Context

The Federal Reserve sets interest rates to promote conditions that achieve the mandate set by the Congress — high employment, low and stable inflation, sustainable economic growth, and moderate long-term interest rates. Interest rates set by the Fed directly influence the cost of borrowing money. Lower interest rates encourage more people to obtain a mortgage for a new home or to borrow money for an automobile or for home improvement. Lower rates encourage businesses to borrow funds to invest in expansion such as purchasing new equipment, updating plants, or hiring more workers. Higher interest rates restrain such borrowing by consumers and businesses.

Content

This dataset includes data on the economic conditions in the United States on a monthly basis since 1954. The federal funds rate is the interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight. The rate that the borrowing institution pays to the lending institution is determined between the two banks; the weighted average rate for all of these types of negotiations is called the effective federal funds rate. The effective federal funds rate is determined by the market but is influenced by the Federal Reserve through open market operations to reach the federal funds rate target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds target rate; the target rate transitioned to a target range with an upper and lower limit in December 2008. The real gross domestic product is calculated as the seasonally adjusted quarterly rate of change in the gross domestic product based on chained 2009 dollars. The unemployment rate represents the number of unemployed as a seasonally adjusted percentage of the labor force. The inflation rate reflects the monthly change in the Consumer Price Index of products excluding food and energy.

Acknowledgements

The interest rate data was published by the Federal Reserve Bank of St. Louis' economic data portal. The gross domestic product data was provided by the US Bureau of Economic Analysis; the unemployment and consumer price index data was provided by the US Bureau of Labor Statistics.

Inspiration

How does economic growth, unemployment, and inflation impact the Federal Reserve's interest rates decisions? How has the interest rate policy changed over time? Can you predict the Federal Reserve's next decision? Will the target range set in March 2017 be increased, decreased, or remain the same?

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 interest rate set by the Federal Reserve is a crucial tool for promoting economic conditions that meet the mandate established by the United States Congress, which includes high employment, low and stable inflation, sustainable economic growth, and the moderation of long-term interest rates. The interest rates determined by the Fed directly influence the cost of credit, making financing either more accessible or more restrictive. When interest rates are low, there is a greater incentive for consumers to purchase homes through mortgages, finance automobiles, or undertake home renovations. Additionally, businesses are encouraged to invest in expanding their operations, whether by purchasing new equipment, modernizing facilities, or hiring more workers. Conversely, higher interest rates tend to curb such activity, discouraging borrowing and slowing economic expansion.

The dataset analyzed contains information on the economic conditions in the United States on a monthly basis since 1954, including the federal funds rate, which represents the percentage at which financial institutions trade reserves held at the Federal Reserve with each other in the interbank market overnight. This rate is determined by the market but is directly influenced by the Federal Reserve through open market operations to reach the established target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds rate target, which has been defined within a range with upper and lower limits since December 2008.

Furthermore, real Gross Domestic Product (GDP) is calculated based on the seasonally adjusted quarterly rate of change in the economy, using chained 2009 dollars as a reference. The unemployment rate represents the seasonally adjusted percentage of the labor force that is unemployed. Meanwhile, the inflation rate is determined by the monthly change in the Consumer Price Index, excluding food and energy prices for a more stable analysis of core inflation.

The interest rate data was sourced from the Federal Reserve Bank of St. Louis' economic data portal, while GDP information was provided by the U.S. Bureau of Economic Analysis, and unemployment and inflation data were made available by the U.S. Bureau of Labor Statistics.

The analysis of this data helps to understand how economic growth, the unemployment rate, and inflation influence the Federal Reserve’s monetary policy decisions. Additionally, it allows for a study of the evolution of interest rate policies over time and raises the question of how predictable the Fed’s future decisions may be. Based on observed trends, it is possible to speculate whether the target range set in March 2017 will be maintained, lowered, or increased, considering the prevailing economic context and the challenges faced in conducting U.S. monetary policy.

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.

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.; ;

The benchmark interest rate in Russia was last recorded at 16.50 percent. This dataset provides the latest reported value for - Russia Interest 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 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.

UK Mortgage Rates

Mortgage products in the united kingdom

By Jeff [source]

About this dataset

This dataset contains information on thousands of mortgage products available in the UK, including the interest rate, APR, revert rate, fees, and initial rate period. This data can be used to compare different mortgage products and find the best deal for your needs

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains information on thousands of mortgage products available in the UK, including the interest rate, APR, revert rate, fees, and initial rate period.

