Dataset Overview for XYZ Multistate Bank:

This dataset is for XYZ Multistate Bank and contains various columns that capture key aspects of customer behavior and attributes. Each column provides valuable insights into the factors influencing customer churn, with the goal of predicting which customers are most likely to leave the bank. Below is an explanation of each column and its relevance to customer retention.

1. RowNumber:
The "RowNumber" column corresponds to the unique record number for each customer entry. It has no impact on the outcome of customer churn but is used to identify and organize data within the dataset. Since it doesn't contain any meaningful information related to customer behavior, it is not relevant for churn prediction and can be excluded in analysis.

2. CustomerId:
The "CustomerId" column consists of randomly generated identifiers for each customer. While this ID helps to uniquely distinguish each customer, it has no impact on the likelihood of a customer leaving the bank. As a categorical feature, it does not contribute to the analysis of churn and can be omitted when building predictive models.

3. Surname:
The "Surname" column holds the last names of customers. Although this information is useful for identification purposes, it does not have a direct relationship with customer churn. Since a customer's surname is not an influencing factor in their decision to stay or leave the bank, it is not considered relevant for churn prediction and can be disregarded.

4. CreditScore:
"CreditScore" is an important variable that can significantly affect customer churn. Customers with higher credit scores are generally considered more financially stable and less likely to leave the bank, as they are less likely to face issues with financial institutions. Therefore, this feature can provide valuable insights into customer retention and should be included in churn analysis.

5. Geography:
"Geography" refers to the geographical location of the customer, which can influence their likelihood of leaving the bank. Customers living in different regions may have varying experiences with the bank’s services, fees, or offerings, making this an important factor to explore. Understanding regional differences helps tailor retention strategies for specific locations and improve overall customer satisfaction.

6. Gender:
"Gender" is an interesting demographic factor to consider in churn prediction. While gender itself may not directly affect the likelihood of a customer leaving, it could correlate with other behavioral patterns or preferences that influence retention. Analyzing gender in combination with other features may reveal potential insights, making it worthwhile to examine as part of the churn model.

7. Age:
The "Age" column is a key factor in understanding customer behavior. Typically, older customers are less likely to churn because they tend to be more established with their financial institutions and may have a greater sense of loyalty. In contrast, younger customers may be more likely to switch banks, especially if they are seeking better services or offers. This feature is essential for predicting churn and should be analyzed in detail.

8. Tenure:
"Tenure" refers to the number of years a customer has been with the bank. Longer-tenured customers are often more loyal and less likely to leave the bank. The correlation between tenure and churn is strong, as established relationships tend to make customers less susceptible to leaving. This is a critical factor for churn prediction and should be given high consideration when modeling customer retention.

9. Balance:
The "Balance" column reflects the amount of money a customer holds in their bank account. Customers with higher balances are typically more invested in the bank and are less likely to leave. In contrast, customers with low balances may be more willing to switch to other financial institutions offering better rates or services. This feature plays a significant role in churn prediction, as financial stakes are directly tied to loyalty.

10. NumOfProducts:
"NumOfProducts" refers to the number of products (e.g., savings accounts, loans, credit cards) that a customer has with the bank. Customers with multiple products are usually more invested in the bank, making them less likely to leave. The greater the number of products, the higher the customer's commitment to the bank, making this feature highly relevant in understanding churn patterns and developing retention strategies.

11. HasCrCard:
"HasCrCard" indicates whether or not a customer holds a credit card with the bank. Having a credit card typically reduces the likelihood of customer churn, as credit cards are a widely used financial product that locks customers into a long-term relatio...

Beginning June 2015, the NLF had been replaced with the Basel III Liquidity Coverage Ratio framework (LCR). The LCR seeks to ensure that banking institutions hold sufficient high-quality liquid assets (Stock of HQLA) to withstand an acute liquidity stress scenario over a 30-day horizon. LCR is calculated by dividing the amount of Stock of HQLA with the total Net Cash Outflows. Stress assumptions are incorporated into the LCR through haircuts applied to the stock of HQLA and run-off factors applied to the cash flow items. Stock of HQLA refers to Level 1, Level 2A and Level 2B assets which are defined in the framework. Among others, it includes cash, central bank reserves, sovereign bonds/sukuk, corporate bonds/sukuk rated AAA and Cagamas Residential Mortgage-backed Securities (RMBS) and A-rated corporate bonds/sukuk (foreign currency only). Net Cash Outflows refers to the total cash outflows less total cash inflows expected over a 30-day liquidity stress scenario, and which are calculated based on the run-off and inflow rates specified in the framework.

