Despite significant contributions of £30 billion annually to the UK economy, independent workers face challenges accessing core financial services and products like mortgages and loans.  

In fact, the issue isn’t a lack of funds, but the credit scoring system’s inability to understand new work patterns. Financial institutions favour applicants with a single income source, excluding many gig workers. Therefore, treating gig workers as a ‘red flag’ is outdated and fails to serve modern customers.  

The Existing Bias in Credit Scoring 

The Hidden Cost of Gig Worker Living report, funded by Rollee, found that 7 in 10 UK gig workers have been denied access to basic financial products such as a loan, despite having a good credit score.  

Regrettably, financial institutions use outdated criteria that doesn’t consider emerging work patterns. Relying solely on credit scoring to determine financial eligibility no longer aligns with today's workforce. As a result, financial institutions must find a solution that allows them to engage with this expanding market without adversely affecting their acceptance rates. 

Closing the Divide 

To enhance inclusion in finance, it is crucial to embrace comprehensive income and employment data for forward-thinking, equitable solutions. Consider an example of a Senior Software Engineer switching from full-time employee to freelancer on a platform like Malt. This independent worker/ freelancer works only during the first and the last quarter of a year with a daily rate of £800, generating a yearly revenue of £96,000. Despite this being a comfortable income in Europe, traditional scoring systems may view it negatively due to the absence of income during the summer.  Financial institutions often make loan decisions based on income regularity, without considering the dynamics behind their activity.  

To create fair and inclusive scoring rules, it's crucial to identify suitable scoring features for each worker category that effectively capture their professional behaviours. This approach guarantees that the scoring system accurately represents the unique attributes of various types of workers. 

In developing fair credit scoring rules for various categories of self-employed or freelance workers, it is crucial to consider alternative data points such as skill set, project duration, customer quality, and worker demand. Applying the same rules across different categories, such as an Uber driver, an Etsy sole trader, solely based on their shared working status would be inappropriate.  

Paving the Way for Fairness 

Financial institutions must adopt a digitised, automated approach for real-time visibility and transparency across diverse datasets. Incorporating alternative data points representing self-employed workers’ financial stability enables more equitable scoring models, streamlining processes, saving time and costs while expediting decision-making.  

A shift in mindset 

Financial institutions must adapt scoring criteria to cater to the expanding market of independent workers, ensuring a fair assessment of their finances. Obtaining comprehensive information enables institutions to effectively meet the diverse needs of modern workers. 

These solutions empower businesses and enhance financial accessibility, fostering a more inclusive and supportive environment.