Financial Services Solutions

Enable your financial institution to grow, transform and succeed

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Toward Transformation

Modern cloud services enable intelligent firms to move resources away from undifferentiated, costly activities — think IT maintenance, regulatory compliance, re-implementing technology already widely deployed — and to allocate it toward transformational activities, like tapping unprecedented streams of real-world data to derive business insight quickly. Google Cloud Platform can identify trends in massive, noisy datasets from diverse sources, provide companies with secure and efficient IT consumption models, archive large volumes of sensitive data — and most promising of all, unleash the latest machine learning techniques on dynamic market events and a wide range of real-world data.

Insights, Not Infrastructure

Financial Services firms must generate timely insight for customers and continuously earn their trust. The cost and complexity involved in maintaining infrastructure — be it on-premises or servers rented remotely — is not a differentiating core competency for financial services firms and should be taken off-balance-sheet to free up resources. Leverage Google’s expertise at running world-class infrastructure, which provides firms with a superior pricing model for efficient IT spend, including world-class reliability and security. Most importantly, firms can free up resources toward adding unique value, rather than costly maintenance.

Beyond Cloud Economics

Convert CapEx to OpEx and take advantage of automatic sustained-use discounts with Cloud Platform’s pricing model and philosophy. The benefits from cloud economics are passed continuously to customers, along with the innovations made available to you just by choosing to Go Google. Cloud Platform runs on the same infrastructure relied on by Google’s own trusted services, from Search, to YouTube, to Maps.

Enterprise-Grade Security & Compliance

Financial Services companies face more regulatory scrutiny than before and the risk of breach and data loss are top of mind for decision makers. End-to-end security remains critical. Cloud Platform has undergone rigorous compliance measures, and for nearly two decades Google has practiced integrated, pervasive security to protect its own services. Considering the flurry of on-prem data center breaches debunking the myth that on-prem is automatically more secure, Google is striving to make security a key reason to adopt public cloud.

Solutions Abound

Our customers from the financial services community use Cloud Platform’s product portfolio to address a broad range of solutions across high-performance and grid computing, storage & archival, business process automation, analytics & data warehousing, and machine learning. Contact us to learn how we can work with you to transform these capabilities at your firm.

  • Compute Grid
  • Data Warehouse
  • Dev/Test Environments
  • Financial Web & Mobile Apps
  • Modeling & Simulation
  • Market Data Ingest
  • Fraud & Anomaly Detection
  • Archival, Backup & Disaster Recovery
  • Regulatory & Risk Platform
  • FinTech SaaS
  • Machine Learning
  • Business Process Automation

The Next Stage of Market Intelligence

The long-term opportunity lies in applying Google’s heritage of machine learning and analytics at web-scale to financial/market data. Cloud enables small, modest-sized teams to aggregate and run machine learning workloads on massive real-world data to do predictive analytics, and many domain-specific applications such as fraud & anomaly detection. To disseminate use of machine learning, Google has recently opened-sourced its library for machine intelligence TensorFlow and launched Cloud Machine Learning products, including several pre-trained models usable out-of-the-box such as Cloud Vision API, Cloud Speech API, and Google Cloud Translation API.


In-depth guides and resource will help you get your financial solutions running

Analyzing Financial Time Series

Run SQL-like queries on BigQuery against financial time series data and you’ll get results back very quickly.


Time Series Schema Design

Learn more about Cloud Bigtable data schema design patterns.


ML for Financial Time Series Data

This solution is accessible, non-trivial example of machine learning with financial time series using Cloud Datalab.


Monte Carlo on Dataproc & Spark

Run Monte Carlo simulations written in Java, Python, or Scala on Cloud Dataproc and Apache Spark.


FIS Case Study CAT Prototype

FIS tests a Consolidated Audit Trail (CAT) prototype on Cloud Bigtable at 10 billion financial trade records per hour.


Explore Financial Services Guides

Explore all of our tutorials and articles about Financial Services on Cloud Platform.


“Google Cloud Bigtable gives FIS the ability to scale the platform up or down based on trading volume, thus driving cost efficiencies and control at a granular level.”

— FIS The world's largest global provider of banking and payments technologies