External Assets Amount Due from Designated Financial Institutions refer to claims by reporting institutions on foreign banking institutions. Stock and Shares comprise foreign currency denominated equity instruments of financial assets designated as fair value through profit and loss, financial assets held for trading and financial assets available for sale issued by foreign corporations and held by reporting institutions. Investments include FX foreign government securities held, FX corporate debt securities held and FX other held to maturity investment held by reporting institutions. Loans/Financing and Advances refer to RM and foreign currency loans/financing extended by domestic banks to foreign banking institution and foreign non-bank entities. Other External Assets include RM gold, holdings of foreign currency other assets, investment at cost, cash and balances with banks and other financial institutions and property, plant and equipment. External Liabilities Amounts Due to Designated Financial Institutions refer to claims by foreign banking institutions on the reporting institutions. Deposits Accepted comprise RM and foreign currency deposits accepted and repurchase agreement received from both foreign banking institution and foreign non-bank entities. Bills Payable refers to RM and foreign currency bills payable to non-residents. Other External Liabilities include RM subordinated borrowings, both RM and foreign currency debt certificates/sukuk issued, RM interest/profit payable and RM miscellaneous other liabilities to non-residents.

Cash and Cash Equivalents refer to cash and balances with banks and other financial institutions, short-term deposits which include money at call and other deposits with remaining maturity less than 3 months, and other cash and cash equivalents. Other Deposits Placed and Reverse Repos include reverse repos and deposits with remaining maturity greater than 3 months i.e fixed deposits, specific and general investment accounts placed, clients and remisiers trust monies held as deposits, and other deposits placed. Amounts Due from Designated Financial Institutions refer to conventional and IBS amounts owed by designated financial institutions which are booked in RM overdrawn vostro accounts, RM nostro accounts, RM surplus amount, RM interbank placements, FX nostro accounts and FX interbank placements. Other Non-Banking Institutions refer to non-bank entities, i.e. non-bank financial institutions, business enterprises, government, individuals and other entities. Negotiable Instruments of Deposit Held refer to the holding of RM-denominated negotiable instruments of deposit including Non-SPI NIDs and IBS NIDs issued by other commercial banks and merchant/investment banks. Treasury Bills refer to debt securities issued by the Federal Government. The features include original maturity of less than one year, no interest is payable and the bills are issued at a discount to face value. Government Securities refer to debt-securities issued by the Federal Government. The features include original maturity of more than one year and interest payable periodically, usually semi-annually. Loans and Advances is reported net of provision for impairment from January 2013 onwards. Property, Plant and Equipment is reported net of depreciation and impairment from January 2013 onwards. Malaysian Government Investment Issues (MGII) refers to debt securities issued by the Federal Government. The features include original maturity of more than one year and profit payable periodically.

Central Bank Balance Sheet in the United States decreased to 6587034 USD Million in October 29 from 6589533 USD Million in the previous week. This dataset provides - United States Central Bank Balance Sheet - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Department of Public Services and Procurement Canada, in its role as Receiver General for Canada, is responsible for the management and safeguarding of all federal government money. All funds paid to the Government of Canada are held in a central account at the Bank of Canada, known as the Consolidated Revenue Fund (CRF). The Receiver General uses a centralized banking system (Government Banking System or GBS) to record the inflow of funds to the CRF. Using this system, cash balances at the Bank of Canada are reviewed and approved each day by the Receiver General. This dataset entitled “Daily Cash Balances” provides details, extracted from the GBS, of the balance and amounts held at the Bank of Canada. Updates will be posted quarterly.

Money Supply M2 in the United States increased to 22298.10 USD Billion in October from 22212.50 USD Billion in September of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The FDIC is often appointed as receiver for failed banks. This page contains useful information for the customers and vendors of these banks. This includes information on the acquiring bank (if applicable), how your accounts and loans are affected, and how vendors can file claims against the receivership.

The Federal Reserve Banks provide the Fedwire Funds Service, a real-time gross settlement system that enables participants to initiate funds transfer that are immediate, final, and irrevocable once processed. Depository institutions and certain other financial institutions that hold an account with a Federal Reserve Bank are eligible to participate in the Fedwire Funds Services. In 2008, approximately 7,300 participants made Fedwire funds transfers. The Fedwire Funds Service is generally used to make large-value, time-critical payments.The Fedwire Funds Service is a credit transfer service. Participants originate funds transfers by instructing a Federal Reserve Bank to debit funds from its own account and credit funds to the account of another participant. Participants may originate funds transfers online, by initiating a secure electronic message, or off line, via telephone procedures.

Money Supply M0 in the United States increased to 53615000 USD Million in October from 5478000 USD Million in September of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Total-Cash-From-Operating-Activities Time Series for Seacoast Banking Corporation of Florida. Seacoast Banking Corporation of Florida operates as the bank holding company for Seacoast National Bank that provides integrated financial services to retail and commercial customers in Florida. The company offers noninterest and interest-bearing demand deposits, money market, savings, and customer sweep accounts; time deposits; construction and land development, commercial and residential real estate, and commercial and financial loans; and consumer loans, including installment and revolving lines, as well as loans for automobiles, boats, and personal and family purposes. It also provides wealth management, mortgage, and insurance services through mobile and online banking solutions, as well as brokerage and annuity services. Seacoast Banking Corporation of Florida was founded in 1926 and is headquartered in Stuart, Florida.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

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.

