Mr. Rajkumar Sekar

Securing the Future: Designing AI-Driven Financial Applications with Privacy, Compliance & Trust at Scale

Abstract:

As artificial intelligence and big data continue to transform the financial services landscape, securing data applications has become essential. Financial institutions face growing threats from cyberattacks, data breaches, insider risks, and adversarial machine learning, making trust, privacy, and compliance central to technology design. This session explores how to build secure, AI-driven financial applications that protect sensitive data, meet regulatory obligations, and foster customer confidence. It addresses challenges such as data poisoning, model manipulation, and regulatory complexity common hurdles for organizations adopting AI in production environments. The discussion outlines best practices including end-to-end encryption, homomorphic encryption for secure data processing, and strong access control systems like multi-factor authentication and role-based access. It will also introduce privacy-preserving AI techniques such as differential privacy and federated learning, which help ensure compliance with data privacy laws like GDPR and MiFID II. Real-time AI-driven fraud detection will be covered, with examples of how machine learning models can reduce financial crime and improve risk response. Looking forward, the session will explore the potential of blockchain to provide tamper-proof data governance and the importance of quantum-safe encryption in preparing for future threats. Attendees will gain practical strategies for designing secure-by-design, compliant-by-default AI applications that can scale with confidence across the financial sector, aligning innovation with trust and long-term resilience.

Profile:

Rajkumar Sekar is a seasoned data engineering leader with over 20 years of experience in architecting and scaling data platforms across financial, risk, and cloud-native domains. Currently serving as Senior Manager of Data Engineering at BILL, Rajkumar drives innovation in real-time data processing, fraud detection, and cloud data lake modernization.

At BILL, Rajkumar spearheads the development of streaming architectures using Kafka, Flink, and Kinesis, enabling high-throughput, low-latency pipelines critical for risk intelligence and revenue optimization. He also architected self-service transformation frameworks with Spark on AWS EMR, modern lakehouse solutions with Apache Iceberg, and real-time analytics systems that deliver actionable insights to business users.

Previously at SVB Financial Group, he designed a multi-account AWS Lakehouse and implemented robust data governance frameworks to ensure GDPR and CCPA compliance. His work led to an estimated $10M annual cost savings through cloud-native cost optimization strategies using Redshift and Glue. At Bank of America, he built enterprise data lakes and hybrid event-driven systems supporting critical banking operations and M&A initiatives.

Rajkumar’s technical expertise spans AWS cloud services, PySpark, DBT, Informatica, and streaming analytics platforms. He is recognized for his ability to blend strategic platform thinking with hands-on engineering excellence. His projects consistently balance performance, governance, and scalability, enabling real-time decision-making and self-serve analytics across complex data ecosystems.

Rajkumar holds a Bachelor of Engineering from Anna University, India, and has a passion for mentoring engineers and sharing insights on cloud-native data platforms, AI-driven streaming, and edge-to-cloud architectures at industry conferences.