ClearScore Dataset | UK Aggregated Consumer Transaction Data | 1.4m users.

This is our aggregated version of our Exact.One dataset. You can aggregate the transactions using different variables according to your needs: • Per merchant/ticker. • Per category/industry. • You can choose if you would like to see daily/weekly/monthly/quarterly aggregated data. What makes ClearScore's Exact.One dataset unique? • ClearScore provides consumer debit and credit card data at a transaction level. • The data is made available directly from open banking connections that users have with the ClearScore App. • A large active panel of 1.4m users ever connected and 1.8m accounts • Historic view of data spanning 5+ years. • Native categorisation methodology curated over the last 10 years. • Coverage of over 250 million transactions annually mapped to 330+ publicly listed companies. What is the panel size? • More than 1.4m users who have connected to share their data via Open Banking. 1.8m total accounts ever connected in the panel. How do I receive the data? • S3 bucket transfer (Preferred) • SFTP • Snowflake What’s the structure of the data? • Raw transactions (row-level data) or Aggregated data *Data dictionary available upon request. What is the quality of the panel? • The users who we have acquired have a re-auth rate of ~65%. • The coverage is 250 million transactions accounting for £6 billion in spend (Jan - Dec 2021 example) • Coverage of over 1400 merchants mapping to 330 tickers, 130 of which are UK publicly listed Exact.One is built on an industry-leading transaction categorisation service: • Our categorisation service is a rules based deterministic model which favours accuracy over coverage • We focus on accurately categorising spend at merchants, as well as spend pertaining to credit risk (e.g. income, gambling, benefits, financial institutions, cash withdrawals, and debt management services) • Clients can request improvements to the model, and these can easily be implemented by adding new rules or adapting existing rules Purpose tagging: We classify transactions utilising 286 purpose tags which are rolled up to higher level tags (e.g., childcare benefits > benefits > income). Merchant tagging: We tag 1.4k merchants in our model with updates applied each month. Version control: Strong version control allows us to improve our categorisation each month, whilst not breaking models. Unrivalled foundation: Engine trained on the richest data bank in the UK, with >1bn transactions from 60+ financial institutions

搜集汇总

背景与挑战

背景概述

该数据集是英国聚合消费交易数据，覆盖140万用户，通过开放银行直接获取交易级信息，包含5年以上历史记录和超过250亿笔年交易。数据支持按商户、类别等变量聚合，并采用基于规则的分类方法，映射到330多家上市公司，适用于金融分析和风险建模。