Spyros Retzekas |
Non-performing loans (NPLs) reduce cash flow, tie up capital, and reduce profitability. That is why a creative and effective approach to overdue debts makes sense.
According to data from the World Bank, non-performing loans held by banks in some countries can be more than a third of the total gross loans on their books. In Greece, for example, the proportion stands at 34%, in Cyprus almost 49%.
These extreme examples serve to illustrate the damage that NPLs can wreak on bank balance sheets and they do so in every banking market in the world. Moreover, all type of businesses that concedes credit to its customers including utility companies and retailers, experience late payments and bad debts.
Resolving bad debts enables businesses to increase their working capital with less recourse to borrowing.
It also releases capital buffers held against the risk of loss. This is particularly true of regulated businesses such as banks, where the level of their capitalisation is established through regulation. Uncontrolled NPLs therefore invite regulatory scrutiny.
Many debt owners do not understand the debt that they hold. That is to say that they may know what the outstanding figure is but they have not got to grips with why the debts remain unpaid and what the chances of repayment are.
For this reason, careful cleaning and checking of the known data behind a non-performing facility needs to be carried out.
As far as possible, using internal information and that available outside of the organisation, the goal must be to get to know the customer. This requires investment in suitable technology resources.
Data analytics carried out across one or more portfolios of NPLs, segmenting NPLs by duration, size and nature of debt reveal patterns of behaviour, geographic and demographic trends and offer clues to the best way to approach customers.
This may involve devoting internal resources – technology and staff – to properly address the customers or finding a suitable debt collection agency (DCA) to outsource collection and recovery (C&R) to.
Choosing DCAs according to their particular skills and geographical coverage is an important step.
Some DCAs will be better skilled at collecting short overdue payments in a particular region. Others may specialise in longer debt in other areas. Applying agile, flexible technology to match NPL data with details of DCAs is a vital step.
Once this has been achieved, DCA performance needs to be monitored and measured against agreed target performance levels.
If a DCA underperforms in its collection tasks, it may be because they are adopting an approach that needs adjustment or because the nature of the debt requires another approach altogether.
At the same time, adhering to regulatory guidelines on fairness in the treatment of overdue customers is important, not least because the reputation of the debt owner could be damaged if not performed correctly.
An ideal fair outcome would be one where the customer meets his or her obligations to the owner of the debt and remains on good terms as a continuing customer.
Selling a portfolio of debts to a third party could also be an option. This could be the case where the outstanding debts are larger or of a longer duration and where a specialist debt buyer would have particular skills and resources that enable them to make recoveries.
In either case, whether the original debt owner retains the debt or sells it, there are immediate benefits.
Firstly, there is a cash flow gain. In the case of debt sale the gain is one-off. Where the debt is retained and a successful strategy and processes have been put in place there will be an ongoing cash flow benefit.
Secondly, reduction on NPL volume releases loan loss provisions. In the case of organisations with regulatory capital, this means the release of scarce capital for deployment in other business and improvement in return on capital measures.
Overall, the result is greater balance sheet efficiency. This will have been achieved simply by adopting the right NPL strategy, and executing it through the use of technology to apply data analytics to a legacy issue of uncollected debts.
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