The main causes of non-payments and the need for an effective strategy

Financial, liquidity and economic problems, unexpected incidents, poor management, lack of an invoice tracking system, emotional problems are some of the main causes for late or non-payments
Modellica Collection Suite from GDS Modellica uses the power of data analysis to help businesses to increase revenue by automating and accelerating their collection processes
According to the European Payment Report by Intrum, the present COVID-19 crisis has led four of every five Spanish businesses to extend their payment terms to suppliers in order to not damage their relationship with customers. Numerous businesses and self-employed workers have seen both their finances and ability to meet payment obligations seriously affected, with consequences not just for their liquidity but also their workforce. In just one year, the number of businesses who believe that debt risk will increase next year has multiplied by five. There is growing pressure on businesses to recover unpaid debt with current default rates in an increasingly competitive global market.
GDS Modellica offers the solution Modellica Collection Suite, which provides data analysis to help businesses increase their revenue by automating and speeding up their collection processes. Debt Management is one of GDS Modellica’s effective solutions which provides businesses with precise segmentation of debt accounts and visibility of one-to-one, personalised customer data in real-time. It analyses the data and manages the customer experience to understand their behaviour and specific messages and evaluate the level of risk. This way, it is able to strategically direct the business’ interactions to increase debt recovery, focus collection activity, automate decisions and increase customer retention by providing a higher-quality service.
Amongst the main causes for late or non-payments and the impact they have on debt, GDS Modellica highlights the following:
Financial or liquidity problems. Sometimes businesses don’t have enough funds to cover operating costs and other payments, resulting in a cash flow problem which can briefly affect their ability to pay debts.
Economic reasons. Businesses are unable to make payments for various economic circumstances, but the most common is a drop in sales or revenue.
Unfortunate events or incidents. These are sudden changes in circumstances that change the future of the business, whether it be an illness, accident or the emergence of a new competitor on the market.
Poor management or negligence. This can cause significant harm to suppliers and lead to a failure to make payments and meet obligations.
Lack of an invoice monitoring system or IT system that detects late payments and how fast they accumulate. Without a debt monitoring system, the warning signs may be noticed too late.
Finally, Emotional reasons. These are also relevant and go beyond simple good manners and politeness. Poor behaviour doesn’t tend to go hand in hand with good results when it comes to debt recovery.
Businesses need to be prepared when it comes to identifying risk and recovering debt. Before awarding credit to a new customer, it’s important to pay special attention to certain basic requirements, in particular: the payment conditions, payment methods, potential delays to payments, the customer’s operating conditions and being careful with customers that have been rejected by competitors. Often, late payments are the result of a lack of foresight. However, today, there exist a number of mechanisms to help avoid, predict and prevent the risk of late or non-payments. One example is the Modellica Collections Suite. This is a highly configurable and effective solution which addresses a wide range of needs when it comes to ensuring the recovery of debt. At each stage of the credit cycle, the appropriate treatment is used to restore payments and ensure that customers meet their obligations. Furthermore, it can also be used as an effective tool for segmentation, communication and decision strategies that use predictive analysis, modelling and scoring to prioritise recovery efforts.
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