Financial services

Over the last few years, AI technology has evolved and matured to be a powerful tool for financial services and fintech companies spanning a wide range of use cases including:

• Customer potential/wallet sizing
• Relationship manager productivity
• ESG/net-zero analytics
• Retention/churn analytics
• Personalized product recommendations
• Pricing optimization
• Sales and demand forecasting
• Spend analytics
• Granular liquidity and cash forecasting

The challenge

To achieve success with AI, financial services companies capture and aggregate data from a wide range of online and offline sources. The volume of clickstream, traffic logs, database extracts, social and other data is tremendous and can easily grow to tens of petabytes, costing millions of dollars annually even when stored in "low-cost" cloud object stores.

For financial services companies, all data is hot data which must be quickly ingested, analyzed, and acted upon. Companies typically build their AI data infrastructure around Amazon S3 Standard and GCS Standard rather than archival tiers. While standard tiers are the most cost-effective for AI processing needs, the volume of data means that storage costs are still large and growing rapidly.

Financial services data also often contains sensitive content given the very nature of financial data, as well as the fact that clickstream data captures user behaviors and inputs. These data costs are consuming ever more of the AI innovation budget and crowding out investment in compute, tooling, and people.

How Granica helps

Granica increases the efficiency and utility of your AI-related data and your downstream AI pipeline stages, enabling you to free up significant money, resources, and time which you can reinvest to improve AI performance and outcomes.

Granica Crunch for financial services data

Granica Crunch reduces the storage cost associated with AI data without archival and/or deletion. Its advanced, patented ML-powered data reduction algorithms are specifically optimized for the clickstream and log data prevalent in the financial services industry.

How Crunch helps AI/ML Engineers

• Optimizes S3/GCS object storage costs, so you can allocate more resources to data quality and model performance
• Supports a wide range of data types unique to financial analytics and fraud systems
• Elastically scales up and down to support dynamic workloads

How Crunch helps AI Product Owners and FP&A

• No upfront capital outlay — Crunch doesn't cost budget, it frees up budget
• Ongoing, predictable savings enable accurate forecasting and planning

Typical data reduction rates

DRR varies based on the customer-specific data contained inside each file type. Estimate your savings to evaluate the DRR for your specific data.