Who Are Playter?
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.
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
Challenges
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 underwrtei 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.
Solution
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.
Read the full case study here.
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