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Introducing our Propensity and Risk Model (PRISM)

19th Nov 2018   ·   The ThinCats Team   ·   News

Data science and analytics is embedded deep within ThinCats’ business principles. It is the company’s aim, to develop some of the industry's most predictive models; to identify and fund the underserved companies that ultimately drive our economy.

Harnessing data in a digital age involves a relentless search for knowledge, both from traditional sources (financial performance, business demographics, market positioning and macroeconomics) and alternative sources (behavioural and growth indicators, peer-group outperformance and digital footprint).

Information wealth is only valuable when coupled with robust analytics that empowers informed yet fair business decisions. ThinCats’ evolutionary statistical models combine a depth of SME knowledge which stems from decades of industry experience in building risk systems, together with frontier machine learning techniques.

PRISM (“Propensity and Risk Model”) represents a suite of predictive models that are used to enhance our understanding of a potential borrower. This enables us to price risk in a way that we believe better reflects a business’ true potential.

PRISM Risk is used to give early indication of our credit appetite and pricing of a potential business loan. The model combines market and company financial data with proprietary non-financial data to provide a credit grade from 1 star (weak) to 5 stars (strong). The model also provides a security grade for each loan taking into account balance sheet assets and certainty of future cashflows.

PRISM Prospect identifies the likelihood of a business to have a funding need over the next 12 months. The model segments businesses into five propensity categories from very likely to unlikely. Whilst PRISM Prospect has obvious benefits for ThinCats in terms of identifying potential borrowers, the model also shows that companies with stronger growth characteristics generally have a higher propensity for funding. ThinCats has found that these companies are typically underserved by traditional lenders.


Find out more about how we harness data

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