To use this dataset, simply download it and then import it into your favorite spreadsheet program. You can then use the data to compare mortgage rates across different products and banks.

This dataset can be used to help you: - Compare mortgage rates from different banks - Find the best mortgage product for your needs - Understand how fees and other charges affect the overall cost of a mortgage

Research Ideas

• Analysing the different mortgage products available on the market
• Benchmarking against other products in order to get a competitive rate
• Finding products that have low fees and revert rates

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: UK_Mortgage_Rate.csv | Column name | Description | |:----------------------------|:----------------------------------------------------------------| | SKU | The product's SKU. (String) | | BANK_NAME | The name of the bank that offers the mortgage product. (String) | | MTG_PRODUCT_SUBTITLE | The subtitle of the mortgage product. (String) | | MTG_PRODUCT_TYPE_RAW | The raw product type of the mortgage product. (String) | | MTG_PRODUCT_YEARS | The number of years of the mortgage product. (Integer) | | MTG_INITIAL_RATE_PCT | The initial rate percentage of the mortgage product. (Float) | | MTG_APR_PCT | The APR percentage of the mortgage product. (Float) | | MTG_REVERT_RATE | The revert rate of the mortgage product. (Float) | | MTG_FEES_TOTAL | The total fees of the mortgage product. (Float) | | MTG_INITIAL_RATE_MONTHS | The initial rate months of the mortgage product. (Integer) | | SCAN_DATE | The date that the mortgage product was scanned. (Date) |

Acknowledgements

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

🏦 Synthetic Loan Approval Dataset

A Realistic, High-Quality Dataset for Credit Risk Modelling

🎯 Why This Dataset?

Most loan datasets on Kaggle have unrealistic patterns where:

1. ❌ Credit scores don't matter
2. ❌ Approval logic is backwards
3. ❌ Models learn nonsense patterns

Unlike most loan datasets available online, this one is built on real banking criteria from US and Canadian financial institutions. Drawing from 3 years of hands-on finance industry experience, the dataset incorporates realistic correlations and business logic that reflect how actual lending decisions are made. This makes it perfect for data scientists looking to build portfolio projects that showcase not just coding ability, but genuine understanding of credit risk modelling.

📊 Dataset Overview

🔑 Key Features

Identifier:

-Customer ID (unique identifier for each application)

Demographics:

-Age, Occupation Status, Years Employed

Financial Profile:

-Annual Income, Credit Score, Credit History Length -Savings/Assets, Current Debt

Credit Behaviour:

-Defaults on File, Delinquencies, Derogatory Marks

Loan Request:

-Product Type, Loan Intent, Loan Amount, Interest Rate

Calculated Ratios:

-Debt-to-Income, Loan-to-Income, Payment-to-Income

💡 What Makes This Dataset Special?

1️⃣ Real-World Approval Logic The dataset implements actual banking criteria: - DTI ratio > 50% = automatic rejection - Defaults on file = instant reject - Credit score bands match real lending thresholds - Employment verification for loans ≥$20K

2️⃣ Realistic Correlations - Higher income → Better credit scores - Older applicants → Longer credit history - Students → Lower income, special treatment for small loans - Loan intent affects approval (Education best, Debt Consolidation worst)

3️⃣ Product-Specific Rules - Credit Cards: More lenient, higher limits - Personal Loans: Standard criteria, up to $100K - Line of Credit: Capped at $50K, manual review for high amounts

4️⃣ Edge Cases Included - Young applicants (age 18) building first credit - Students with thin credit files - Self-employed with variable income - High debt-to-income ratios - Multiple delinquencies

🎓 Perfect For - Machine Learning Practice: Binary classification with real patterns - Credit Risk Modelling: Learn actual lending criteria - Portfolio Projects: Build impressive, explainable models - Feature Engineering: Rich dataset with meaningful relationships - Business Analytics: Understand financial decision-making

📈 Quick Stats

Approval Rates by Product - Credit Card: 60.4% more lenient) - Personal Loan: 46.9 (standard) - Line of Credit: 52.6% (moderate)

Loan Intent (Best → Worst Approval Odds) 1. Education (63% approved) 2. Personal (58% approved) 3. Medical/Home (52% approved) 4. Business (48% approved) 5. Debt Consolidation (40% approved)

