Credit Management
Is your credit risk data up to scratch? By Patrick Coghlan MICM* The continuing uncertainty in trading conditions and health of businesses due to the spread of the Omicron variant means business risk intelligence is arguably more important than ever before. But are your data sources cutting it?
In the digital age, data has become a key driver of growth, with detailed insights guiding businesses to success. The financial services sector is heavily reliant on data, with banks, insurance companies and any organisations who deal in risk seeking to bolster their modelling and reduce defaults. Channelling rich, new data into these models can provide the clarity needed for enhanced decision making – both in identifying credit risk from the outset at the application stage, and on an ongoing basis and credit risk management over time – alerting financial organisations to timely indicators that are statistically likely to impact their bottom line.
More data, richer models
Patrick Coghlan MICM
Organisations that manage credit have always relied on traditional risk indicators like bankruptcies, insolvencies and court judgments from ASIC, ABR, AFSA, the Australian courts and Insolvencynotices.gov.au. While these are still a vital part of the picture, granular detail is often lacking.
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The value of this financial risk data lies in its predictive abilities. It reveals how businesses are faring right now, in terms of making payments they owe to other businesses and enables organisations who manage credit to see signs of distress early. Are they paying their invoices quickly? Within 30 days? Or are payments taking two or three months? What’s their overdue invoices status? Late trade payments are a clear predictor of future defaults and credit risk, so these insights are extremely valuable, particularly when they can be easily compared to industry standards for greater context. But why stick to purely traditional risk indicators? For example, CreditorWatch is also able to generate digital insights from more than 11 million tradelines and 15 million monthly invoices. This brightens and sharpens the picture dramatically. More big data enriches models and enhances your ability to predict future default and credit risk; a boon for businesses seeking to avoid poor credit approval decisions and reduce exposure to costly defaults.
