The Credit Card Statistics provide data in relation to monthly credit card transactions. A breakdown of the number of credit cards issued to Irish residents is also provided.

A Credit Card Dataset for Machine Learning!

Don't ask me where this data come from, the answer is I don't know!

Context

Credit score cards are a common risk control method in the financial industry. It uses personal information and data submitted by credit card applicants to predict the probability of future defaults and credit card borrowings. The bank is able to decide whether to issue a credit card to the applicant. Credit scores can objectively quantify the magnitude of risk.

Generally speaking, credit score cards are based on historical data. Once encountering large economic fluctuations. Past models may lose their original predictive power. Logistic model is a common method for credit scoring. Because Logistic is suitable for binary classification tasks and can calculate the coefficients of each feature. In order to facilitate understanding and operation, the score card will multiply the logistic regression coefficient by a certain value (such as 100) and round it.

At present, with the development of machine learning algorithms. More predictive methods such as Boosting, Random Forest, and Support Vector Machines have been introduced into credit card scoring. However, these methods often do not have good transparency. It may be difficult to provide customers and regulators with a reason for rejection or acceptance.

Task

Build a machine learning model to predict if an applicant is 'good' or 'bad' client, different from other tasks, the definition of 'good' or 'bad' is not given. You should use some techique, such as vintage analysis to construct you label. Also, unbalance data problem is a big problem in this task.

Content & Explanation

There're two tables could be merged by ID:

The credit card penetration in Thailand was forecast to continuously increase between 2024 and 2029 by in total 36.8 percentage points. After the fifteenth consecutive increasing year, the credit card penetration is estimated to reach 67.53 percent and therefore a new peak in 2029. Notably, the credit card penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like Malaysia and Philippines.

The credit card penetration in Brazil was forecast to continuously increase between 2024 and 2029 by in total 16.6 percentage points. After the twelfth consecutive increasing year, the credit card penetration is estimated to reach 62.27 percent and therefore a new peak in 2029. The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The “Customer Credit Card Data” dataset provides valuable insights into credit card usage patterns and financial behavior. Each record represents an individual credit card holder, and the dataset includes the following features:

Id | Features | Description --|:---------|:----------- 1|**Cust_Id:** | Identification of credit card holder
2|**Balance:** | A credit card balance or Total amount left in their account to make purchases 3|**Balance_Frequency:** | How frequently the balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated) 4|**Purchases:** | Total amount of purchases made from account 5|**One_Off_Purchases:** | Maximum purchase amount done in one-go 6|**Installments_Purchases:** | Amount of purchase done in installment 7|**Cash_Advance:** | Cash in advance given by the user 8|**Purchases_Frequency:** | How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased)
9|**One_Off_Purchases_Frequency:** | How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased)
10|**Purchases_Installments_Frequency:** | How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done)
11|**Cash_Advance_Frequency:** | How frequently the cash in advance being paid
12|**Cash_Advance_Trx:** | Number of Transactions made with "Cash in Advanced"
13|**Purchases_Trx:** | Number of purchase transactions made
14|**Credit_Limit:** | Limit of Credit Card for user
15|**Payments:** | Total amount of payments done by user
16|**Minimum_Payments:** | Minimum amount of payments made by user 17|**Prc_Full_Payment:** | Percentage of full payment paid by user
18|**Tenure:** | Tenure of credit card service for user

This dataset is valuable for analyzing credit card behavior, identifying trends, and building predictive models related to credit usage. Researchers, analysts, and financial institutions can leverage this data to gain deeper insights into customer profiles and optimize credit card services.

The Credit Card Transactions Dataset provides detailed records of credit card transactions, including information about transaction times, amounts, and associated personal and merchant details. This dataset has over 1.85M rows.

How This Dataset Can Be Used:

Fraud Detection : Use machine learning models to identify fraudulent transactions by examining patterns in transaction amounts, locations, and user profiles. Enhancing fraud detection systems becomes feasible by analyzing behavioral patterns.

Customer Segmentation : Segment customers based on spending patterns, location, and demographics. Tailor marketing strategies and personalized offers to these different customer segments for better engagement.

Transaction Classification : Classify transactions into categories such as grocery or entertainment to understand spending behaviors. This helps in improving recommendation systems by identifying transaction categories and preferences.

Geospatial Analysis : Analyze transaction data geographically to map spending patterns and detect regional trends or anomalies based on latitude and longitude.

Predictive Modeling : Build models to forecast future spending behavior using historical transaction data. Predict potential fraudulent activities and financial trends.

Behavioral Analysis : Examine how factors like transaction amount, merchant type, and time influence spending behavior. Study the relationships between user demographics and transaction patterns.

Anomaly Detection : Identify unusual transaction patterns that deviate from normal behavior to detect potential fraud early. Employ anomaly detection techniques to spot outliers and suspicious activities.

