Inclusive credit underwriting: Banks and lenders turn to data integration

In the ever-evolving landscape of the modern workforce, traditional credit assessment models are struggling to keep pace with the changing dynamics of employment to accurately assess all risk signals. Financial institutions traditionally favour applicants with a single income source and a stable work history at the same company for an extended period. Such records are seen as indicators of stability and enable easy assessment of a worker's income and loan repayment capabilities. Workers deviating from these traditional paths are automatically perceived as higher risk borrowers.

Consequently, many independent workers face unequal access to crucial financial services, including mortgages and loans. Shockingly, a report titled "The Hidden Cost of Gig Worker Living", commissioned by Rollee, reveals that despite having good credit scores, 7 out of 10 UK gig workers have been denied basic financial products like loans. Recognising the need to incorporate alternative income and employment data, banks and lenders are now turning to data integration to enable inclusive credit underwriting.

Manual risk assessments
Manual risk assessments have long been the norm in the banking industry. However, in today's digital age, relying solely on manual approaches to assess credit risks is increasingly outdated. Financial institutions often find themselves confronted with the challenge of gathering and incorporating relevant data from disparate sources. With salary records scattered across multiple platforms, the manual gathering of data becomes time-consuming and inefficient. As a result, gig workers are denied access to financial services, and businesses suffer losses due to the limited availability of credit.

In-house data integrations
Financial institutions are now acknowledging the need to incorporate alternative income and employment data by integrating with freelance platforms and HR software through public APIs. However, developing these integrations in-house often encounters obstacles and bottlenecks. Negotiating with platforms to obtain access to private APIs can lead to refusals. Furthermore, integrating with numerous platforms becomes challenging, both in terms of scalability and the investment required from backend, data, and DevOps teams to maintain all integrations flawlessly. Whilst these efforts aim to foster data-driven decision-making, the reality is that the complexity of technology can hinder this and intended business growth.

Striving for scalability
To establish fair scoring models that cater to all types of workers, financial institutions must gain swift and seamless access to alternative data points that accurately reflect the financial stability of self-employed individuals from various working categories. Leveraging an external API infrastructure that facilitates automated connections to numerous income and employment platforms is essential for achieving scalable data connectivity across markets and regions. Those who originally built in-house integrations in local markets are only blocked from expanding faster as they figure out fragmented payroll systems in different markets. Automating data consolidation and standardisation reduces the need for time-consuming manual processes and minimises the complexity of internal technology team efforts across borders.

This approach also empowers independent workers to retain ownership of their data. It allows them to grant permission for sharing financial data without fully relinquishing control over the data itself. It also avoids them having to go through painful processes collecting multiple documents such as payslips and invoices. Additionally, implementing a central monitoring system to analyse data ensures enhanced transparency and reduces the risk of fraudulent activities or data tampering.

Advancing inclusive credit underwriting
Financial institutions acknowledge the necessity of adapting scoring rules to accommodate the diverse workforce of today's market. By finding fast and scalable ways to access workers' detailed professional data, including income and activity, these institutions can provide inclusive services to all types of workers. This enables them to confidently engage with a growing market that represents both present and future workers.