De-Risking Generative AI in Transaction Banking: From Proof of Concept to Production

Generative AI is rapidly transforming transaction banking, promising unprecedented efficiency, personalization, and value creation for banks and their clients. Yet, as banks move from experimentation to enterprise-scale deployment, the journey is fraught with operational, regulatory, and reputational risks. For decision-makers in transaction banking, the challenge is clear: how to harness the power of generative AI while safeguarding customer trust, ensuring compliance, and delivering measurable business value.

This guide provides a practical roadmap for banks and financial institutions to de-risk generative AI adoption in transaction banking—moving beyond proofs of concept (POCs) to robust, scalable production solutions. Drawing on Publicis Sapient’s frameworks and real-world experience, we outline actionable strategies for risk mitigation, governance, and responsible AI adoption, while highlighting the early-mover advantage and the critical importance of upskilling talent for sustainable transformation.

The Promise and Peril of Generative AI in Transaction Banking

Generative AI is redefining transaction banking by enabling:

Real-time, 360-degree liquidity insights across multiple banking relationships
Automated, no-code dashboards for working capital management and forecasting
Embedded finance that integrates banking directly into clients’ ERP and treasury systems
Proactive, AI-driven recommendations for cash optimization and credit access

However, the path from POC to production is not straightforward. Many banks encounter hurdles such as:

Legacy technology and fragmented data that hinder integration and scalability
Siloed teams and shadow IT leading to inconsistent approaches and security risks
Unclear governance and regulatory uncertainty
Customer experience risks from inaccurate, biased, or opaque AI outputs
Data security and privacy concerns

Key Risk Areas and Mitigation Strategies

1. Model and Technology Risk

Challenge: Selecting, scaling, and maintaining the right AI models is complex. Costs can escalate, and rapid model updates may outpace integration efforts.

Mitigation:

Design for portability and scalability from day one. Choose models that balance accuracy, speed, and cost, and plan for ongoing evaluation as technology evolves.
Monitor model performance and drift continuously, with clear protocols for updates and fallback.
Leverage modular, cloud-native architectures to decouple AI services from legacy systems, enabling rapid integration and future-proofing.

2. Customer Experience Risk

Challenge: Generative AI can produce irrelevant, biased, or incorrect responses, undermining trust and usability.

Mitigation:

Invest in prompt engineering and human-centered design to ensure outputs are accurate, relevant, and intuitive for users.
Split complex requests and embed context to reduce hallucinations and improve response quality.
Provide structured options and guidance within AI-powered tools, making them accessible to users without technical expertise.
Continuously refine user experience based on feedback and evolving model capabilities.

3. Data Security and Privacy Risk

Challenge: Handling sensitive financial data in AI workflows raises the stakes for privacy, compliance, and reputational risk.

Mitigation:

Avoid using personal or sensitive data in model training and inference wherever possible.
Implement robust data masking, pseudonymization, and sandboxing to prevent data leakage.
Ensure transparency and obtain consent for data use, with clear privacy policies and disclosures.
Partner with trusted technology providers offering enterprise-grade security and compliance features.

4. Regulatory and Legal Risk

Challenge: Evolving AI regulations (such as the EU AI Act) and existing financial laws require proactive compliance.

Mitigation:

Build cross-functional teams including legal, compliance, data, and technology experts to monitor and interpret regulatory changes.
Document AI use cases, data sources, and risk assessments to demonstrate compliance.
Establish processes for regular review and updates as laws and best practices evolve.
Prioritize transparency, explainability, and auditability in all AI solutions.

5. Governance and Responsible AI

Challenge: Without clear governance, AI initiatives can become fragmented, increasing risk and reducing impact.

Mitigation:

Establish clear principles and frameworks for responsible AI, covering transparency, fairness, accountability, privacy, and beneficence.
Define roles, responsibilities, and escalation paths for risk management and incident response.
Implement continuous monitoring, feedback loops, and improvement cycles to adapt to new risks and opportunities.

Best Practices for Moving from POC to Production

1. Start with High-Value, Low-Risk Use Cases

Focus initial deployments on areas where generative AI can deliver clear business value with manageable risk—such as internal process automation, customer support, or liquidity forecasting. Use these as learning grounds to refine governance and technical approaches.

2. Build Cross-Functional, Agile Teams

Break down silos by assembling teams that span business, technology, data, compliance, and customer experience. Empower these teams to iterate quickly, learn from real-world feedback, and scale successful solutions.

3. Invest in Data Governance and Core Modernization

Modernize core banking systems and data architectures to enable secure, real-time data access and integration. Decouple data from legacy systems and adopt cloud-native, API-driven platforms to support AI at scale.

4. Prioritize Change Management and Talent Upskilling

Generative AI success depends on people as much as technology. Invest in upskilling employees, fostering a culture of experimentation, and providing hands-on experience with AI tools. Early movers who build internal expertise will outpace competitors as the technology matures.

5. Monitor, Measure, and Continuously Improve

Establish clear metrics for success, monitor outcomes, and adapt solutions based on performance and user feedback. Plan for ongoing model updates, regulatory changes, and evolving business needs.

The Early-Mover Advantage

Banks that move decisively to scale generative AI—while managing risk—stand to gain a lasting competitive edge. Early adopters benefit from:

Faster learning cycles and proprietary data advantages
Enhanced customer experiences and operational efficiency
New revenue streams from AI-powered and embedded finance offerings
A future-proofed, agile workforce ready for ongoing transformation

Why Publicis Sapient?

Publicis Sapient partners with leading banks to design, build, and scale generative AI solutions that are secure, compliant, and business-driven. Our expertise spans strategy, product, experience, engineering, and data—enabling us to:

Define and execute AI-first, embedded finance strategies
Build modern, cloud-native platforms and data architectures
Implement robust governance and responsible AI frameworks
Upskill teams and drive continuous innovation

Conclusion: From Experimentation to Enterprise Value

The future of transaction banking is intelligent, embedded, and client-centric. By de-risking generative AI adoption—through robust governance, cross-functional teams, and a relentless focus on responsible innovation—banks can move confidently from proof of concept to production, unlocking new value for clients and the business alike.

Ready to accelerate your generative AI journey in transaction banking? Publicis Sapient stands ready to partner with you—combining deep industry expertise, proven frameworks, and cutting-edge technology to deliver scalable, AI-powered solutions that drive growth, compliance, and customer trust.

To learn more about how Publicis Sapient can help your bank de-risk and scale generative AI in transaction banking, visit publicissapient.com/fs.