Credit Score Distribution - Mean: 644 - Range: 300-850 - Realistic bell curve around 600-700

Income Distribution - Mean: $50,063 - Median: $41,608 - Range: $15K - $250K

🎯 Expected Model Performance

With proper feature engineering and tuning: - Accuracy: 75-85% - ROC-AUC: 0.80-0.90 - F1-Score: 0.75-0.85

Important: Feature importance should show: 1. Credit Score (most important) 2. Debt-to-Income Ratio 3. Delinquencies 4. Loan Amount 5. Income

If your model shows different patterns, something's wrong!

🏆 Use Cases & Projects

Beginner - Binary classification with XGBoost/Random Forest - EDA and visualization practice - Feature importance analysis

Intermediate - Custom threshold optimization (profit maximization) - Cost-sensitive learning (false positive vs false negative) - Ensemble methods and stacking

Advanced - Explainable AI (SHAP, LIME) - Fairness analysis across demographics - Production-ready API with FastAPI/Flask - Streamlit deployment with business rules

⚠️ Important Notes

This is SYNTHETIC Data - Generated based on real banking criteria - No real customer data was used - Safe for public sharing and portfolio use

Limitations - Simplified approval logic (real banks use 100+ factors) - No temporal component (no time series) - Single country/currency assumed (USD) - No external factors (economy, market conditions)

Educational Purpose This dataset is designed for: - Learning credit risk modeling - Portfolio projects - ML practice - Understanding lending criteria

NOT for: - Actual lending decisions - Financial advice - Production use without validation

🤝 Contributing

Found an issue? Have suggestions? - Open an issue on GitHub - Suggest i...

Unternehmensfinanzierung. Nutzung von Krediten. Schwierigkeiten bei Kreditaufnahme. Vorgehen von Kreditinstituten in Bezug auf Finanzierungsmöglichkeiten. Finanzierungsberatung. Themen: Finanzielle Situation des Unternehmens; 3-Jahres Plan; wichtigste Maßnahmen zur Festigung des Unternehmens: qualifizierte Mitarbeiter, der Branche angepasstere soziale und steuerliche Bestimmungen, größere Produktionskapazität, einfacher Zugang zu Finanzierungsmitteln, strengere Regulierung der Konkurrenz aus Nicht-EU-Ländern, Beratung und Unterstützung für die Unternehmensentwicklung; Inanspruchnahme finanzieller Leistungen (Dispositionskredit, Leasing/Mieten, Diskont/Factoring, Kapitalerhöhung für Wagniskapitalfonds und für Privatpersonen, (Kurz-)Darlehen, öffentliche Fördermittel); Höhe des letzten Kreditantrags; Verwendungsabsicht für den Kredit; Schwierigkeiten einen Kredit unter 250.000 Euro zu bekommen im Vergleich zu anderen Finanzierungsformen; Gründe für Kreditaufnahme (niedrigere Zinssätze, einfacherer Bewilligungsvorgang, geringere Anforderung auf Kreditsicherheit, kürzere Bearbeitungszeit für Kreditbewilligung); Einschätzung der Unternehmensfinanzierung als ausreichend für Projektrealisierung; primäre Anlaufstellen für den Erhalt von Finanzmitteln; Erschließungsmöglichkeiten für Kapital um finanzielle Bedürfnisse des Unternehmens zu erfüllen; Wege für die Kapitalerschließung des Unternehmens; Einschätzung der Schwierigkeit heutzutage einen Kredit bei Banken zu bekommen im Vergleich zu früher; Gründe, warum es heutzutage schwieriger ist einen Kredit bei einer Bank zu bekommen; Einstellung zu: Kreditabhängigkeit bei Durchführung von Projekten, nicht auf die Belange des Unternehmens zugeschnittene Angebote der Banken; geringe Risikobereitschaft von Banken bei Kreditvergabe; Verständnis für die spezifischen Belange der eigenen Branche durch den zuständigen Bankangestellten; ausreichende Unterstützung bei der Finanzierung durch die Bank; Beurteilung des firmeninternen Finanzmanagements; primäre Anlaufstelle für Finanzierungsberatung. Demographie: Angaben zum Unternehmen: Anzahl der Mitarbeiter, Entwicklung der Anzahl der Beschäftigten seit 2004, Unternehmensgröße, Hauptgeschäftsfeld des Unternehmens, Gründungsjahr, Aktienanteil des Unternehmens; Jahresumsatz des Unternehmens im letzten Geschäftsjahr. Zusätzlich verkodet wurde: Land; Befragten-ID; Interviewsprache; Gewichtungsfaktor. Access to finance of small and medium enterprises. Topics: development of the following indicators in the last six months: turnover, profit, profit margin, level of debt, cash flow, investment, level of exports, research and development, market share; existence of a development plan for the next three years; most important element to ensure the company’s development: better qualified people on the market, social and fiscal regulations more suited to the sector of activity, greater production capacity, easy access to means of financing, stricter regulation regarding competition from outside the EU, advice and support service for the development of the company; use of selected types of financing in the past: overdraft, leasing or renting, discount or factoring, increase in capital dedicated to venture capital funds or to private individuals, loans shorter or longer than a 3-year term, public subsidies; approximate amount of last loan; recent request for a loan less than 25000 €; needs to be met by this loan; assessment of the difficulties to obtain a loan less than 25000 € compared to other forms of company’s financing; most important elements to resort a loan less than 25000 €: lower interest rates, simpler procedures for granting loans, less demanding on guarantee requirements, shorter delays for granting loans; assessment of the current financing of the company as sufficient; institutions contacted to obtain financing: banks, public institutions, private financing companies, leasing or renting companies, venture capital companies, private investors; expectations regarding the increase of the company’s capital within the next years; measures to increase the company’s capital: opening-up capital to private individual investors or to venture capital companies, management buy-out, going on the stock exchange, opening-up capital to the company’s employees; assessment of the access to bank loans as easy; assessment of the development of the impediments to access bank loans compared to a few years ago; reasons that impede obtaining a bank loan compared to a few years ago: interest rates are too high, banks request too much information, loan granting procedures are too long, administrative side of the loan application is very demanding; approval of the following statements: loan is needed to conclude projects, unsuitable offers from banks, risk-averseness of banks, banker understands specifics of the company’s sector, banker sufficiently supports the company in terms of its financing; assessment how the company’s needs regarding financial management are met internally; preferred sources of information on financing. Demography: information about the company: number of employees, development of the number of employees since 2004; company size; main activity of the company; year of company establishment; shareholding of the company; turnover of the company in the own country in the last fiscal year. Additionally coded was: country; respondent ID; language of the interview; weighting factor.