Explore central bank assets data including deposits, loans, cash balances, and more. Analyze investment opportunities in the United Arab Emirates.

Deposits, Loans and Advances, Cash and Bank Balances, Held-To-Maturity Investments, Other Assets, Investment, Loan, Deposit, Money, Bank



United Arab Emirates

Follow data.kapsarc.org for timely data to advance energy economics research..

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1600.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Funds-From-Operation-To-Total-Debt Time Series for Bank First National Corp. Bank First Corporation operates as a holding company for Bank First, N.A. that provides consumer and commercial financial services to businesses, professionals, and consumers, in Wisconsin. It offers checking, savings, money market, cash management, retirement, and health savings accounts; other time deposits; certificates of deposit; and residential mortgage products. The company's loan products include real estate loans, including commercial real estate, residential mortgage, and home equity loans; commercial and industrial loans for working capital, accounts receivable, inventory financing, and other business purposes; construction and development loans; residential 1-4 family loans; and consumer loans for personal and household purposes, including secured and unsecured installment loans, and revolving lines of credit. It also provides credit cards; insurance; data processing and other information technology; investment and safekeeping; treasury management; and online, telephone, and mobile banking services. The company was formerly known as Bank First National Corporation and changed its name to Bank First Corporation in June 2019. Bank First Corporation was founded in 1894 and is headquartered in Manitowoc, Wisconsin.

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.

Banks and financial institutions can make money through financing. For example, they likely earn a profit by issuing home, car, and personal loans and charging interest on those financial products. Thus, banks are often in need of capital to fund the loans. Term deposits can provide locked-in capital for lending institutions.

Here’s how many bank accounts work: When a customer places funds in a term deposit, it’s similar to a loan to the bank. The bank will hold the funds for a set time and can use them to invest elsewhere to make a return. Let’s say the bank gives the initial depositor a return of 2% for the use of funds in a term deposit. The bank can then use the money on deposit for a loan to a customer, charging a 6% interest rate for a net margin of 4%. Term deposits can help keep their financial operation running.

Banks want to maximize their net interest margin (net return) by offering lower interest for term deposits and charging high interest rates for loans. However, borrowers may choose a lender with the lowest interest rate, while CD account holders probably seek the highest rate of return. This dynamic keeps banks competitive.

Therefore, using certain factors we need to classify whether or not a customer subscribes to the term deposit upon getting a call from a bank’s representative.

Dataset Description Customer Details: 1 - Age - Age of the customer and it’s a numerical variable

2 - Job- The type of job a customer does and it’s a categorical variable

3 - Marital : It’s self explanatory. It demonstrates the customer’s marital status and is a categorical variable.

4 - Education - Educational level of a customer and it’s a categorical variable.

5 - Default :Showcases if a cx has credit in default? (Categorical)

6 - housing: Tells you If a cx has a housing loan.(categorical))

7 - loan: Demonstrates you If a cx has a Personal loan (categorical)

8 - contact: contact communication type (categorical)

9 - month: last contact month of year (categorical)

10 - day_of_week: last contact day of the week (categorical)

11 - duration: last contact duration, in seconds (numeric).

12 - campaign: number of contacts performed for this client during this campaign (numeric)

13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric)

14 - previous: number of contacts performed for this client before this campaign(numeric)

15 - poutcome: outcome of the previous marketing campaign (categorical)

16 - empvarrate: employment variation rate - quarterly indicator (numeric)

17 - conspriceidx: consumer price index - monthly indicator (numeric)

18 - consconfidx: consumer confidence index - monthly indicator (numeric)

19 - euribor3m: euribor 3 month rate - daily indicator (numeric)

20 - nremployed: number of employees - quarterly indicator (numeric)

21 - y - has the client subscribed a term deposit? (binary: 'yes','no')

