Best Bank Transaction Datasets, Databases & APIs
AML Data on traditional and crypto transactions
Multimedia List - Transactional Retail & Bank Card Holder Data USA (24 Million records)
UNBANKS Real-Time EU Consumer Transaction Data (de-identified, pay as you go option available)
Bank Statements from different countries
U.S. Banks Ranked by Non-Current Loan Volume
Fraud Detection: Brand Safety Monitoring
Monitoring changes in customers and suppliers
The Ultimate Guide to Bank Transaction Data 2022
What is Bank Transaction Data?
Bank transaction data is data that shows cash in and cash out of an account, normally referred to as account statements. It can include logistical, financial, and work-related insights for individual account-holders, as well as corporate and company accounts.
How is Bank Transaction Data collected?
This data in the modern-day running of banks where the focus has shifted away from paperwork, electronic transactions can be found on bank databases, and can also be generated from customers through interactions with bank sales agents and service representatives and transactions. In modern banking, where banks are striving every day to offer personalized services to every customer, banks have made it a key strategy to send customers bank statements that highlight bank transaction details.
What are the typical attributes of Bank Transaction Data?
This data includes such information as the number of credit card withdrawals per day, the value of credit card withdrawals per day, cash credits value and number of online transactions. This gives the user access to the specific information they need.
How is Bank Transaction Data commonly used?
Bank transactional data can be leveraged by banks in many ways. In well-structured systems, banks can use this data to group their clients and customers in a way that they handle and sell their policy products to them. The success of any bank is pegged on their ability to understand their customers’ needs and they can only do this with good data in place. Having an aggregated customer database enables a bank to leverage big data analytics for better marketing strategies.
How can a user assess the quality of Bank Transaction Data?
To remain competitive in a red ocean competitive business environment, conventional banks should make intelligent and intuitive decisions on how best to serve their customers. As such, the backbone of intelligent decision making is the quality of data collected about every client. The quality of bank transactional data is gauged by how effectively is can be utilized by its user. Bank transaction data should allow a user to make smarter decision and generate high ROI. This can only be achieved when the data is consistent and complete. ‘Consistency’ means that the data has been collected over a suitable span of time and is always up to date. Bank transactional data accuracy is also a key indicator of good quality, as only with accurate statistics can a user make effective, data-driven decisions.
Where can I buy Bank Transaction Data?
Data providers and vendors listed on Datarade sell Bank Transaction Data products and samples. Popular Bank Transaction Data products and datasets available on our platform are AML Data on traditional and crypto transactions by HubioID, Multimedia List - Transactional Retail & Bank Card Holder Data USA (24 Million records) by Multimedia Lists, and UNBANKS Real-Time EU Consumer Transaction Data (de-identified, pay as you go option available) by UNBANKS.
How can I get Bank Transaction Data?
You can get Bank Transaction Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Bank Transaction Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Bank Transaction Data APIs, feeds and streams to download the most up-to-date intelligence.
What are similar data types to Bank Transaction Data?
Bank Transaction Data is similar to Credit Card Transaction Data, Point-of-Sale (POS) Data, Loyalty Card Data, Consumer Transaction Data, and Debit Card Transaction Data. These data categories are commonly used for Purchase Intelligence and spending analytics.