View the interview at Forbes.com here
Over the past decade, the European non-performing loans (NPLs) industry has matured, with loan sales and securitisations becoming the modus operandi for banks, and several investors actively entering into NPL transactions. But just as the NPL market achieved a steady, effective pace, the Covid-19 pandemic brought a very rapid and deep fall in economic activity.
With the level of uncertainty high, it is difficult to produce projections. However, this sudden halt is highly likely to cause a re-emergence of the NPL problem. According to recent research of the European Central Bank, during crises NPLs typically follow an inverse-U pattern. They start at modest levels, rise rapidly around the start of the crisis, and peak some years afterwards, before stabilising and declining.
Preparing a plan now to identify and deal with vulnerable loans is imperative, and it starts with developing a proactive debt management mechanism tailored to the creditor’s asset classes and customer circumstances. This mechanism requires accurate and timely loan and customer data, which often entails changes to legacy IT systems.
The ability to spot how individual customers will be impacted by the pandemic will be the differentiating factor and value driver for banking and lending organisations.
The Covid-19 outbreak immediately changed the way people work, shop, socialise, interact with their bank, and make payments, with a significant percentage moving to digital options for the first time. Unemployment levels have risen and will continue to rise, as various protection schemes, such as furlough, are withdrawn, and this will undoubtedly lead to higher levels of indebtedness.
Identifying whose financial circumstances are adversely affected and to what extent is difficult – especially given the variance in moratoriums, the duration of the recession, the pace of an economic recovery and the changes in consumer behaviour caused by lockdowns.
The use of established models to predict future behavior has become somewhat arbitrary given the increase in unknowns. These models no longer support the new approach needed to cater for a Covid-19 world, and businesses are having to adapt. Circumstances today call for adaptive models that are constantly updating and quickly recognise changes in behaviour, re-calibrating and/or rebuilding them when needed. Daily feeds of large volumes of data into these models, such as those reflecting the frequency of reaching credit limits, or frequency of contact via online help pages or missing payments, enable continuous change and lead to more accurate predictions and personalised treatment paths.
At QUALCO we have seen that combining this approach with machine learning and a comprehensive collections system revolutionises NPL management operations and radically reduces losses.
A 30 per cent improvement in efficiencies and higher cash flow rates can be expected by introducing ML behavioural analysis and digital tools that allow customers to interact with their creditors.
Acting now to align operational activity with constantly changing customer behaviour will allow creditors to respond quickly and effectively to the inevitable increase in non-performing exposures. Failure to take steps to mitigate the risk today will ultimately lead to increased losses and swamped collection centres.