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Wiserfunding, Trade Ledger announce risk partnership

Trade Ledger and Wiserfunding are partnering to allow banks and alternative finance providers to more accurately assess their mid-market commercial customers for secured and unsecured credit.  Trade Ledger, the specialist mid-market commercial Lending-as-a-Service platform, is making Wiserfunding’s business credit score, the SME Z-Score, available within both loan origination and management portals.

Roger Vincent, Chief Innovation Officer at Trade Ledger said: “Inaccurate and slow credit risk assessment, for SME commercial loan requests is one of the major reasons that over 50% of loan applications are currently declined by financial institutions (FIs).  This has created a global credit gap and is stunting economic growth and social mobility.

“To address this issue risk assessments must be exceptionally accurate and utilise unstructured, as well as structured, big data inputs and machine learning techniques in this continually evolving environment. WiserFunding, uniquely does just that, but offers the reassurance of the best of academia and its established client base, while drastically reducing the time taken to underwrite counterparties.”

Gabriele Sabato, CEO of Wiserfunding, said: “Trade Ledger were the perfect fit for our credit risk assessment solution. As one of the fastest growing lending technology providers in the world, their data-driven approach to commercial lending provides the ideal platform for underwriting and portfolio management teams to accelerate their risk management capabilities. With Wiserfunding’s SME Z-Score embedded within the platform, these institutions can now start to think about more advanced segmentation, automation and straight-through-processing, advancing the accuracy to record levels and profiting from this hugely underserved market.”

The new global partnership will aim to target mainly mid-market lenders and banks, who have traditionally used manual risks assessments to identify high-risk customers. The score is tailored specifically for SMEs with revenue between £500,000 and £200 million. On average, the score delivers between 83% and 95% default prediction accuracy, 20-30% higher than other than other industry benchmarks in this space.

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