Total-Cashflows-From-Investing-Activities Time Series for City Holding Company. City Holding Company operates as a financial holding company for City National Bank of West Virginia that provides banking, trust and investment management, and other financial solutions in the United States. The company offers checking, savings, and money market accounts, as well as certificates of deposit and individual retirement accounts. It also provides commercial and industrial loans that consist of loans to corporate and other legal entity borrowers primarily in small to mid-size industrial and commercial companies; commercial real estate loans comprising commercial mortgages, which are secured by nonresidential and multi-family residential properties; residential real estate loans to consumers for the purchase or refinance of residence; first-priority home equity loans; home equity lines of credit; amortized home equity loans; consumer loans that are secured and unsecured by automobiles, boats, recreational vehicles, certificates of deposit, and other personal property; and demand deposit account overdrafts, as well as owner-occupied real estate and construction, land development, and lines of credit. In addition, the company offers mortgage banking services, including fixed and adjustable-rate mortgages, construction financing, land loans, production of conventional and government-insured mortgages, secondary marketing, and mortgage servicing. Further, it provides treasury management, lockbox, and other cash management services; merchant credit card services; wealth management, trust, investment, and custodial services for commercial and individual customers; and corporate trust and institutional custody, financial and estate planning, and retirement plan services, as well as automated-teller-machine, interactive-teller-machine, mobile banking, interactive voice response systems, and credit and debit card services. The company was founded in 1957 and is headquartered in Charleston, West Virginia.

At LOL Bank Pvt. Ltd., ensuring the safety and integrity of economic transactions is a top priority. With increasingly more on line transactions and digital banking activities, fraudulent transactions have end up a good sized danger to both the financial institution and its customers. Fraudulent activities, along with unauthorized account get right of entry to, identification robbery, and suspicious transaction patterns, bring about economic losses and harm to patron agree with.

To cope with this developing subject, LOL Bank Pvt. Ltd. Is in search of a strategy to stumble on and save you fraudulent transactions in real time. This includes analyzing ancient transaction records, consisting of account info, transaction quantities, service provider records, and time stamps, to pick out patterns indicative of fraudulent conduct. The intention is to construct a robust fraud detection gadget that may distinguish among legitimate transactions and probably fraudulent ones, with minimal fake positives.

The answer must incorporate device learning algorithms to study from transaction history, allowing the machine to become aware of rising fraud strategies and adapt to evolving threats. The gadget must be able to flag suspicious transactions in real time, providing bank employees with actionable insights to take activate action. By enhancing fraud detection abilities, LOL Bank Pvt. Ltd. Objectives to shield patron belongings, lessen financial losses, and keep its reputation as a secure and honest economic organization.

Here are the information of the columns:

1. Customer_ID: A particular identifier for every customer within the bank's system.
2. Customer_Name: The name of the consumer making the transaction.
3. Gender: The gender of the consumer (e.G., Male, Female, Other). Four. Age: The age of the consumer at the time of the transaction.
4. State: The nation in which the patron resides.
5. City: The metropolis wherein the client is living.
6. Bank_Branch: The specific financial institution branch wherein the consumer holds their account. Eight. Account_Type: The kind of account held with the aid of the customer (e.G., Savings, Checking). Nine. Transaction_ID: A particular identifier for each transaction.
7. Transaction_Date: The date on which the transaction passed off. Eleven. Transaction_Time: The specific time the transaction became initiated.
8. Transaction_Amount: The financial value of the transaction.
9. Merchant_ID: A particular identifier for the merchant worried within the transaction.
10. Transaction_Type: The nature of the transaction (e.G., Withdrawal, Deposit, Transfer).
11. Merchant_Category: The class of the merchant (e.G., Retail, Online, Travel).
12. Account_Balance: The balance of the customer's account after the transaction.
13. Transaction_Device: The tool utilized by the consumer to perform the transaction (e.G., Mobile, Desktop).
14. Transaction_Location: The geographical vicinity (e.G., latitude, longitude) of the transaction.
15. Device_Type: The kind of device used for the transaction (e.G., Smartphone, Laptop).
16. Is_Fraud: A binary indicator (1 or zero) indicating whether or not the transaction is fraudulent or now not.
17. Transaction_Currency: The currency used for the transaction (e.G., USD, EUR).
18. Customer_Contact: The contact variety of the client.
19. Transaction_Description: A brief description of the transaction (e.G., buy, switch).
20. Customer_Email: The e-mail cope with related to the consumer's account.

These column descriptions give a clear expertise of the facts as a way to be used for fraud detection analysis.

Detailed Information

Problem Statement: Fraud Detection in Bank Transactions for LOL Bank Pvt. Ltd.

At LOL Bank Pvt. Ltd., making sure the safety of patron financial transactions is paramount. With the rise of digital banking, the growth in transaction extent has unfolded greater opportunities for fraudulent activities, which could significantly affect the bank's recognition and lead to substantial financial losses. The undertaking is to accurately hit upon and prevent fraud while preserving a continuing banking revel in for clients. The key aspects of this trouble are as follows:

Nature of the Problem:
- Fraudulent transactions encompass unauthorized account get right of entry to, cash laundering, identity robbery, and uncommon transaction styles. - Traditional strategies of fraud detection are regularly reactive, main to behind schedule identity of fraud. - Fraudsters continuously evolve their tactics, making it harder to discover new forms of fraud the use of conventional strategies.

Data Available:
- The dataset includes historic transaction facts, which includes transaction information consisting of: - Transaction ID, ...