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

A traditional way of thinking about the exchange rate regime and capital account openness has been framed in terms of the 'impossible trinity' or 'trilemma', according to which policymakers can only have two of three possible outcomes: open capital markets, monetary independence and pegged exchange rates. The present paper is a natural extension of Escude (A DSGE Model for a SOE with Systematic Interest and Foreign Exchange Policies in Which Policymakers Exploit the Risk Premium for Stabilization Purposes, 2013), which focuses on interest rate and exchange rate policies, since it introduces the third vertex of the 'trinity' in the form of taxes on private foreign debt. These affect the risk-adjusted uncovered interest parity equation and hence influence the SOE's international financial flows. A useful way to illustrate the range of policy alternatives is to associate them with the faces of an isosceles triangle. Each of three possible government intervention policies taken individually (in the domestic currency bond market, in the foreign currency market, and in the foreign currency bonds market) corresponds to one of the vertices of the triangle, each of the three possible pairs of intervention policies corresponds to one of the three edges of the triangle, and the three simultaneous intervention policies taken jointly correspond to the triangle's interior. This paper shows that this interior, or 'pos sible trinity' is quite generally not only possible but optimal, since the central bank obtains a lower loss when it implements a policy with all three interventions.

Lending Rate: per Month: Pre-Fixed: Individuals: Mortgages with Market Rates: Deutsche Bank S.A. - Banco Alemao 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. Lending Rate: per Month: Pre-Fixed: Individuals: Mortgages with Market Rates: Deutsche Bank S.A. - Banco Alemao 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. Lending Rate: per Month: Pre-Fixed: Individuals: Mortgages with Market Rates: Deutsche Bank S.A. - Banco Alemao 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

Net-Interest-Income Time Series for Veritex Holdings Inc. Veritex Holdings, Inc. operates as the bank holding company for Veritex Community Bank that provides various commercial banking products and services to small and medium-sized businesses and professionals in the United States. The company accepts deposit products, such as demand, savings, money market, and time accounts. Its loan products include commercial real estate (CRE) and general commercial, owner and non-owner occupied CRE, mortgage warehouse loans, residential real estate, construction and land, farmland, 1-4 family residential, agricultural, multi-family residential, and consumer loans, as well as purchased receivables financing. The company also provides interest rate swap services; and a range of online banking solutions, such as access to account balances, online transfers, online bill payment and electronic delivery of customer statements, and ATMs, as well as mobile banking, mail, and personal appointment. In addition, it offers debit cards, night depository services, direct deposits, cashier's checks, and letters of credit; treasury management services, including balance reporting, transfers between accounts, wire transfer initiation, automated clearinghouse origination, and stop payments; and cash management deposit products and services consisting of lockbox, remote deposit capture, positive pay, reverse positive pay, account reconciliation services, zero balance accounts, and sweep accounts, including loan sweep. Veritex Holdings, Inc. was founded in 2004 and is headquartered in Dallas, Texas.

Context

Term deposits are a major source of income for a bank. A term deposit is a cash investment held at a financial institution. Your money is invested for an agreed rate of interest over a fixed amount of time, or term. The bank has various outreach plans to sell term deposits to their customers such as email marketing, advertisements, telephonic marketing, and digital marketing.

Telephonic marketing campaigns still remain one of the most effective way to reach out to people. However, they require huge investment as large call centers are hired to actually execute these campaigns. Hence, it is crucial to identify the customers most likely to convert beforehand so that they can be specifically targeted via call.

The data is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe to a term deposit (variable y).

