Data-Driven Finance. AI-Powered Order-to-Cash
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Shared services organizations are under pressure to improve cash flow, reduce costs, and deliver better business insights, all while managing growing transaction volumes and rising customer expectations. Leading finance teams are turning to AI and data-driven automation to transform the order-to-cash process from a transactional function into a strategic driver of working capital performance.
Join this session to learn how modern finance organizations use AI-powered intelligence to predict payment behavior, prioritize actions, automate routine processes, and uncover insights hidden within their data. Discover how shared services leaders can improve efficiency, accelerate cash conversion, and free teams to focus on higher-value work while delivering measurable business outcomes. The discussion draws on emerging approaches that use AI to predict risk, automate actions and turn financial data into actionable intelligence.
Key takeaways include:
- How AI can help prioritize collections and cash application activities
- Best practices for building a trusted data foundation for intelligent finance operations
- Ways to improve working capital and productivity without increasing headcount
Attendees will learn how to prioritize collections and cash application with AI, build a trusted data foundation for intelligent finance operations, and improve working capital and productivity without adding headcount
Speakers
Mike Swantek
Principal Technical Program Manager
Microsoft
Keith Cowart
Global Market Owner, Automated Finance
FIS
Keith Cowart is the Global Market Owner for Automated Finance at FIS which features a suite of Receivables Automation, Payables Automation, and Revenue Optimization solutions that modernize the office of the CFO. He has over 25 years of professional experience in various accounting and finance leadership roles including Accounts Payable, G/L Accounting, and Credit and Collections in large global companies with shared service centers. Keith’s focus has always been in continuous improvement and leveraging technology to automate processes which drive results. Keith holds a Bachelor of Business Administration degree from Piedmont University and a Master of Business Administration degree in Management from Georgia State University.
Grahame Vincent
Director of Global Credit & Collections
The Trade Desk
Grahame Vincent is a global credit, collections, and working-capital leader with more than 35 years of experience helping organizations turn financial risk, cash flow, and customer growth into strategic advantage. As Director of Global Credit & Collections at The Trade Desk, he leads in a fast-moving, data-driven technology environment where credit decisioning, collections strategy, process automation, and emerging AI capabilities are reshaping the future of finance operations.
His career spans Big 4 consulting with PwC and senior leadership roles across FTSE 100 and FORTUNE 500 organizations, with experience across the full cash-conversion cycle, more than 25 countries, and multiple sectors. A Fellow and Graduate of the Chartered Institute of Credit Management since 1994 and a Certified International Credit Professional (CICP, FCIB) since 2026, Grahame combines technical credit expertise with a practical understanding of how technology, analytics, and AI can improve controls, accelerate cash flow, and support better decision-making at scale. He is an active voice in the global credit community, contributing to NACM and FCIB discussions and conversations on the future of credit, risk, and AI-enabled finance. His perspective bridges traditional credit discipline with the opportunities of modern finance transformation—making him a compelling contributor to conversations on the future of credit, risk, and AI-enabled finance.
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As this is FIS-branded content, by clicking 'Continue' you agree to having your details passed onto the sponsor, FIS, who may promote similar products and services related to your area of interest subject to their privacy policy. You have the right to object here.
