Data sharing and credit scoring is a vitally important part of the way lending works for customers in vulnerable circumstances. Being turned down for a loan due to a lack of data could make the difference between being able to buy a new washing machine to wash a child’s clothes for that week of school or not. On the other hand, receiving an inappropriate loan based on incomplete data could tip a borrower over the edge into problem debt when a better solution was available.
Earlier this year, Fair4All Finance was commissioned by the Financial Inclusion Policy Forum to review the role of credit data in the affordable credit market and to assess whether new data sources could improve providers’ ability to make appropriate lending decisions for individuals in financially vulnerable circumstances.
Over the summer we held fifty consultation meetings with affordable lenders, the financial services industry, government departments, consumer support organisations and civil society, to explore the issues from a range of perspectives. We also worked closely with a sub-group of Policy Forum members and held a meeting with credit reference agencies (CRAs). We presented our findings and recommendations to the Forum in October 2019.
We are excited about the potential that these recommendations have to enable community and mainstream lenders to make better decisions for those who most need the decisions to be the right ones. Taken together, they will help to build a richer data set in a careful and considered way, and address some of the imbalance in the way that data currently works for customers in vulnerable circumstances.
1) Customer analysis and its relationship to new data sources
Adding further datasets into a complex credit scoring system is challenging for legislative and logistical reasons. Before deciding to introduce any new data source, its impact on consumers needs to be better understood. We need to know more about the financial behaviour and credit outcomes of people who are ‘hidden’ or ‘invisible’ (not known to the CRAs); those with ‘thin’ files (about whom little is known); and those with ‘bad’ files (who have a troubled financial history).
We therefore recommend that an independent study is undertaken to understand how a lack of data affects consumers’ access to credit, who is affected, how they’re impacted and which additional data sources (which could include government data) may help.
2) Incomplete loan data
Affordable lenders don’t always have access to a complete picture of a prospective borrower’s financial position. CRA loan data differs between agencies and can be incomplete because a significant number of lenders don’t feed their loan data into the CRAs. Whilst affordable lenders place value on CRA data, they do not always fully trust this data to give them a complete picture of an individual’s financial history.
This issue requires further review before solutions are developed – we recommend that the FCA considers this as part of its ongoing Credit Information Market Study. The study would also benefit from having appropriate representation and input from the affordable lending sector. In parallel with the market study, we will continue to explore with the CRAs how the usability of current data by affordable lenders could be improved.
3) Existing data initiatives
There are several ongoing initiatives that seek to improve the data available in the credit process. Rental data and Open Banking data are the most advanced and have already shown signs of success, although each has its limitations.
Open Banking is helping to provide up-to-date assessments of income and affordability and can create efficiencies for lenders, but it is currently being used inconsistently and only benefits consumers with online bank accounts.
We recommend that affordable lenders be supported to improve their knowledge of new data sources and to understand how they can improve the way that they interpret and process data. As part of this, Fair4All Finance will be helping affordable lenders to advance their operations and technology, which will include the roles that Open Banking and other emerging data sources could play in their decision-making processes.
Incorporating rental payment data into the credit scoring process has the potential to improve the credit rating of most renters (though not all). A significant amount of work has already been done to make this data set available, but it is not yet being well utilised by lenders.
To drive adoption of rental data by affordable lenders, we recommend that a place-based pilot is undertaken to demonstrate the benefits (to both consumers and lenders) of including rental data in the lending decision-making process.
4) Consumer engagement
Consumers do not always receive clear and detailed information to explain why they’ve been declined for a loan, which would enable them to make well-informed financial decisions with a clearer understanding of how their credit file will be impacted.
We recommend that consumers should be helped to understand more about the outcome of a lending decision. Where appropriate, they should be directed to supportive resources or guidance. We understand that work is already underway to address some of these issues and we will hold further discussions with relevant organisations to help them achieve their objectives.
The Financial Inclusion Policy Forum welcomed this work as the right direction of travel to improve the way that data sharing and credit scoring can work for vulnerable customers.
With the review now complete, we are currently engaging with government, individual members of the Policy Forum and other stakeholders, to agree ownership and implementation of these respective areas of work so that they can be taken forward in an appropriate and timely manner.
We will publish a further update in early 2020 to highlight the way that these recommendations are being taken forward.
If you’re interested in reading a slightly longer version of this project summary and seeing the list of organisations consulted, please review this PDF: Project summary – Data Sharing ReviewBack