Daily Card Payments by Irish Households. A subset of the monthly Card Payment Statistics. The onset of the Covid-19 pandemic created the need for timely, high-frequency data, such as the Daily Credit and Debit Card Statistics, to better understand the impact of the pandemic on personal expenditure and economic activity. This high-frequency daily Credit and Debit Data captures expenditure of euro-denominated credit and debit cards issued to Irish residents. The dataset consists of total daily debit and credit card spending and ATM withdrawals, while from 1 October 2020, expenditure in a number of key sectors of the economy, and a split of online and in-store spending is also available.

The number of credit cards in use in India was forecast to continuously increase between 2024 and 2029 by in total *** million cards (+**** percent). After the fifteenth consecutive increasing year, the number is estimated to reach ***** million cards and therefore a new peak in 2029. Notably, the number of credit cards in use of was continuously increasing over the past years.Shown is the estimated number of credit cards currently in use.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Co-Branded Credit Card Defaults

Analyzing Application and Credit History Data

By Amit Kishore [source]

About this dataset

This dataset provides insights into the predictability of co-branded credit card default in a retail network of a company. With over [x] columns of data, this dataset contains information ranging from applicants' demographics and credit scores to their limits and payment history. This comprehensive dataset was constructed with the goal of understanding how demographic factors influence credit risk and ultimately, co-branded credit card default rates. From age to income, marital status to educational background, each variable is used to create an understanding of the risks associated with applicants taking out co-branded cards in the retail network. Additionally, get an inside look at current trends in loan application behavior — see how often customers use loan or have applied for new cards over set time intervals — as well as monthly payments and query history. Use this unique dataset to develop an improved model for predicting credit card default that could help financial institutions assess potential cusotmers more accuracyly!

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How to use the dataset

This dataset aims to help predict co-branded credit card defaults in retail networks by providing a variety of information about the applicants. The dataset includes information such as age, gender, marital status, employment status, education level, monthly income and expenses, credit history length, number of loans and credit cards owned by the applicant, number of times they applied for loan/credit card inquiries and how many times they used each loan/credit card in the last months.
- In order to use this dataset effectively to predict co-branded credit card default rates in a retail network it is important to understand the data and how it's related each other. It is also important to consider any external factors that can influence an individual's likelihood of defaulting on a loan.
- The first step is to look at the descriptive statistics for each column so that we can get some idea as to what kind of values are seen most often among our data points and if there are any outliers present. This will give us an idea about which features may be most relevant when predicting defaults or if our model may need more contextual information from outside sources like socio-economic or political factors.
- Once we have identified any relevant features from our descriptive statistics analysis we'll then want to start exploring different ways these variables are related with one another and what kind of relationship these variables have with regards to defaults (both positively correlated/directly increase default risk plus negatively correlated/directly decrease default risk). This can be done through simple pair plots which show distribution and correlations between two given columns or triangular heatmaps which allow us explore correlations among multiple columns at once. Building upon these relationships further allows us then determine possible causes behind the observed correlations between different variable groups – allowing us get even more insight into why certain individuals are more likely than others be defaulters on their co-branded cards (whether it because they simply had bad luck or because there were larger systematic factors playing out).
- Having identified all relevant features from this data exploration process along with any external “background” data points - we finally move into constructing our machine learning models using appropriate algorithms suitable for predicting probability outcomes such as SVM or XGBoost tree ensembles etc.. When building out your ML model you’ll want ensure that all parameters necessary for accurate predictions have been included before deploying them on production systems so as not compromise neither customer privacy nor product quality standards set by regulatory authorities governing such models across countries globally

Research Ideas

• Using the given dataset to create a predictive model that can be used to identify customers at risk of defaulting on their co-branded credit cards. This could help determine which customers should be offered special incentives or strategies in order to reduce their risk of defaulting.
• Using the given dataset to create a financial health recommendation engine that analyzes customer’s existing credit cards and recommends other ways they can improve their financial situation (e.g., balance transfers, better rewards programs, etc.).
• Extracting insights from the data by...

The credit card penetration in Canada was forecast to continuously increase between 2024 and 2029 by in total 1.4 percentage points. After the seventh consecutive increasing year, the credit card penetration is estimated to reach 84.55 percent and therefore a new peak in 2029. The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like United States and Mexico.

Korea Number of Credit Cards Issued data was reported at 103,541.000 Unit th in Aug 2018. This records an increase from the previous number of 102,874.000 Unit th for Jul 2018. Korea Number of Credit Cards Issued data is updated monthly, averaging 94,345.000 Unit th from Jan 2003 (Median) to Aug 2018, with 188 observations. The data reached an all-time high of 122,543.000 Unit th in Aug 2011 and a record low of 0.000 Unit th in Nov 2003. Korea Number of Credit Cards Issued data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s Korea – Table KR.KA012: Credit Card Statistics: The Bank of Korea.

