Individual Companies Transactional Data and Insights via Dashboard

This is our aggregated version of our Exact.One dataset, cut per individual companies or merchants and accessible via our Web-based Dashboard. You can have a pack of individual tickers and access to the associated transactions weekly aggregated. You can also see some KPIs like Share of Wallet, Average Basquet Size etc. 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 550K users contributing to over £6 billion in spend. Over 1.5 million users overall. • 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 500k users are actively sharing their Open Banking data and this is growing monthly as we acquire more users. Over 800k actively connected accounts. How do I receive the data? • Via Web Based Dashboard 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Exact.One的聚合版本，按公司或商户划分，通过Web仪表板提供访问，包含每周汇总的交易数据和关键绩效指标。它基于开放银行连接获取消费者交易数据，拥有超过50万活跃用户和250亿笔年交易量，覆盖330多家上市公司，历史数据超过5年，并采用基于规则的分类模型进行精准分类。