How Playter makes instant credit decisions to scale sustainably with Wiserfunding and Codat

Playter is a B2B Buy Now, Pay Later (BNPL) solution that empowers small and medium enterprises (SMEs) to take control of their cashflow through fast, flexible funding to spread their invoice across 6 or 12 monthly installments. Playter’s vision is to accelerate its customers’ growth by bringing the speed and flexibility of BNPL to startups and small businesses. As a high-growth startup, Playter grew turnover 20x and raised over £55m in the last 9 months. 

The challenge 

Playter needed a credit analytics solution that could scale with them, accurate underwriting is at the core of their business model. To provide fast, reliable credit decisions to applicants, Playter needed to develop a SME-focused solution that could screen, analyse and process applicants’ creditworthiness instantly. Alternative corporate credit screening tools did not provide enough data or granularity and were too costly and time-intensive to scale. 

Critically, Playter takes a different approach to BNPL than the majority of incumbents. Most BNPL providers underwrite individual transactions for customers at the point of purchase.  This approach often underestimates risk and can easily allow borrowers to become overleveraged. Rather than looking at transactions at the point of purchase, Playter evaluates the SME’s creditworthiness as a single entity. This allows them to better understand liabilities against income and cash flow. However, to do so, Playter required a proven credit risk analytics solution that analysed financial and risk across all fundamental business metrics. 

Objectives 

Playter needed a solution that: 

• Instantly screens SMEs for creditworthiness based on a simple online application 
• Provides a holistic, single customer view of credit factors to power underwriting decisions and internal operations 
• Continuously monitor credit changes to update loss provisions, manage distressed debt, and calibrate underwriting policies

How it works 

Playter implemented Wiserfunding and Codat within their underwriting solution to empower the entire credit lifecycle analysis of its customers. 

Screening the SME 

When customers apply to Playter, Wiserfunding automatically calculates the applicant’s SME Z-Score using one of Wiserfunding’s 62 models.  Playter has calibrated distinct score thresholds by industry, and applicants who meet the risk criteria are automatically passed to the next underwriting stage. The screening process is completely automated, delivering qualified leads to the next stage in seconds, saving underwriters the time and effort of wading through thousands of unviable applications. 

Underwriting the loan 

Once screened candidates are passed through, Playter’s underwriters conduct a detailed assessment incorporating Wiserfunding’s metrics, including the applicant’s SME Z-score, probability of default (PD), bond rating equivalent (BRE), Reliability Index, Risk Outlook, financial ratios, and non-financial data. Once the underwriters make a credit decision, approved customers can immediately accept an offer and receive the funds.

Portfolio monitoring and intelligence 

Playter’s Risk team runs a full suite of automated portfolio analytics through Wiserfunding’s platform on a regular basis, updating critical metrics like the borrower’s SME Z-score, PD, LGD, and BRE. Using the updated figures, the Risk team flags distressed clients, updates loss provisions, and calibrates screening variables for future underwriting policies. Playter’s Credit team and leadership use the input to “turn the tap on and off” based on evolving market conditions and risk appetite. 

Management account data powered by Codat 

When evaluating a credit decision, most SME lenders review an applicant’s filed accounts.  However, this data can be up to 12-18 months out of date. Using Codat’s API, Playter asks applicants to connect their accounting platform and open banking data.  It pulls data from applicants’ management accounts (e.g. Xero, Quickbooks, SAGE) and bank directly into the Wiserfunding platform. By using fresh data, underwriting decisions are based on a more accurate, up-to-date picture of the applicant’s financial health.   

Results 

Speed to value. From start to finish, Playter fully implemented the Wiserfunding solution in under one week. Now, the applicant screening process is instant, credit decisions are made in 1 hour, and most approved customers receive their cash in 24 hours or less. For customers, the difference is clear: while banks take weeks to months for similar decisions, Playter’s customers can go from application to cash in less than two working days. 

Growing the pie. Playter has increased the size of its addressable market by responsibly managing and expanding the constraints of SME lending. With accurate, fresh management account data provided by Codat, borrowers previously locked out of lending from traditional institutions can now prove their creditworthiness and receive funding, and Playter can provide larger credit limits to healthy customers. 

Optimising margins. Better, fresher data means that Playter has been able to reduce the costs of bad debt and defaults. Highly automated credit decisioning allows their team to focus on solving edge cases and driving customer experience, so the company can sustainably scale its portfolio much faster than headcount.  

Strengthening investability.  Since implementing the Wiserfunding and Codat solution, Playter has raised over $55mn in combined funding to fuel its journey. The reliability of their risk engine has helped Playter convince both VCs and institutional investors of their model’s sustainability. 

Changing the game. Playter does BNPL differently. Other lenders often assess individual transactions for customers, which can easily lead businesses down the slippery slope of over-indebtedness. Instead, Playter lends responsibly by evaluating the holistic creditworthiness of an entire business. With Wiserfunding and Codat’s support, Playter excels in delivering its vision of ethical lending.