Content

The data is related to the direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed by the customer or not. The data folder contains two datasets:-

• train.csv: 45,211 rows and 18 columns ordered by date (from May 2008 to November 2010)
• test.csv: 4521 rows and 18 columns with 10% of the examples (4521), randomly selected from train.csv

Detailed Column Descriptions

bank client data:

1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success")

Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no")

Missing Attribute Values: None

Citation

This dataset is publicly available for research. It has been picked up from the UCI Machine Learning with random sampling and a few additional columns.

Please add this citation if you use this dataset for any further analysis.

S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

Past Usage

The full dataset was described and analyzed in:

• S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology.
• In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.

Acknowledgement

Created by: Paulo Cortez (Univ. Minho) and Sérgio Moro (ISCTE-IUL) @ 2012. Thanks to Berkin Kaplanoğlu for helping with the proper column descriptions.

Return-On-Assets Time Series for Byline Bancorp Inc. Byline Bancorp, Inc. operates as the bank holding company for Byline Bank that provides various banking products and services for small and medium sized businesses, commercial real estate and financial sponsors, and consumers in the United States. It offers various retail deposit products, including non-interest-bearing accounts, money market demand accounts, savings accounts, interest-bearing checking accounts, and time deposits; ATM and debit cards; and online, mobile, and text banking services, as well as commercial deposits. The company also provides term loans, revolving lines of credit, and construction financing services; senior secured financing solutions to private equity backed lower middle market companies; small business administration and the United States department of agriculture loans; and treasury management products and services, such as treasury services, information reporting, fraud management, cash collection, and interest rate derivative products. In addition, it offers financing solutions for equipment vendors and their end users; syndication services; and investment, trust, and wealth management services that include fiduciary and executor services, financial planning solutions, investment advisory services, and private banking services for foundations and endowments, and high net worth individuals. The company was formerly known as Metropolitan Bank Group, Inc. and changed its name to Byline Bancorp, Inc. in 2015. Byline Bancorp, Inc. was founded in 1914 and is headquartered in Chicago, Illinois.

The benchmark interest rate in Switzerland was last recorded at 0 percent. This dataset provides - Switzerland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Stock Price Time Series for Byline Bancorp Inc. Byline Bancorp, Inc. operates as the bank holding company for Byline Bank that provides various banking products and services for small and medium sized businesses, commercial real estate and financial sponsors, and consumers in the United States. It offers various retail deposit products, including non-interest-bearing accounts, money market demand accounts, savings accounts, interest-bearing checking accounts, and time deposits; ATM and debit cards; and online, mobile, and text banking services, as well as commercial deposits. The company also provides term loans, revolving lines of credit, and construction financing services; senior secured financing solutions to private equity backed lower middle market companies; small business administration and the United States department of agriculture loans; and treasury management products and services, such as treasury services, information reporting, fraud management, cash collection, and interest rate derivative products. In addition, it offers financing solutions for equipment vendors and their end users; syndication services; and investment, trust, and wealth management services that include fiduciary and executor services, financial planning solutions, investment advisory services, and private banking services for foundations and endowments, and high net worth individuals. The company was formerly known as Metropolitan Bank Group, Inc. and changed its name to Byline Bancorp, Inc. in 2015. Byline Bancorp, Inc. was founded in 1914 and is headquartered in Chicago, Illinois.

