Risk Analytics

Executive Summary This article explores the development of a scalable, high-performance risk analytics engine for financial institutions, leveraging Apache Beam and Google Cloud Dataflow. The project addressed the need for efficient, portable, and robust risk management solutions in a rapidly evolving regulatory and market environment, resulting in a modernized architecture that supports both batch and streaming analytics at scale. Business Context and Drivers Financial institutions face increasing regulatory scrutiny, market volatility, and the need for real-time risk assessment. Traditional risk engines, while functional, often struggle to scale and adapt to new requirements. The move to a cloud-native, distributed architecture was driven by the need for agility, cost efficiency, and the ability to process large volumes of data with low latency. ...

June 23, 2020 · 6 min

Starting a cloud journey

Executive Summary This case study details the journey of a leading investment bank as it embarked on establishing a secure, scalable, and adaptable Google Cloud Platform (GCP) foundation. The project addressed business drivers such as regulatory compliance, operational agility, and cost optimization, resulting in a robust cloud environment that empowered diverse business applications and fostered a culture of innovation and collaboration. Business Context and Drivers The bank faced increasing demands for agility, scalability, and security in its IT operations. Legacy infrastructure limited the ability to rapidly deploy new services and respond to market changes. Regulatory requirements and the need for robust data protection further motivated the move to a modern cloud platform. After evaluating several providers, GCP was selected for its advanced security features, data analytics capabilities, and strong support for hybrid cloud architectures. ...

June 23, 2020 · 5 min

Startup Quant Fund

Executive Summary This case study describes the journey of a quant hedge fund startup in enhancing stock price prediction accuracy through big data analytics, machine learning, and cloud infrastructure optimization. The project tackled challenges in data quality, model performance, and operational efficiency, resulting in faster, more reliable trading insights and significant team skill development. Business Context and Drivers In the fast-paced world of quantitative finance, the ability to generate accurate trading signals and execute models efficiently is a key competitive advantage. The fund faced increasing data volumes, market complexity, and the need for rapid iteration on trading strategies. Cloud adoption was driven by the need for scalable compute, cost control, and faster time-to-insight. ...

June 23, 2020 · 5 min