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?

Patrick Coghlan MICM

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

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.

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.

“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.”

Access to additional unique insights

CreditorWatch’s unique accounting software data, a massive bank of commercial data – that contains more than 40 vital financial factors – can be used retrospectively to provide insight into your customers’ behaviour over the past five years, giving a fuller account of each business’s health.

These insights into past and present behaviour can be seamlessly integrated into your existing workflows, such as your CRM system, ERP or any other thirdparty software, rounding out each profile to help predict the future.

The power of early warning sign alerts

Access to the latest default data can also assist with annual reviews of risk grades, complementing your current data sources to track businesses throughout the customer lifecycle and better identify credit quality deterioration early so timely action can be taken.

The importance of this cannot be understated given that we know: z Small businesses in arrears are more of a default risk compared to those who pay on time. { 60+ days arrears are more than five times the average default risk { 30-60 days arrears are approximately three times the average default risk z Large businesses with 60+ days arrears are more than double the default risk of large businesses that pay within 30 days. z Businesses that pay most invoices on time are less than half the default risk of the average business. z High-trade-activity businesses are less than half the default risk of the average business. z Businesses with recent B2B trade payment defaults are approximately seven times the default risk of businesses with no trade payment defaults.

Algorithm transparency is essential

Algorithms are notoriously secretive, but it is crucial for those who make informed decisions based on insights to know how the information is generated. The aggregate data that contributes to a credit score from CreditorWatch is completely transparent and easily shared. In the field of finance – a world where numbers are of great importance – there shouldn’t be any secrets.

Experienced data scientists

James O’Donnell, the Open Analytics Managing Director, who was a Westpac senior credit decisioning specialist for 15 years, has transformed CreditorWatch’s data into user-friendly insights of the greatest value to the banking and financial services sector.

“In such uncertain times as we are in now thanks Omicron, having access to important, accurate, timely data is essential,” James says. “Critical insights give banks, insurance companies and any organisations that work in the risk space a holistic, comprehensive picture of how sound and safe a business is – right now and over time. As we navigate through great economic uncertainty, that information is more important than ever.”

*Patrick Coghlan MICM CEO CreditorWatch Ph: 1300 50 13 12 www.creditorwatch.com.au