Category: Financial Services

In this post, we explore how Razorpay, India’s leading FinTech company, transformed their data platform by migrating from a third-party solution to Amazon EMR, unlocking improved performance and significant cost savings. We’ll walk through the architectural decisions that guided this migration, the implementation strategy, and the measurable benefits Razorpay achieved.

Swiss Life Germany, a leading provider of customized pension products with over 100 years of experience, recently transitioned from legacy on-premises infrastructure to a modern cloud architecture. To enable secure data sharing and cross-departmental collaboration in this regulated environment, they implemented Amazon SageMaker with a custom Terraform pattern. This post demonstrates how Swiss Life Germany aligned SageMaker’s agility with their rigorous infrastructure as code standards, providing a blueprint for platform engineers and data architects in highly regulated enterprises.

Verisk, a catastrophe modeling SaaS provider serving insurance and reinsurance companies worldwide, cut processing time from hours to minutes-level aggregations while reducing storage costs by implementing a lakehouse architecture with Amazon Redshift and Apache Iceberg. If you’re managing billions of catastrophe modeling records across hurricanes, earthquakes, and wildfires, this approach eliminates the traditional compute-versus-cost trade-off by separating storage from processing power. In this post, we examine Verisk’s lakehouse implementation, focusing on four architectural decisions that delivered measurable improvements.

In this post, we’re excited to announce the release of four Amazon SageMaker Unified Studio publicly available workshops that are specific to each FSI segment: insurance, banking, capital markets, and payments. These workshops can help you learn how to deploy Amazon SageMaker Unified Studio effectively for business use cases.

In this post, we demonstrate how to use Amazon Bedrock Flows in SageMaker Unified Studio to build a sophisticated generative AI application for financial analysis and investment decision-making.

In this blog post we shall cover how understanding real-time payout performance, identifying customer behavior patterns across regions, and optimizing internal operations required more than traditional business intelligence and analytics tools. And how since implementing Amazon Redshift and Zero-ETL, MassPay has seen 90% reduction in data availability latency, payments data available for analytics 1.5x faster, leading to 45% reduction in time-to-insight and 37% fewer support tickets related to transaction visibility and payment inquiries.

In our previous post Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg, we showed how to use Apache Iceberg in the context of strategy backtesting. In this post, we focus on data management implementation options such as accessing data directly in Amazon Simple Storage Service (Amazon S3), using popular data formats like Parquet, or using open table formats like Iceberg. Our experiments are based on real-world historical full order book data, provided by our partner CryptoStruct, and compare the trade-offs between these choices, focusing on performance, cost, and quant developer productivity.

In this post, we highlight key lessons learned while helping a global financial services provider migrate their Apache Hadoop clusters to AWS and best practices that helped reduce their Amazon EMR, Amazon Elastic Compute Cloud (Amazon EC2), and Amazon Simple Storage Service (Amazon S3) costs by over 30% per month.

Capital Fund Management (CFM) is an alternative investment management company based in Paris with staff in New York City and London. CFM takes a scientific approach to finance, using quantitative and systematic techniques to develop the best investment strategies. In this post, we share how we built a well-governed and scalable data engineering platform using Amazon EMR for financial features generation.

This post is written in collaboration with Clarisa Tavolieri, Austin Rappeport and Samantha Gignac from Zurich Insurance Group. The growth in volume and number of logging sources has been increasing exponentially over the last few years, and will continue to increase in the coming years. As a result, customers across all industries are facing multiple […]