In the fast-paced world of global payments, organizations are under constant pressure to manage massive transaction volumes, ensure regulatory compliance, and protect against fraud and operational risks. As enterprises adopt cloud platforms and AI to accelerate decision-making, new challenges emerge: scattered data, governance gaps, and evolving cyber threats. According to industry reports, AI adoption is growing rapidly, yet many organizations still struggle with trust, security, and data quality, slowing the realization of real business impact.
To gain deeper insight into these issues, we spoke with Himanshu Shah, a seasoned technology leader with over 25 years of experience driving enterprise data innovation, AI integration, and strategic transformation. Himanshu has built cloud data platforms for payment companies and global banks, enabling operational efficiency, revenue growth, and fraud mitigation. In this interview, he shares his perspectives on industry risks, innovative solutions, and the roadmap for 2026.
1. What are the most pressing risks and challenges in global payments today, and how are companies adapting to fraud and cyber threats?
Himanshu: Fraud and cyberattacks are more sophisticated than ever, targeting both financial institutions and customers. High-volume transactions, cross-border payments, and third-party integrations increase exposure. Organizations are adapting by implementing real-time monitoring, AI-powered anomaly detection, and proactive risk scoring. They are also moving toward unified data platforms, which provide a single source of truth for fraud analysis and compliance checks, helping teams respond faster to threats. At the same time, AI and cloud adoption introduce new operational vulnerabilities such as model bias, misconfiguration, and supply chain risks. Embedding governance, auditing, and security by design helps mitigate these challenges without slowing innovation.
2. You often emphasize treating data as a product rather than a by-product. How does this approach improve reliability and mitigate risks?
Himanshu: Treating data as a product ensures that every dataset has clear ownership, defined service levels, and built-in quality controls. This reduces inconsistencies and prevents errors from propagating through analytics or AI systems. When data is reliable and well-documented, teams can trust insights for decision-making, whether it's pricing optimization, fraud detection, or regulatory reporting. This approach also streamlines adoption across multiple departments, accelerating business impact and reducing operational risks.
3. How do you design AI-driven systems that prevent fraud while remaining scalable and real-time?
Himanshu: Scalability and real-time response require a cloud-native architecture that integrates streaming pipelines, change-data-capture, and automation-first analytics. We use technologies like Kafka, DBT, and Databricks to process data continuously, combined with AI models that detect anomalous patterns in milliseconds. Each AI component is monitored for performance and accuracy, ensuring reliable detection without slowing down high-volume transaction processing. These systems help enterprises maintain operational resilience and prevent potential losses from fraud.
4. Can you share specific examples where your data platforms reduced risks, improved operational efficiency, or delivered measurable business results?
Himanshu: We built a platform that unified data from authorization, settlement & reconciliation. This provided near real-time insights into unusual patterns, enabling faster fraud detection and improved operational decision-making. In practice, it significantly reduced false positives while improving the speed of true fraud detection. In another instance, we developed a RevOps platform that consolidated sales, pricing, and payment operations into a single analytics layer, helping improve revenue protection. These examples show how disciplined data and operational design can directly drive both business outcomes and risk mitigation.
5. How do you balance innovation, security, and compliance when building global cloud data platforms?
Himanshu: The key is embedding security and governance into the platform from the start, not as an afterthought. Automated access controls, data masking, latency & anomaly detection and continuous monitoring allow teams to innovate safely. CI/CD pipelines include checks for compliance and quality, so every deployment meets regulatory standards. Strong governance ensures that AI, analytics, and operational systems operate on decision-ready data while maintaining agility for innovation.
6. What strategies ensure that data quality and governance keep pace with high-volume, fast-moving payments?
Himanshu: Automation is critical. We have Observability tools to implement validation rules at every stage of data ingestion, reconciliation checks that give real-time alerts for anomalies, latency, and any job failures. Data lineage and business catalogues provide full visibility into data usage and transformations. This proactive approach ensures that downstream AI and analytics systems operate on reliable data. High-quality data reduces errors and ensures trust & regulatory compliance in global operations.
7. What skills and organizational practices are essential for teams managing data and AI in high-risk environments like payments?
Himanshu: Teams need a combination of technical expertise, business acumen, and risk awareness. Engineers must understand cloud architecture, streaming data, quality checks, and AI, while business teams need to translate insights into actionable decisions. Collaboration, cross-training, and a culture of accountability are essential. Continuous learning, mentoring, and strategic leadership are critical to building resilient, high-performing global teams capable of managing both innovation and risk.
8. What emerging threats, technologies, or industry trends do you expect to dominate the payments and cloud data landscape in 2026?
Himanshu: Looking ahead, AI-driven fraud detection, automated threat monitoring, and advanced analytics will continue to evolve. Regulatory requirements around payments and AI use will become stricter. Hybrid cloud architectures, real-time analytics, and machine-readable governance frameworks will become standard. Companies that fail to implement scalable, trusted data platforms may struggle with both speed and competition. The future will reward organizations that integrate AI safely while maintaining transparency, reliability, and operational resilience.
9. How are you preparing your organization's data and AI platforms for the challenges and opportunities in 2026?
Himanshu: We are focusing on AI-readiness, feature pipelines for machine learning, and richer metadata to make data explainable and machine-consumable. Automation continues to improve pipeline reliability and operational efficiency. Our goal is to enable faster experimentation while maintaining strict governance, ensuring that the platform scales safely and delivers measurable business value in 2026 and beyond. We are also investing in cross-team collaboration, skills development, and cloud optimization strategies to stay ahead of emerging threats and evolving market demands.
10. Looking back, which innovations or initiatives are you most proud of, and what lessons can other leaders take from your experience?
Himanshu: I am most proud of building platforms that not only improved operational efficiency but also created tangible business outcomes of revenue uplift and risk reduction. The lesson for leaders is simple: focus on solving real business problems, invest in governance and quality, and build platforms that empower teams across the organization. Trustworthy, well-designed data is the key to scaling AI safely and effectively. Innovation paired with discipline and a clear focus on business impact drives lasting results.
Conclusion: Securing Data and Driving Growth
Himanshu Shah's insights make one thing clear: data and AI are powerful tools, but they only deliver results when built on strong, secure, and governed foundations. In payments and banks, where risk, fraud, and compliance challenges are constant, enterprises must integrate technology, people, and processes to stay ahead.
Looking toward 2026, the focus will be on AI-ready platforms, real-time analytics, and proactive governance. Companies that combine innovation with risk management will unlock new opportunities, improve operational resilience, and deliver better outcomes for customers and stakeholders.
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