The exponential growth of financial data, driven by digitization, regulatory mandates, and market complexity, has outpaced the capabilities of traditional data management infrastructures. In response, financial institutions are increasingly adopting cloud-native data lake architectures to meet the demands of advanced financial modelling and compliance analytics. These architectures combine the scalability and flexibility of cloud computing with the agility of data lake designs, enabling seamless ingestion, storage, and processing of diverse financial datasets—including structured, semi-structured, and unstructured data. This explores the core components and strategic advantages of cloud-native data lakes in the context of modern financial operations. Key technologies include scalable object storage systems (e.g., Amazon S3, Azure Data Lake), distributed processing engines (e.g., Apache Spark, Flink), and real-time ingestion tools (e.g., Kafka, AWS Kinesis). When integrated with AI/ML toolchains and robust metadata management frameworks, these ecosystems support complex use cases such as credit risk assessment, portfolio optimization, regulatory stress testing, and anti-money laundering (AML) analytics. A major benefit of cloud-native data lakes is their support for schema-on-read and lakehouse paradigms, which enhance data agility and analytical flexibility without the rigidity of traditional ETL pipelines. In compliance contexts, they facilitate real-time monitoring, audit trails, and regulatory reporting by enabling unified access control, encryption, and data lineage tracking. However, the shift to cloud-native platforms is not without challenges. Data quality assurance, governance, and interoperability across cloud providers remain significant concerns. Nonetheless, when designed with architectural best practices and regulatory alignment, cloud-native data lakes empower financial institutions with a future-proof platform for data-driven innovation, operational resilience, and compliance adherence. This provides a conceptual and technical foundation for understanding and implementing cloud-native data lake architectures tailored to the evolving needs of the financial services sector.