Traditional lending metrics are hitting a wall. In the current landscape, relying on static data like gross income and basic credit scores is no longer enough to protect consumers or lenders. Three major regulatory shifts, spanning mortgages, Buy Now, Pay Later (BNPL), and energy debt relief - all point to the same necessity: real-time visibility. At PayPoint, we believe that understanding a customer’s true financial health requires looking past what they should be able to afford and seeing what they can.
The Visibility Gap: Why Traditional Metrics Fall Short
Consider a borrower earning £60,000 with a flawless credit history. On paper, they easily meet a 4.5x income multiple. However, traditional assessments often miss the "hidden" drains on disposable income: Active BNPL accounts across multiple providers. Energy bills that have surged in recent months. Recurring subscriptions that quietly erode monthly budgets. In many cases, a borrower's actual disposable income is half of what the official paperwork suggests.
The shift toward behavioural data is already being reflected in new regulations:
The energy sector faces a similar hurdle. UK households currently owe £4.4 billion in energy debt. While schemes like Ofgem’s Debt Relief Proposal aim to help, relying on "benefit status" as a filter is often too crude.
PayPoint’s approach utilizes Open Banking insights to create a more surgical response. By analysing real-time income and expenditure patterns, suppliers can:
Traditional affordability checks often rely on static data like payslips, credit files, and estimated costs, which can miss the full financial picture.
Complete Commitment Visibility: Open Banking reveals "hidden" outgoings like undeclared direct debits, forgotten subscriptions, multiple BNPL accounts, and payday loans.
Analysis of Spending Habits: We identify signs of financial stress, such as overdraft reliance or volatile expenditure, which self-declared estimates often hide.
Automated Income Verification: We confirm the actual regularity and amount of income hitting an account, including benefits and multiple streams for gig economy workers.
Machine Learning (ML) Categorisation: Since manual review of 150+ monthly transactions is impossible, ML identifies merchants and patterns to turn data noise into clear affordability signals.
Advanced Infrastructure: Robust assessments require secure APIs, automated risk flagging, and real-time processing.
Accelerated Compliance: Building these tools in-house can take up to 24 months. Partnering with PayPoint allows lenders to meet compressed timelines driven by Consumer Duty and upcoming BNPL regulations.
The common thread: Each scenario demonstrates how real-time data enables faster, more accurate decisions based on actual financial behaviour rather than estimates.
Powering Decisions with Open Banking
To bridge the data gap, PayPoint leverages Open Banking infrastructure to turn "transaction noise" into clear affordability signals. Our solution provides:
Moving Toward Financial Inclusion
Better data doesn't just manage risk, it expands access. By looking at actual financial management rather than a "thin" credit file, lenders can confidently support creditworthy customers who might otherwise be unfairly rejected.
Ready to modernise your affordability journey? Contact our team to see how real-time data can transform your decision-making.