Card Payments by Irish Households. The Credit and Debit Card Statistics provide data in relation to credit and debit card transactions, including a sectoral breakdown of expenditure, E-commerce, spending outside Ireland, and data pertaining to the role of debit cards. A breakdown of the number of credit/debit cards currently issued to Irish residents is also provided. .hidden { display: none }

The monthly card payment statistics provide data in relation to credit and debit card transactions undertaken by Irish resident households. The data includes the monthly value and volume of transactions across both credit and debit cards by Irish households. The data is collected from issuers of credit and debit cards and specifically from reporting agents that are resident in Ireland (including established foreign branches). The aggregate data is further broken down into, remote and non-remote card spending; contactless and mobile wallet card spending; sectoral card spending; domestic and non-domestic card spending; regional card spending in Ireland; and cash withdrawals. A breakdown of the number of credit & debit cards currently issued to Irish residents is also provided. Note, only Personal Cards are in scope for this reporting, business cards and cards issued to non-Irish residents are not included. Additionally, data files uploaded here follow the SDMX –ML format where Series Key are the primary identifier for a reporting period (Date for which the data is reported is represented in the Reporting Period field). For example : PCI.M.IE.W2.PCS_ALL.11.PN is the series key and each element/dimension between the delimiter “.” is expanded with a description in subsequent columns ending with the subscript “DESC” to understand the meaning of each element/dimension. The Observation_free column represents the value (€ EUR) or Volume (PN) of transactions depending on the last element/dimension, EUR or PN. For further information on the Payment Statistics Monthly, the reporting instructions in the Landing page link has additional details about the table and the column names used in this data collection.

The number of credit cards in use in Australia was forecast to continuously increase between 2024 and 2029 by in total *** million cards (+**** percent). After the twelfth consecutive increasing year, the number is estimated to reach ***** million cards and therefore a new peak in 2029. Shown is the estimated number of credit cards currently in use.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Description of the Credit Card Eligibility Data: Determining Factors

The Credit Card Eligibility Dataset: Determining Factors is a comprehensive collection of variables aimed at understanding the factors that influence an individual's eligibility for a credit card. This dataset encompasses a wide range of demographic, financial, and personal attributes that are commonly considered by financial institutions when assessing an individual's suitability for credit.

Each row in the dataset represents a unique individual, identified by a unique ID, with associated attributes ranging from basic demographic information such as gender and age, to financial indicators like total income and employment status. Additionally, the dataset includes variables related to familial status, housing, education, and occupation, providing a holistic view of the individual's background and circumstances.

Researchers, analysts, and financial institutions can leverage this dataset to gain insights into the key factors influencing credit card eligibility and to develop predictive models that assist in automating the credit assessment process. By understanding the relationship between various attributes and credit card eligibility, stakeholders can make more informed decisions, improve risk assessment strategies, and enhance customer targeting and segmentation efforts.

This dataset is valuable for a wide range of applications within the financial industry, including credit risk management, customer relationship management, and marketing analytics. Furthermore, it provides a valuable resource for academic research and educational purposes, enabling students and researchers to explore the intricate dynamics of credit card eligibility determination.

Daily, weekly and monthly data showing seasonally adjusted and non-seasonally adjusted UK spending using debit and credit cards. These are official statistics in development. Source: CHAPS, Bank of England.

This Dataset contains year, month, bank-type and bank-wise total value and volume of card payments and cash withdrawal transactions of credit and debit Cards at point of sale (PoS), ATMs and online during each month

This case requires to develop a customer segmentation to define marketing strategy. The sample Dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.

Following is the Data Dictionary for Credit Card dataset :-

CUST_ID : Identification of Credit Card holder (Categorical) BALANCE : Balance amount left in their account to make purchases ( BALANCE_FREQUENCY : How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated) PURCHASES : Amount of purchases made from account ONEOFF_PURCHASES : Maximum purchase amount done in one-go INSTALLMENTS_PURCHASES : Amount of purchase done in installment CASH_ADVANCE : Cash in advance given by the user PURCHASES_FREQUENCY : How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased) ONEOFFPURCHASESFREQUENCY : How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased) PURCHASESINSTALLMENTSFREQUENCY : How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done) CASHADVANCEFREQUENCY : How frequently the cash in advance being paid CASHADVANCETRX : Number of Transactions made with "Cash in Advanced" PURCHASES_TRX : Numbe of purchase transactions made CREDIT_LIMIT : Limit of Credit Card for user PAYMENTS : Amount of Payment done by user MINIMUM_PAYMENTS : Minimum amount of payments made by user PRCFULLPAYMENT : Percent of full payment paid by user TENURE : Tenure of credit card service for user

The number of credit cards in use in the United Kingdom was forecast to continuously decrease between 2024 and 2029 by in total 0.02 million cards (-0.03 percent). The number is estimated to amount to 63.62 million cards in 2029. Shown is the estimated number of credit cards currently in use.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Mexico Credit Cards: Issued Cards: Others data was reported at 1,528,047.000 Number in Dec 2018. This records a decrease from the previous number of 1,575,440.000 Number for Sep 2018. Mexico Credit Cards: Issued Cards: Others data is updated quarterly, averaging 1,570,739.000 Number from Mar 2006 (Median) to Dec 2018, with 52 observations. The data reached an all-time high of 6,361,111.000 Number in Mar 2008 and a record low of 1,311,005.000 Number in Jun 2011. Mexico Credit Cards: Issued Cards: Others data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA008: Number of Credit and Debit Cards.