Biography
Mukesh Kumar Mishra is an IEEE Senior Member and technology professional with over 18 years of experience in Software Quality Engineering, Data Validation, Cloud Computing, and Enterprise Technology Solutions.He has contributed to technology initiatives for leading organizations, including Bank of America, Moody’s, Travelers Insurance, Truist, Ahold Delhaize, and Partners HealthCare, with a focus on data integrity, regulatory compliance, enterprise modernization, and quality engineering.His technical expertise encompasses Artificial Intelligence and Machine Learning, Microsoft Azure, AWS, Azure Databricks, Delta Lake, SQL, and enterprise data validation frameworks. His professional interests include trustworthy AI, data governance, anti-money laundering systems, and cloud-based financial technologies.Mukesh has authored research on data quality engineering, AML transaction monitoring, and scalable data lineage and auditability. He has also participated in technical peer-review activities and served as a judge for technology innovation competitions, including NexusHacks 2026.He is committed to advancing reliable, responsible, and scalable intelligent systems.
Abstract
Title: Engineering Trustworthy AI: Data Quality, Explainability, and Governance in Cloud-Based Financial Systems
Abstract: As artificial intelligence (AI) and machine learning (ML) become increasingly integrated into financial services, ensuring the reliability, transparency, and regulatory compliance of AI-driven systems has become a critical engineering challenge. While AI offers significant opportunities to improve fraud detection, anti-money laundering (AML) monitoring, risk assessment, and operational efficiency, its effectiveness depends heavily on the quality, integrity, and governance of the underlying data.
This presentation explores an engineering framework for building trustworthy and scalable AI systems through the integration of Data Quality Engineering, Cloud Computing, and AI Governance. The proposed approach emphasizes five fundamental dimensions of data quality: completeness, accuracy, consistency, timeliness, and traceability.
Drawing on practical experience in enterprise technology and financial services, the presentation examines how cloud-native platforms, including Microsoft Azure, Databricks, and Delta Lake, can support automated data validation, end-to-end lineage tracking, anomaly detection, and audit-ready data pipelines. Particular attention is given to the application of these engineering principles in AML transaction monitoring, where unreliable data can contribute to false positives, missed suspicious activities, and regulatory exposure.
The presentation also discusses the importance of explainability, metadata governance, continuous monitoring, and human oversight in strengthening AI system accountability.
By connecting foundational data engineering practices with responsible AI principles, this session offers practical insights into designing intelligent systems that are scalable, transparent, resilient, and suitable for highly regulated environments.
