About
Arpita Agarwal is a Staff Solutions Engineer at Confluent with a strong experience in enterprise software and startup environments, all of which have been acquired. She specializes in architecting and developing AI/ML algorithms, building scalable data pipelines, and delivering enterprise-grade products. Her expertise lies primarily in real-time event streaming at scale, leveraging high-performance platforms such as Apache Kafka. In her current role, Arpita partners with customers across the ANZ region to design and implement event-streaming platforms, utilizing Confluent's cutting-edge technologies to drive business impact.
Sessions
Real-Time Financial Crime Detection: Architecting Streaming AML (anti-money laundering) Pipelines for Instant Insights
Financial crime including money laundering, fraud, and terrorism financing is a significant and rapidly evolving threat that undermines global financial institutions and economies. Traditional Anti-Money Laundering (AML) systems are reliant on batch processing and struggle to keep pace with the increasing velocity of transactions, such as real-time payments and cryptocurrency exchanges, resulting in delayed detection and increased risk exposure. This paper will explore the architecture and implementation of streaming-first Anti-Money Laundering (AML) pipelines that leverage event-driven frameworks, real-time data enrichment, graph-based anomaly detection, and AI-driven automation to detect suspicious activity instantaneously. Attendees will gain insights into how cutting-edge technologies like Apache Kafka, Apache Flink, Graph databases, and Agentic AI can be integrated to create scalable, low-latency AML solutions. I will be talking about the following: Why traditional AML tools no longer keep up with evolving financial crimes and challenges faced by regulators in catch up to the changing world. The limitations of traditional batch-based AML approaches. Modern architecture of streaming AML pipelines, transaction ingestion, transaction enrichment with KYC and risk data, real-time analytics, and automated decision-making. Real-world use cases like transaction monitoring, crypto AML and risk scoring. Challenges to watch for, like privacy concerns and integrating with old systems. This session is ideal for fintech architects, data engineers, compliance officers, and financial crime analysts interested in using the power of real-time streaming data and AI to build next-generation AML capabilities that detect financial crime as it happens.