Leading Real-Time Data Engineering Innovation in Finance

The financial data is flowing at a faster rate than before. Millions of transactions, customer interactions, and risk alerts paint a constantly changing picture every moment. The ability to convert this flood of information into trustworthy and understandable insights to enable teams to act with power and precision at any moment and under any circumstance is the real test. As a replacement to slow batch processing, real-time data engineering has become the new gold standard, with pipelines running data in real-time, allowing financial organizations to be confident and responsive in their actions.

Pavan Kumar Mantha is a driving force behind this shift. With hands-on experience evolving from batch ETL to real-time streaming, he has pioneered pipelines that harness Kafka and Spark Structured Streaming to transform raw data into reliable intelligence. A revolution was the improvement of the data processing of the Interactive Voice Response (IVR) system. The new streaming system consumes less than 5% of cluster resources (down from 75% in the previous heavy resource-hungry batch process). This enables call-center agents and fraud teams to read instant, precise context which significantly increases the speed of service and risk detection.

His work doesn’t stop there. By capturing the digital customer behaviour by viewing near-real-time usage statistics streaming and powering the customer journey analytics updated after every three hours, the expert has allowed teams to customize the interaction more precisely. Critical to this success is the shift from full data refreshes to change-data-capture techniques, updating over 100 million customer accounts daily with minimal resource use. Additionally, real-time ingestion of point-of-sale authorization data helps detect fraud faster, protecting customers and the institution proactively.

Beyond the immediate analytical use cases, his work has strengthened the consistency and observability foundations required for large-scale real-time systems. He introduced streamlined governance practices that ensure each team maintains clear visibility into the quality and flow of their own streaming data assets. By establishing reliable, replay-ready event logs, he improved the trust, traceability, and recoverability of critical data pipelines. This approach not only enhanced system resiliency but also allowed customer-servicing, compliance, and fraud-monitoring platforms to scale more easily. His broader strategy aligns with the industry’s shift toward highly dependable, self-maintaining real-time data infrastructures capable of supporting modern regulatory and decision-centric workloads

Behind the scenes, he builds these pipelines with layered controls. Deduplication, data lineage, and audit capabilities are embedded to ensure downstream users receive clean, trustworthy data. This attention to quality and speed also implies that the insights that are offered by the fraud investigators, compliance officers, and service agents can be counted on at all times without doubting the accuracy. The outcomes are measurable: quicker fraud detection, lower operational expenses and a more streamlined customer experience.

Reflecting on his journey, Pavan added, “Real-time data isn’t just about speed, it’s about trust. Delivering fast insights means little if teams can’t be confident in the data’s accuracy.” His 2023 paper on real-time data streaming in financial services offers practical lessons and emerging trends to help others replicate this balance of speed and reliability in complex, regulated environments.

In the future, the specialist envisions a time when microservices driven by events and streaming pipelines with AI integration become the new normal. Such innovations will reduce latency further and enable financial institutions to act in real-time in response to evolving customer requirements and regulatory demands and provide highly personalized, adaptable experiences on a large scale.

At a broader level, the transition to real-time data ecosystems is redefining how enterprises approach reliability, scalability, and governance. By embedding observability, automated quality checks, and domain-driven ownership models into streaming architectures, organizations can ensure that real-time insights remain both trusted and actionable. This approach allows institutions to react instantly to customer needs, market fluctuations, and compliance alerts with precision. As financial systems evolve, such architectures, grounded in transparency, replayable data flows, and self-monitoring pipelines, are becoming foundational to next-generation analytics and decision intelligence across industries