Artificial Intelligence is quickly reshaping the retail and financial services landscape. Firms like Amazon, Citi, and C3.ai are integrating AI into core operations for efficient payments, personalized offers, and effective fraud detection. With AI becoming increasingly critical to business strategy, responsible deployment is no longer a choice, it’s a necessity. Bhavnish Walia, who leads AI Risk Management at Amazon and serves as Senior Risk Manager for the company’s Responsible AI initiatives, says: “There’s never been a more critical time to define the future of finance. Generative AI has moved beyond theory, it’s reshaping risk, and our role is to ensure it does so responsibly.”
AI at the Heart of Online Retail
Throughout the retail industry, AI drives quicker checkouts, easier seller onboarding, dynamic pricing, fraud detection in real-time, and personalized promotions. Amazon infuses AI into almost every aspect of its payments and risk infrastructure, from seller onboarding to carrying out anti-fraud measures enhancing both speed and security. But as adoption increases, so do the stakes.
Managing AI Risk at Amazon
Walia has set global benchmarks for the responsible implementation of artificial intelligence in high-risk e-commerce systems. He created Amazon’s first Anti–Money Laundering AI Governance Framework and Model Risk Management Policy to evaluate large language models prior to production deployment, with a focus on mitigating both customer and operational risks.
This framework integrates regulatory scorecards, human-in-the-loop controls, and shadow testing environments, providing a composite evaluation metric to ensure that AI systems in payments and anti–money laundering are compliant, explainable, and fair by design. “As we adopt AI at scale across retail and fraud detection, we can no longer treat these systems as black boxes,” Walia states.
Aligned with regulations such as the EU AI Act, NIST’s AI Risk Management Framework, and the White House’s Blueprint for an AI Bill of Rights, Walia’s approach integrates stringent pre-deployment testing to detect and mitigate model hallucinations, bias, and toxicity, ensuring that LLM models are safe for usage. He has also built post-deployment monitoring systems that are auditable and continuously assess algorithmic behavior, enabling ongoing compliance and transparency.
As Walia’s work at Amazon demonstrates, integrating AI responsibly into online retail systems requires more than technical expertise, it demands structural accountability. This ethos is now influencing the broader industry, with banking institutions implementing similar governance-first approaches to ensure AI not only delivers results but also earns the trust of customers.
Responsible Personalization at Citi
Seth Rubin, formerly VP of Lending Marketing Analytics at Citibank, led transformative efforts in applying AI for pricing optimization and enhancing customer experiences across multiple marketing channels. His team developed machine learning models to predict customer lifetime value as well as price elasticity, allowing data-informed decision-making that weighed business growth against customer trust.
“AI enables us to personalize at scale, but every model we bring to production must meet a high bar for fairness, transparency, and business relevance,” Rubin emphasizes. “It’s not just about what works, it’s about being able to explain why it works, to both stakeholders and regulators.”
Rubin’s approach exemplifies a growing movement across the finance sector: embedding ethical AI governance throughout the modeling lifecycle, from experimentation to real-world deployment.
Scaling Enterprise AI at C3.ai
Meanwhile, enterprise AI firm C3.ai enables online retails and financial institutions to detect anomalies, manage credit risk, and maintain regulatory compliance at scale.
C3.ai Senior AI/ML Software Engineer Swaroop Rath develops generative AI for enterprise applications. His work involves incorporating models like ChatGPT into finance and online retail mission-critical systems, designing systems that are not only performant but secure, traceable, and auditable.
“Enterprise AI must be explainable and robust,” Rath says. “It’s not just what the model predicts, but why and whether you can trace it back for regulators, auditors, or customers.”
By creating AI workflows that record model lineage and explain decisions, Rath is bridging the gap between innovation and compliance.
A Call for Responsible AI Leadership
The potential of AI in retail and finance is clear: more intelligent decision-making, quicker implementation, and more targeted customer experiences. Yet the dangers particularly in such critical areas as payments, fraud, pricing, and customer eligibility call for prudent governance.
Execs such as Walia, Rubin, and Rath show us that responsible AI isn’t a technical goal, it’s a strategic necessity. With regulatory pressure mounting and customer expectations changing, the winners will be the ones that develop AI systems that aren’t just powerful, but principled.
For more like this on Forbes, check out What Is Agentic AI And What Will It Mean For Financial Services? and AI’s Growing Role In Financial Security And Fraud Prevention.