Technology
Navigating Data and AI Challenges in Payments: Expert Analysis by Himanshu Shah
Global Banking and Finance Review
Company
This article is based on industry commentary and analysis provided by Himanshu Shah.
This article is based on industry commentary and analysis provided by Himanshu Shah.
The global payments industry is experiencing unprecedented growth with trillions of transactions processed annually through complex digital channels. At the same time, organizations face rising risks from fraud, fragmented data architectures, regulatory scrutiny and growing expectations to extract real time intelligence from data. A recent report indicates that a majority of enterprises struggle with data readiness, preventing them from fully leveraging machine learning to optimize revenue, detect fraud and improve customer experience.
As organizations grapple with rising transaction volumes and growing challenges in data governance, we were exploring how industry leaders are responding. During our research, we received insights from cloud data and analytics expert Himanshu Shah, who explained how organizations can build trusted, scalable data platforms for AI driven decision making.
Data Infrastructure Challenges and Industry Trends
According to Shah, many organizations continue to rely on pipeline-centric data delivery models, treating datasets as downstream artifacts rather than as strategic products.This approach often results in fragmented systems, delayed insights, and higher operational risk.
“Organizations that treat data as a product, with clear service levels, versioning, and enforceable contracts, can accelerate decision-making while ensuring reliability and governance,” Shah explains. “It is this mindset shift that allows data to power critical operations such as fraud detection, revenue optimization, and customer engagement.”
Industry surveys support this perspective, highlighting that data silos, inconsistent governance, and poor quality are major blockers to AI adoption. Experts note that embedding governance and quality at the point of data creation, rather than retrofitting controls downstream, improves both regulatory compliance and business outcomes.
Insights on Cloud Data Platforms
Shah emphasizes the importance of modern, cloud-native architectures for organizations handling high-volume transactions. By leveraging technologies such as Snowflake, AWS, Kafka, DBT, Databricks and Tableau, companies can process growing volumes of data in near real-time while maintaining security, auditability, and resilience.
“Cloud platforms that integrate streaming pipelines and automation-first analytics stacks enable organizations to achieve both operational efficiency and commercial insight,” Shah notes. “When data flows reliably, it becomes a foundation for everything from customer insights to fraud prevention to business trends to automation of all critical internal business processes”
He points out that these platforms are essential for organizations looking to create AI-ready data environments. Standardized feature pipelines, stable metadata, and contract-driven interfaces allow machine learning models to operate safely in high-stakes areas such as payments, fraud prevention, and revenue assurance.
From Expertise to Measurable Outcomes
Shah highlights examples from across the financial services and payments industries where robust data foundations have produced tangible results. Payment companies that adopt domain-oriented, productized data assets report improvements in authorization rates, smoother settlement operations, pricing optimization, better customer servicing and marketing effectiveness.
“Embedding trusted, high-quality data into operational processes not only drives efficiency but also provides commercial teams with actionable insights and reduces risk,” Shah says. “It helps anticipate churn, surface upsell opportunities and protect revenue while maintaining strong customer relationships.”
Governance and quality, according to Shah, should be built into automated pipelines with mechanisms such as anomaly detection, latency issues, reconciliation checks, and fine-grained access controls. This ensures that analytics and decision-making are based on data that meets both regulatory and business standards.
Scaling for Global Operations and Future AI Capabilities
Experts like Shah underscore the need to balance scale with discipline. Large, distributed data engineering teams benefit from structured practices in architecture, automation, and performance optimization. Doing so reduces operational complexity and cost while increasing the platform’s ability to support high-volume, high-value transactions.
Looking forward, Shah stresses that organizations must prepare their data for AI responsibly. “It is not enough to experiment with machine learning. Data must be machine-consumable, explainable, and governed from the start,” he says. Early initiatives include near-real-time data availability, stable feature definitions, and clear lineage, laying the groundwork for AI-driven innovation in payments and finance.
Building Expertise and Industry Leadership
Shah brings a unique blend of strategic and hands-on experience to these challenges; drawing from his background in advising technology start-ups and leading enterprise-scale risk, analytics, and data transformation programs at global financial institutions. He combines strategic vision with hands-on expertise, business domain knowledge, emphasizing operational excellence, stakeholder collaboration, and practical implementation of advanced data solutions.
According to Shah, the combination of robust cloud architectures, product-oriented data management, and governance by design is what allows organizations to turn data into a true strategic asset, one that supports revenue growth, risk management, and AI-driven innovation.
Disclaimer: Outcomes described in this article reflect reported experiences and industry examples shared by the contributor. Actual results may vary depending on organization, implementation, and market conditions.
Data governance refers to the management of data availability, usability, integrity, and security in an organization. It ensures that data is accurate, consistent, and used responsibly.
Machine learning is a subset of artificial intelligence that enables systems to learn from data and improve their performance over time without being explicitly programmed.
A cloud data platform is a cloud-based service that allows organizations to store, manage, and analyze data efficiently, providing scalability and flexibility for data operations.
Fraud detection involves identifying and preventing fraudulent activities in financial transactions. It uses various techniques, including data analysis and machine learning, to spot anomalies.
Real-time intelligence refers to the ability to analyze and act on data as it is generated, allowing organizations to make timely decisions based on current information.
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