We are experts in data enrichment - going beyond aggregation - to provide our partners with highly accurate insights.
Collecting Open Banking data
Upon the applicant’s consent, an individual or company’s account(s) are aggregated into a single view of their financial behaviour. Advanced models detect and indicate when an applicant hasn’t shared their main income or expenditure account.
Classification, Grouping, Prediction
For market-leading coverage and accuracy, we have developed state of the art modelling techniques using millions of real transactions to categorise income and expenditure. Our advanced machine-learning models use historical data to predict future behaviour, taking into account outliers, seasonality and changes in circumstance.
Financial behaviour insights
Proven predictive insights are surfaced through our API or underwriting platform, Atlas. These insights can be incorporated into scorecards and policies, used to improve manual underwriting, or automate decisioning.
Embed our Open Banking journey into your existing customer journey, or bring-your-own-data.
Credit Kudos’ Open Banking journey has been built based on extensive consumer, UX and UI research to optimise for conversion. Our journey seamlessly embeds Open Banking into any customer journey, making it easy to access verified transaction data directly from financial institutions.
The applicant connects their bank accounts via our Open Banking journey
Credit Kudos instantly aggregates and analyses verified transaction data
View insights through our Reports API or underwriting platform, Atlas.
Bring your own data enables you to enrich any existing transaction data with Credit Kudos’ insights. This could be data from another aggregation service, data held by you as a bank or similar financial institution, or data collected by a third party.
As a credit reference agency, our predictive insights are built by combining transaction and loan outcome data. We distill transaction data into proven predictive insights that can be incorporated into scorecards and policies.
Advanced machine-learning models use historical data to predict future behaviour, taking into account outliers, seasonality and changes in circumstance such as a pay rise or moving house. Measured on real historical data, we have built models that are 37% more accurate than any traditional statistical approach.
Our classification engine uses state of the art modelling techniques and millions of real transactions to categorise income and expenditure with market-leading coverage and accuracy.
Access rich insights via our Reports API or our underwriting platform, Atlas.
Our easy-to-use underwriting platform displays rich insights on top of Open Banking data to streamline your underwriting process and help you to better understand your customers’ creditworthiness.
Our Reports API allows you to fully or partially automate decisioning, use Credit Kudos data in existing risk models or integrate financial behaviour data into your underwriting platform.
Best Credit Information Partner
Credit Strategy Lending Awards 2020
Best Technology Provider
Credit Strategy Credit Awards 2020
Credit Information Partner of the Year
Consumer Credit Awards 2020
Innovation of the Year
Consumer Credit Awards 2020
Best Machine Learning in Credit & Collections
Credit Connect Credit & Collections Technology Awards 2020
London's Top 50 Startups
Best Risk and Fraud Solution
Credit Excellence Awards CCR 2020
The Power List 20
Credit Connect 2020
Affordable Credit Challenge
Nesta Challenges 2020
The Fintech50 2020
Top 100 FinTech Disruptors for 2020
Fintech Startups to Watch