Context

Our main motive was to build analysis tool:

• Whatever would categorise the similar trading on the basis of narrations

• Which can extract one bank your or the briefly form of the bank names using the IFSC codes

• Which can knock off the same number transactions with Debit and Credit effect on same date in two separate account and matching narrations.

Content

Check out my work here. Our another objective was to built a die to categorize the narrations appearing in the bank statements into adenine logical manner. These are consolidated and extracted bench account actions of various bank accounts.

Account No. - This represents to user number participate in transaction.

Date - Date about transaction

Transaction Details - Transaction narrations at bench statements

Cheque No. - This specify the cheque number

Value Date - Dates is completing of transaction

Withdrawal Amount - Indicates the amount withdrawn

Deposit Amount - Indicates one qty deposited

Balance Amount - Current balance of account

Inspiration

We want to detect fraud transactions furthermore money laundering. It the the concealment of the origins of illegally obtained money, typically by means of transfers involving foreign banks or legitimate businesses.

Update 10/12/19: Additional info about the data - https://webpages.uncc.edu/mirsad/itcs6265/group1/index.html ^^VERY HELPFUL, especially relative to birthday/age/gender customer info (strangely encoded in the raw data). My attempt to reformat/transalate/clean some of this data: https://data.world/lpetrocelli/some-translatedreformatted-czech-banking-data

Data from a real Czech bank. From 1999.

The data about the clients and their accounts consist of following relations:

-relation account (4500 objects in the file ACCOUNT.ASC) - each record describes static characteristics of an account,

-relation client (5369 objects in the file CLIENT.ASC) - each record describes characteristics of a client,

-relation disposition (5369 objects in the file DISP.ASC) - each record relates together a client with an account i.e. this relation describes the rights of clients to operate accounts,

-relation permanent order (6471 objects in the file ORDER.ASC) - each record describes characteristics of a payment order,

-relation transaction (1056320 objects in the file TRANS.ASC) - each record describes one transaction on an account,

-relation loan (682 objects in the file LOAN.ASC) - each record describes a loan granted for a given account,

-relation credit card (892 objects in the file CARD.ASC) - each record describes a credit card issued to an account,

-relation demographic data (77 objects in the file DISTRICT.ASC) - each record describes demographic characteristics of a district.

Each account has both static characteristics (e.g. date of creation, address of the branch) given in relation "account" and dynamic characteristics (e.g. payments debited or credited, balances) given in relations "permanent order" and "transaction". Relation "client" describes characteristics of persons who can manipulate with the accounts. One client can have more accounts, more clients can manipulate with single account; clients and accounts are related together in relation "disposition". Relations "loan" and "credit card" describe some services which the bank offers to its clients; more credit cards can be issued to an account, at most one loan can be granted for an account. Relation "demographic data" gives some publicly available information about the districts (e.g. the unemployment rate); additional information about the clients can be deduced from this.

Source: http://lisp.vse.cz/pkdd99/berka.htm This database was prepared by Petr Berka and Marta Sochorova. For possible questions on the data and task description contact Petr Berka.

Download Source (download within the comment from Filip Sivák · Czech Technical University in Prague): Download all files as a zip file here - files will be in asc format https://www.researchgate.net/post/Is_there_any_public_database_for_financial_transactions_or_at_least_a_synthetic_generated_data_set

Personal Note: I am thrilled that I found this - convinced this is the only real banking data out there on the web. :)

Data that I reformatted, my R project that uses this data walks through how I did this.

Data originally from : https://data.world/lpetrocelli/czech-financial-dataset-real-anonymized-transactions

Much more info available on that dataset!

The number of mobile banking app downloads in the United States increased in 2023. In the last quarter of 2023, the number of mobile banking app downloads was 34.74 million, up from 31.5 million a year earlier. The number of mobile banking app downloads was the highest in the third and fourth quarter of 2021.

As of December 2023, the six app-only digital banks included in this statistic had more than 117 million app downloads combined. The most popular of these banks in terms of downloads was the London-headquartered Revolut, which has opted for global expansion over a single market approach. As of January 2024, the banking app was particularly popular in the United Kingdom (UK), France, Romania and Poland.

Number of active customers

Due to multiple downloads and inactive customers, looking at the number of active customers as announced by banks could be deemed a more accurate depiction of customer growth. In February 2018, Revolut announced that they had hit 1.5 million customers, a figure that had grown to 18 million by 2022 and to 35 million by October 2023. The incredible acceleration of new customers joining these online-only banks has been mirrored by the Berlin-based N26 and fellow London-headquartered Monzo.

The FDIC is often appointed as receiver for failed banks. This list includes banks which have failed since October 1, 2000.

