

1) Agriculture and Rural development - 42 indicators published on this website. https://data.worldbank.org/topic/agriculture-and-rural-development

2) Access to electricity (% of the population) - Access to electricity is the percentage of the population with access to electricity. Electrification data are collected from industry, national surveys, and international sources.

3) CPIA gender equality rating (1=low to 6=high) - Gender equality assesses the extent to which the country has installed institutions and programs to enforce laws and policies that promote equal access for men and women in education, health, the economy, and protection under law.

4) Mineral rents (% of GDP) - Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate.

5) GDP per capita (current US$) - GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars.

6) Literacy rate, adult total (% of people ages 15 and above)- Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.

7) Net migration - Net migration is the net total of migrants during the period, that is, the total number of immigrants less the annual number of emigrants, including both citizens and noncitizens. Data are five-year estimates.

8) Birth rate, crude (per 1,000 people) - Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.

9) Death rate, crude (per 1,000 people) - Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.

10) Mortality rate, infant (per 1,000 live births) - Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.

11) Population, total - Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.

Acknowledgements

These datasets are publicly available for anyone to use under the following terms provided by the Dataset Source https://www.worldbank.org/en/about/legal/terms-of-use-for-datasets

Banner photo by https://population.un.org/wpp/Maps/

Inspiration

Subsaharan Africa and east Asia record high population total, actually Subsaharan Africa population bypassed Europe and central Asia population by 2010, has this been influenced by crop and food production, large arable land, high crude birth rates(influx), low mortality rates(exits from the population) or Net migration.