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When a business goes into insolvency, there’s an order by which creditors are paid. Secured creditors take precedent, along with the insolvency practitioner, who receives a fee for managing the process. To be a secured creditor, you must have successfully registered your security interests in equipment and goods (‘personal property’) that you’ve sold on terms or leased to the customer. This registration happens on a national online noticeboard known as the Personal Property Securities Register (PPSR).

Few can predict what the economic fallout of the COVID-19 crisis will look like long-term, but even before this pandemic, there was a growing need for lenders to get better at predicting credit risk.  With a greater influx of data and rapid changes in consumer behaviour, traditional risk scoring models like linear regression, are often not up to the task of predicting default risk for complex applications involving multiple data sources and relationships.

Without the right attributes in place, businesses will lose access to vital intelligence behind the ever-expanding data landscape.

“The information consumers generate will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025 across the globe,” predicts market intelligence firm IDC. Along with an average growth of 27% per year from 2018 to 2025, IDC estimates that by 2025, “every connected person in the world on average will have a digital engagement over 4,900 times per day – that’s about one digital interaction every eighteen seconds.”

The arrival of open banking and the growing value of data processing and analysis makes data collaboration within and among businesses more crucial than ever. Meaningful collaboration requires teams and industry partners to work together in real-time, sharing ideas, criticisms and understandings of the data they are all using.

Marketers all over the world are evaluating their marketing spend. Some have hit the pause button. Others are trying to figure out how to proceed. Many are wondering where to find their traditional audiences online. Change is everywhere we look – not only in our daily lives but also in the way we search for and consume content. With change comes deep uncertainty, for both consumers and marketers, about the best way forward through the coronavirus pandemic and its aftermath.

COVID-19 has pushed into the limelight the issue of large firms paying their small business suppliers late. But long before this pandemic, habitual late payment practices have challenged the cash flow of many a small business.

Ready, set, go. Equifax data scientists around the globe sprung into action. The project was to investigate how to classify bank transaction data to make it more meaningful. Could it be categorised in a way that would enrich insights and help lenders better assess risk, ultimately improving customer experience? Competitors were furnished with raw unstructured transaction data from Spain and told to use advanced analytics and innovative techniques to solve this challenge.

The escalation of credit issues under COVID-19 has introduced a new challenge to the hardship strategy of lenders. With a significant increase in the number of accounts requiring financial assistance, the ability to assist customers to better financial health has come under strain. Managing a large volume of customers – each with a different circumstance – lenders must find a way to ensure customers who need the most support are the ones to receive it.

As lenders attempt to navigate through this period of enormous market uncertainty, Equifax will provide a series of regular analysis. Presenting a clear picture of the future for credit markets is challenging in the short term, we hope to provide some clarity on what trends are developing, what challenges are foreseeable, how you can assess the risks to your portfolios and make prudent changes to your risk management controls.

Artificial Intelligence (AI) has taken centre stage during COVID-19, supplementing the work of scientific and medical experts in fighting this pandemic. There are many global examples of AI technologies solving problems across all stages of this crisis. An Australian-developed AI diagnostic tool, for example, is helping hospital staff around the world accurately detect COVID-19 and assist in its containment. An AI-powered research database developed in the US is enabling scientists to discover coronavirus vaccine and treatment literature resources at unprecedented speed. In the UK, University of Cambridge researchers are using AI to analyse patient information in order to predict the risk of COVID-19 patients developing more severe disease and needing respirator support.