Context

The FinCEN files contain money transactions between banks across the world. Also, they have revealed how some of the world's biggest banks have allowed criminals (drug cartels, corrupt regimes, arms trafficking, and more) to move dirty money around the world. Some major banks are HSBC, JP Morgan, Deutsche Bank, Standard Chartered and etc

Reference

• https://www.icij.org/investigations/fincen-files/download-fincen-files-transaction-data/
• https://www.bbc.com/news/uk-54226107
• https://www.icij.org/investigations/fincen-files/dirty-money-has-metastasized-within-global-banking-system-top-regulator-says/

Content

The data that I shared here seems a subset of the actual dataset which was used by ICIJ (International Consortium of Investigative Journalists) for analysis.

transaction_map.csv

• Id = transaction identification number generated by ICIJ
• icij_sar_id = SAR ID number generated by ICIJ that shows all the transactions that were extracted from the same report.
• filer_org_name_id = financial institution that filed the report with FinCEN
• filer_org_name = financial institution that filed the report with FinCEN
• begin_date = date the first transaction in the reported transaction by the filer (set of transactions with the same originator and beneficiary) took place
• end_date = date the last transaction in the reported transaction by the filer (set of transactions with same originator and beneficiary) took place
• origsame originator and beneficiary) took place
• originator_bank_id = bank where the transaction (s) was originated
• originator_bank = bank where the transaction (s) was originated
• originator_bank_country = location country of the originator bank
• originator_iso = originator bank ISO code of the bank location country
• beneficiary_bank_id = bank where the transaction (s) was received
• beneficiary_bank = bank where the transaction (s) was received
• beneficiary_bank_country = location country of the beneficiary bank
• beneficiary_iso = beneficiary bank ISO code of the bank location country
• number_transactions = number of transactions
• amount_transactions = total amount of the transactions #### Data explore

ICIJ provided a visualization tool to explore this data. Here, we can see country wise in & out transactions between the financial institutions(Ex: Banks) and also suspicions filing details of each transaction. https://www.icij.org/investigations/fincen-files/explore-the-fincen-files-data/

Inspiration

We can use this data to understand - Money Laundering - Knowledge Graph construction and visualization

The number of BBVA Mexico app downloads were higher than any other banking app downloads in Mexico not only in the last quarter of 2022, but during the whole observed period. Banco Azteca followed both in terms of quarterly and overall downloads. Santander's SuperMóvil, Banorte Mobile, and BanCoppel were also among the five most widely downloaded banking apps.

Founded in 2013 in Berlin Germany, N26 officially operates across 22 countries worldwide. N26, which currently has over 7 million customers worldwide has been a particular favorite for investors. Between January 2015 and February 2023, despite the bank originating in Germany, French customers had made the most downloads of the app.

This statistic presents the percentage of French having downloaded the banking app(s) of their bank(s) on their smartphone in 2020. It appears that more than half of the respondents had downloaded one banking app, while 15 percent had several because they had several bank accounts.

Graph and download economic data for Commercial Bank Interest Rate on Credit Card Plans, Accounts Assessed Interest (TERMCBCCINTNS) from Nov 1994 to Feb 2024 about consumer credit, credit cards, loans, consumer, interest rate, banks, interest, depository institutions, rate, and USA.

In December 2023, the number of Revolut bank app downloads worldwide was close to 2.5 million, the highest monthly download in the observed period. Between March 2015 and December 2023, the monthly downloads of Revolut increased drastically, with a particularly sharp increase in the second part of 2019, as well as in 2021 and the first part of 2022.

The monthly downloads of the N26 bank app worldwide decreased notably in 2022, compared to the previous years. Between 2015 and January 2023, the highest number of downloads took place in 2019 and 2021, with a slower period in 2020. In 2022, the average number of monthly downloads was roughly 158 thousand, compared to 220 thousand monthly downloads in 2021.

Artificial Neural Network Model using Keras and Tensorflow with 85% Acuuracy

The monthly downloads of Monese bank app rocketed between August 2019 and March 2020. In February 2020, almost 210,000 people downloaded the app, making it the most successful month for the neobank. After March 2020, however, the number of monthly downloads decreased significantly. 2022 brought another increasing trend for Monese, but the monthly downloads remained well below the numbers measured in 2019 and 2020.

Context

In 2006, Hans Rosling gave a TED talk titled The best stats you've ever seen. In the beginning of the talk, he shows an animation he made to debunk some misconceptions about today's world.
I really enjoyed seeing this visualization and have been thinking to try to reproduce it with the tools I know (i.e python and matplotlib).

Content

The data was downloaded from data.worldbank.org on June 28th, 2018.

• life expentancy at birth: number of years a newborn would live if the patterns of mortality at the time of birth remain the same throughout his life.
• Fertility rate: number of children a woman would give birth to during her childbearing years.
• Country population: total number of residents regardless of legal status or citizenship (midyear estimates)

Photo by Ishan @seefromthesky on Unsplash

http://i.imgur.com/KbKhYxJ.jpg" alt="">

This data set was compiled to support the blog post SQL Intermediate: PostgreSQL, Subqueries and more!.

The original source of the data is the Consumer Financial Protection Bureau. For further information on the data, you can consult the CFPB Field Reference.

This data set is in the Public Domain in the United States: Full Licensing Info.

The number of Nubank downloads in Brazil were higher than any other banking app downloads in every quarter of 2022. In spite of this, CAIXA remained the banking app with the highest overall downloads between 2015 and 2022. FGTS, an app that links bank accounts to the employment contract in order to protect employees from being dismissed without just cause, was only launched in March 2016, but it ranked three in terms of overall downloads in the observed period.

Graph and download economic data for Total Assets, All Commercial Banks (TLAACBW027SBOG) from 1973-01-03 to 2024-04-17 about assets, banks, depository institutions, and USA.