Accounts Payable is no longer just about invoices and due dates—it's becoming a strategic lever for business growth. Thanks to AI, companies can now predict cash flow, optimize supplier payments, reduce fraud, and unlock savings through dynamic discounting. In this episode, we explore how AI-driven automation is transforming payables management, boosting efficiency, improving supplier relationships, and driving smarter financial decisions.Whether you're a CFO, treasurer, or finance professional, this is your insider’s guide to leveraging AI for a future-ready payables strategy.Glossary of AI Models & Techniques Used in Payables OptimizationMachine Learning (ML): AI that improves decision-making in payables automation by identifying patterns in financial transactions.Natural Language Processing (NLP): AI used for invoice scanning and data extraction, eliminating manual entry errors.Reinforcement Learning (RL): AI that optimizes payment scheduling by dynamically adjusting supplier payments based on real-time cash flow.Neural Networks (NN): A deep learning model capable of identifying complex patterns in financial data for fraud detection and predictive analytics.Decision Trees: A supervised learning algorithm used for classifying invoices, prioritizing payments, and predicting supplier risk.Random Forests: An advanced form of decision trees, improving accuracy in detecting fraudulent payables transactions and predicting supplier creditworthiness.Gradient Boosting Machines (GBM): Used for automated credit scoring, helping companies evaluate the risk of delaying payments.Generative Adversarial Networks (GANs): Used for detecting fake invoices and fraudulent transactions in AP systems.Graph Neural Networks (GNNs): Applied in supply chain finance to analyze relationships between suppliers, predicting risks and optimizing working capital strategies.Bayesian Networks: AI models that predict cash flow volatility and help CFOs make data-driven payables decisions.Main SourcesMcKinsey AI & Finance Report (2023)Deloitte AI in Treasury Management (2024)Goldman Sachs Working Capital Research (2024)J.P. Morgan AI Payables Optimization Study (2024)BNP Paribas AI in Treasury Operations (2023)Alibaba AI-Payables in Supply Chain Finance (2023)HSBC AI Risk & Fraud Analysis (2023)IBM NLP Invoice Processing Report (2024)further read : https://doi.org/10.30574/wjarr.2024.23.1.2141An AI generated Podcast based on open source information selected and reviewed by a Gen X brain.David Perron is a seasoned finance executive with over two decades of expertise in Trade Finance, Working Capital Management, and Corporate Banking. Recognized for his strategic vision, leadership acumen, and deep understanding of complex financial structures, he has successfully led high-profile teams across major global financial institutions such as JPMorgan, Barclays, and HSBC. His expertise spans structured trade finance, supply chain finance, receivable finance and inventory finance.In addition to his extensive professional experience, David is currently pursuing an Executive Master in Artificial Intelligence for Innovative Managers at the Institut Mines-Télécom Business School (a leading institution comparable to MIT Sloan). This advanced degree reflects his commitment to integrating AI into financial solutions, particularly in trade finance and working capital optimization.David is a strong advocate for fintech innovation, compliance, and efficiency in the financial services sector. His career is marked by significant achievements, including structuring multi-billion-dollar portfolios, enhancing revenue streams, and exploring AI-driven trade finance solutions. He is also an accomplished public speaker.
