Data Science for Finance and Financial Services
Financial services has been a big data player for longer than most industries, deploying a sophisticated mix of internal and third-party data sets (including alternative data) in everyday business decisions. The result has been early advances in applying large-scale data analytics to drive business performance. However, there is a downside. The huge volumes of first-, second-, and third-partydata has bogged down analytics platforms, resulting in frustrated data science teams. Adding to this is the inherent complexity of the industry, which between almost constant M&A activity, product and services complexity, and regulatory changes, remains in a constant state of flux.
Finance data science teams need to manage the breadth and complexity of all this data, while also functioning as silo busters in an industry where data is often held close to the vest. Far more than the average generic data or business analysts, finance data science teams require deep domain expertise and skill to successfully navigate the all the potential minefield in their way.
Data scientists in financial services work on everything from the creation of sophisticated data repositories and analytics tools -- from data lakes to data warehousing -- to sourcing valuable data augmentation sources, to building pricing and risk algorithms. On any given day, a finance data scientist is likely to work on one or more of following business-critical projects or data science use cases:
- Risk analytics
- Fraud detection
- Pricing automation
- Real-time analytics
- Customer data management
- Consumer analytics
- Personalized offers/services
- Algorithmic trading
In this environment, data scientists and data analysts in FinServ need access to the best tools, platforms, and data marketplaces to deliver actionable insights to key firm stakeholders.
The Snowflake Financial Services Data Cloud
Snowflake's Financial Services Data Cloud combines the company's governance tools, industry-specific datasets and clients' first party data.
- Governance including private connectivity for multiple public clouds, bring your own key encryption, compliance with Sarbanes-Oxley standards, Virtual Private Snowflake environments and Cloud Data Management Capabilities (CDMC) standards.
- Industry partnerships with Amazon FinSpace, BlackRock, Cognizant, Dataiku, Deloitte, EY and State Street.
- Data sets from industry partners including Acxiom, S&P Global and FactSet via the Snowflake Data Marketplace.