The EU AI Act is here. August 2026 is your compliance deadline. Are you ready?The world's first comprehensive AI regulation isn't just another compliance checkbox. With penalties reaching €35M or 7% of global turnover, misinterpreting this framework is a strategic failure that boards cannot afford.Here's what makes this moment critical: we're witnessing a fundamental divergence in global AI governance. The EU has deployed a horizontal, prescriptive regulation with clear timelines and mandatory obligations. Meanwhile, the US continues with its vertical, sector-specific patchwork—a fragmented landscape of state laws and existing regulations applied case-by-case.For financial institutions operating across jurisdictions, this creates both complexity and opportunity. The EU's extra-territorial scope means that even US-headquartered firms fall under its jurisdiction if their AI systems affect EU citizens. The strategic questions are no longer theoretical:Which of your AI systems are classified as high-risk under Annex III? Credit scoring and insurance underwriting clearly fall within scope, but there's a critical exception many institutions are missing: AI systems for fraud detection are explicitly carved out from high-risk classification. This distinction has profound implications for your AML and transaction monitoring investments.How do you integrate the AI Act with GDPR and DORA? These three regulations form a compliance trinity that cannot be managed in silos. Your governance framework must address all three simultaneously.What are the 7 mandatory obligations for high-risk systems, and how do you implement them by August 2026? From risk management and data governance to human oversight and technical documentation, the requirements are substantial and time-sensitive.In my new 18-minute executive briefing, I provide a strategic framework for navigating this new era. This isn't a summary of headlines. It's a detailed analysis based exclusively on the official regulatory texts—the AI Act itself, the December 2024 US Treasury report, and BIS regulatory guidance.We dissect the phased implementation timeline from 2024 to 2027, reveal the critical fraud detection exception, and provide a 5-step classification framework to assess your AI inventory. Most importantly, we contrast the EU's approach with the US landscape to help you build a coherent, cross-jurisdictional compliance strategy.This is for experienced risk officers, compliance leaders, CTOs, and board members who need actionable intelligence, not generic overviews.#AI #AIAct #FinancialServices #RiskManagement #Compliance #RegTech #Banking #Insurance #FinTech #Governance #EURegulation #DigitalTransformation #Leadership #DavidPerron

Agentic AI in Banking and Finance: The Future is Now | Professional Insights for Financial Leaders

Discover how agentic artificial intelligence is transforming financial operations—from trading floors to treasury departments. In this 16-minute deep dive, we explore the cutting-edge technology that's revolutionizing our industry.

What You'll Learn:

The 3 core architectures of agentic AI (Planner-Executor-Reviewer, Multi-Agent Societies, Finite-State Graphs)

Real-world applications in trading, treasury operations, and compliance (KYC/AML)

Critical insights from NBER, BIS, and FSB research on AI risks and opportunities

Regulatory frameworks: EU AI Act, SEC guidelines, NIST AI RMF, and BoE SS1/23

A 6-step roadmap to deploy agentic AI in your financial institution

Security best practices: OWASP Top 10 for LLMs and model risk governance

Key Technologies Covered:

AutoGen (Microsoft), LangGraph, CrewAI, Model Context Protocol (MCP)

Whether you're a CFO, risk manager, compliance officer, or fintech innovator, this podcast provides the strategic insights and practical frameworks you need to harness AI responsibly and effectively.

The future of finance is collaborative—a partnership between human expertise and artificial intelligence. Are you ready to build it?

Subscribe for more insights at the intersection of technology and finance.

#AgenticAI #FinTech #FinancialOperations #AIinFinance #RegulatoryCompliance #TreasuryManagement #FinancialInnovation #MachineLearning #AIGovernance #FutureOfFinance

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.

Podcast Episode Title: "AI and the Future of Banking Jobs: Automation, Workforce Aging, and HR Challenges"

Episode Overview:
Artificial Intelligence is transforming the financial industry. Banks are integrating AI into trade finance, compliance, risk management, and working capital optimization. But what does this mean for the workforce? Will AI create new opportunities or replace traditional roles?

Key Takeaways:

• Up to 25% of banking jobs could be automated by 2027 (McKinsey, 2023).

• 85% of banks are investing in AI for process automation (Deloitte, 2023).

• 70% of trade finance operations could be digitized with AI (World Trade Organization, 2023).

• AI reduces fraud risks by 50% and speeds up decision-making in credit and compliance (PwC, 2023).

Listen to the podcast for more

• Who’s at Risk?

• Who’s Benefiting?

• Regional Breakdown

• Future of AI and Banking Jobs

• How Should Banks Adapt?

  
  

Main Sources:

• McKinsey AI & Banking Report 2023

• World Trade Organization Trade Finance Report

• PwC AI in Finance Study

• Deloitte Banking Automation Report

• BIS

• The Economist

An 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.

Hosted by Ausha. See ausha.co/privacy-policy for more information.

Artificial Intelligence (AI) is revolutionizing international trade finance. It automates and optimizes letters of credit, reducing errors and accelerating payments. AI enhances fraud detection and regulatory compliance through machine learning and blockchain. Predictive risk analysis improves credit assessment and adjusts financing conditions. While the adoption of electronic letters of credit and electronic bills of lading is increasing, challenges remain regarding interoperability and legal recognition.

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You need buzzwords on artificial intelligence applications for trade finance and working capital - this 20-minute podcast will entertain you during your next commute.

Level 1: cocktail talks and unexpected meetings.

An AI generated Podcast based on a Gen X brain.

#Finance #Fintech #TradeFinance #CorporateFinance #WorkingCapital #CashFlowManagement

#ArtificialIntelligence #AIinFinance #MachineLearning #DigitalTransformation

#FintechInnovation #Blockchain

#BusinessGrowth #BusinessStrategy #Leadership #Entrepreneurship #BusinessSuccess

#SupplyChainFinance #WorkingCapitalOptimization #InvoiceFinancing #ReceivablesFinance #LiquidityManagement

#Podcast #FinancePodcast #FintechPodcast #AIinBusiness #PodcastGrowth

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