Infocredit Group is pleased to announce that Lefteris Elia, Risk Methods Advisor at Infocredit Group, has delivered a presentation entitled “Navigating Late Payments: Early Detection and Proven Methodologies for Success” to the Federation of Business Information Services on 10 July. The presentation covered the early warning signs and implementing effective strategies to manage and prevent late payments, ensuring financial stability and business growth.

This webinar was a game-changer for businesses looking to improve financial stability and drive sustainable growth. The presentation addressed the persistent problem of late payment, which significantly impacts businesses worldwide and costs the economy over $40 billion annually. Mr Lefteris outlined the traditional signs of late payment, such as repeated delays and unresponsive communication, as well as conventional methods to prevent and address these issues, including effective communication strategies and invoicing practices.

In addition, Mr Lefteris introduced the use of Artificial Intelligence (AI) and Machine Learning (ML) as innovative tools in late payment management. These technologies improve predictive accuracy, enabling companies to anticipate and proactively address potential delays. A specific case study demonstrated how a machine learning model used public data to predict payment behaviour, showing significant improvements in risk mitigation and operational efficiency.

In his presentation, Mr Lefteris introduced Payer Predictive Analytics, a new state-of-the-art product from Infocredit Group. Payer Predictive Analytics uses a sophisticated machine learning algorithm that analyses extensive historical transaction data to identify patterns that indicate potential payment delays. The model not only integrates transaction history but also takes into account broader economic indicators and seasonal fluctuations, enhancing businesses in achieving more resilient and efficient operations.