AWS re:Invent 2025-PwC & BMO: AI-Powered Finance Transformation -From Reporting to Treasury Insights

AI-Powered Finance Transformation: From Reporting to Treasury Insights

Overview

This presentation discusses the 2-year journey of PwC and Bank of Montreal (BMO) in leveraging AI to drive finance transformation. The speakers, Reshett from PwC's financial transformation practice and Ram, the Chief Data and Analytics Officer for Risk and Finance at BMO, share their experiences, challenges, and key learnings from this collaborative effort.

Driving Finance Transformation with AI

• Motivation for Transformation: The speakers highlight the need to move beyond traditional finance processes and embrace AI as a tool to drive efficiency, effectiveness, and enable finance teams to become better business partners.
• Challenges and Obstacles:
  • Building trust and governance around AI models, ensuring fairness, transparency, and accountability.
  • Bridging the gap between successful pilots and production-ready solutions, especially in a highly regulated industry like banking.
  • Ensuring the accuracy and precision of AI-generated financial data and insights to meet regulatory requirements.
  • Enabling finance professionals to effectively leverage AI and upskilling them with necessary digital and soft skills.
• Approach to Use Case Selection:
  • Evaluating use cases based on three pillars: Efficiency (process optimization), Effectiveness (accuracy and precision), and Enablement (employee experience and upskilling).
  • Prioritizing use cases based on business value, feasibility, alignment, and regulatory risk.
  • Focusing on a mix of productivity, transformation, and disruptive use cases.

Accelerating Time-to-Value with Agile Delivery

• Embracing Rapid Prototyping: The team challenged themselves to reduce the traditional 3-month timeline for MVP development to just days, leveraging techniques from hackathons.
• Adapting to Evolving Tools and Technologies: Navigating the challenge of incorporating new AI tools and services into a regulated environment, while ensuring appropriate governance and controls.
• Upskilling and Change Management: Implementing programs like "BAI for All" and "AI for Finance and Risk" to educate and empower employees on AI capabilities and applications.
• Fostering Cross-Functional Collaboration: Bringing together stakeholders from various domains, including risk, legal, and compliance, to ensure alignment and buy-in from the start.

Leveraging AWS for Secure and Scalable AI Solutions

• Data Governance and Synthetic Data: Leveraging synthetic data to overcome data sensitivity and confidentiality challenges, while maintaining data representativeness for model training.
• Agentic AI Capabilities: Exploring the use of AI agents to generate synthetic data, fine-tune ML models, and automate various finance and risk-related tasks.
• Scalable and Secure Cloud Infrastructure: Utilizing the AWS platform to build scalable, secure, and compliant AI solutions for finance and risk use cases.

The Future of Finance: Insights and Outlook

• Shifting Focus to ROI: Moving from the experimentation phase to a more strategic, value-driven approach, aligning AI initiatives with the overall business strategy.
• Accelerating Time-to-Value: Continuing to challenge traditional timelines and processes, leveraging rapid prototyping and agile delivery to quickly iterate and deliver tangible results.
• Expanding AI Capabilities: Exploring the potential of agentic AI systems to automate and augment finance and risk-related tasks, while maintaining appropriate governance and controls.
• Upskilling and Change Management: Ongoing efforts to educate and empower finance professionals to effectively leverage AI and become strategic business partners.

Key Takeaways

1. AI is a powerful tool for driving finance transformation, enabling efficiency, effectiveness, and employee enablement.
2. Establishing robust AI governance, including fairness, transparency, and accountability, is crucial in a highly regulated industry like banking.
3. Adopting agile delivery methods and rapid prototyping can significantly accelerate time-to-value for AI-powered finance solutions.
4. Leveraging synthetic data and agentic AI capabilities can help overcome data sensitivity challenges and automate various finance and risk-related tasks.
5. Effective change management and upskilling of finance professionals are essential for successful AI adoption and transformation.