Info

This is a dataset I was given to solve for an interview for a transactions company. It is perfect to practice DAX measures. Dataset: anonymized sample of credit card deposit attempts over a 12-month period. Main problem: It shows a longitudinally decreasing approval rate from 10/1/2020 to 9/26/2021. Note: This means that the approval rate for credit card deposit attempts has been declining over this time period

TOOL You can do this with any tool you like. I used PowerBI and I consider it to be one of the best to solve this exercise.

PARAMETER DESCRIPTIONS

Appr? = Deposit attempts '1' or '0' = approved or declined. CustomerID Co Website = online divisions to which the deposit attempt is directed. Processing Co = credit card processing company that is processing the transaction. (nb: besides processing companies, a few fraud risk filters are also included here). Issuing Bank = bank that has issued the customer's credit card. Amount Attempt Timestamp

QUESTIONS (Qs 1-5 & 8 worth 10 points. Qs 6-7 worth 20 points. Total = 100 points)

1) What is the dataset's approval rate by quarter?

2)How many customers attempted a deposit of $50 in Sept 2021?

3)How much did the group identified in QUESTION 2 successfully deposit during the month?

4)Of the Highest Approval Rate for top 10 banks with the most deposit attempts between $150.00 and $999.99 in 2021?

5)Without performing any analysis, which two parameters would you suspect of causing the successive quarterly decrease in approval rate? Why?

6)Identify and describe 2 main causal factors of the decline in approval rates seen in Q3 2021 vs Q4 2020?

7)Choose one of the main factors identified in QUESTION 6. How much of the approval rate decline seen in Q3 2021 vs Q4 2020 is explained by this factor?

8) If you had more time, which other analyses would you like to perform on this dataset to identify additional causal factors to those identified in QUESTION 6

POWERBI TIPS:

• Try to add the least number of columns. There is no problem with this data but with big datasets more data means slower performance. Make DAX measures instead. 2 • Redefine each question: Picture how to display and make it on the PowerBI. Write what you´ll do. Ex: 1) What is the dataset's approval rate by quarter? = line graph, title = “Approval rate by quarter”. X axis= quarters, y axis = approval rate. • Define each column data type on the PowerBI not the query. This error persists over the years, you may define the type on the query but once you load it changes to the default. • In most datasets add the calendar table. Very useful • GREAT TIP: try to apply the less amount of filters to the visual and use calculated measures instead. You will need them in the future. As the questions start to be more complex • I use this rule for all my reports. Measures starting with "Total" are unfiltered. This means, no matter what the filter they should always be the same. You will use them a lot.

Net-Interest-Income Time Series for Citizens Financial Group, Inc.. Citizens Financial Group, Inc. operates as the bank holding company that provides retail and commercial banking products and services to individuals, small businesses, middle-market companies, large corporations, and institutions in the United States. The company operates through two segments, Consumer Banking and Commercial Banking. The Consumer Banking segment offers deposit products, mortgage and home equity lending products, credit cards, business loans, wealth management, and investment services; and education and point-of-sale finance loans, as well as digital deposit products. This segment serves its customers through telephone service centers, as well as through its online and mobile platforms. The Commercial Banking segment provides various financial products and solutions, including lending and leasing, deposit and treasury management services, foreign exchange, and interest rate and commodity risk management solutions, as well as syndicated loans, corporate finance, mergers and acquisitions, and debt and equity capital markets services. This segment serves multi-family, office, industrial, retail, healthcare, and hospitality sectors. The company was formerly known as RBS Citizens Financial Group, Inc. and changed its name to Citizens Financial Group, Inc. in April 2014. Citizens Financial Group, Inc. was founded in 1828 and is headquartered in Providence, Rhode Island.