Lenders should lean on technology

The Covid-19 pandemic has accelerated many lenders’ plans to build technology into their credit risk assessments, according to Richard Topham, Director of Strategic Sales, EMEA at Experian.

Credit risk assessment is the art of using past performance to understand how people or businesses may react when faced with comparable conditions in the future.

One of the many difficulties posed to lenders by Covid is that it’s the first global pandemic for a century. There is no pre-existing data for models and scorecards to rely upon.

From my discussions with some of our key clients I know this will be a key topic at Experian’s upcoming CxO Forums, a series of roundtable discussions that I will chair. These events will bring together leaders from around EMEA to focus on the challenges they’re experiencing in the market and share strategies for overcoming them.

I know almost all lenders have reassessed their models and scorecards from conversations I’ve had with industry leaders in recent months. Some have recalibrated them for the new environment, while others have rebuilt them entirely.  But they have found it difficult to know which data sets to use as we move towards a post-pandemic world.

As a result, Artificial Intelligence and Machine Learning are at the forefront of many plans to readjust models swiftly as the data sets shift. Some lenders are more advanced than others in this journey, yet they all recognise the need to implement a technology strategy.

Meanwhile, digitalisation is no longer just about more than smoother customer onboarding journeys, where we have seen initial investments made. It is now in other areas of the customer lifecycle, most notably in collections.

It’s invaluable in helping lenders operate at a time when customer needs are at their highest while many staff continue to work from home, so they can only take calls from those who are a high priority. Lenders are encouraging people to self-serve as much as possible or engage with their ‘chat bots’.

Organisations are also recognising the value of categorisation engines to interpret large quantities of data – such as bank account transaction data from Open Banking – to provide up-to-the-minute insight into the customer’s financial health and to inform decisions.  This can only be done with full automation and advanced Machine Learning using models that can adapt as the landscape continues to shift.

By putting technology, Machine Learning and AI, at the centre of their plans to emerge from the pandemic, some lenders are positioning themselves to be in the best possible shape for whatever challenges the future throws at them.

The question is, are you doing the same?