In today’s digital banking environment, the customer experience is critical for the success of commercial banks. Rather than using one-size-fits-all solutions for creating products, banks that focus on personalization and tailoring unique experiences for their customers are positioned to surpass the competition.Â
By leveraging cloud-based data platforms such as Snowflake Data Cloud, these commercial banks can aggregate and curate their data to understand individual customer preferences and offer relevant and personalized products. With its ability to cater to a large variety of workloads, which include AI/ML, data warehousing, data lake, and data engineering, Snowflake also enables banks to go beyond personalization and tackle additional use cases such as financial forecasting, risk management, and more.
In this blog, we will introduce the capabilities of Snowflake, explain why and how it provides commercial banks the competitive edge to go above and beyond for their customers and business, and finally, discuss the next steps you can take to start the journey.Â
Understanding Snowflake
Snowflake is a cloud-based data platform that organizations can use to simplify their data architectures and eliminate data silos. There are four architectural layers to Snowflake’s platform:
Optimized Storage – organizations can bring their unstructured, semi-structured, and structured data.
Elastic Multi-Cluster Compute – virtual warehouses can be started, stopped, and resized at any time to accommodate dynamic workloads.
Cloud Services – Snowflake provides self-managed or automated services, which include security, metadata management, query optimizations, etc., so that organizations can focus on delivering value rather than be burdened by operational complexities.
Snowgrid – Snowflake’s cross-cloud technology layer enables organizations to connect their business ecosystems, which may be across different regions and clouds.
Snowflake uses a hybrid architecture of both the traditional shared-disk and shared-nothing database architecture. Like the shared disk architecture, Snowflake has a central repository for optimized storage, which is accessible to all compute nodes. These compute nodes, the virtual warehouses mentioned above, process the queries using massively parallel processing (MPP).Â
With its flexible architecture and rich set of features, organizations can address their data management and analytics needs while offloading the operational overhead of infrastructure management to Snowflake.
Why Use Snowflake for Commercial Banks?
With today’s increasing demand for personalized services, Snowflake can provide commercial banks with a scalable, secure, and cost-effective solution for managing data and extracting insights to create tailor-made products and services. Furthermore, its AI/ML capabilities also enable these banks to accomplish other use cases detailed below in addition to personalizing the customer experience:
Customer 360
Unified Customer View – by eliminating silos and centralizing customer data from across different systems, especially in larger organizations, Snowflake can be used to create a single view of a customer’s interactions and transactions with the bank.
Personalized Marketing – Commercial banks can analyze individual customer behavior using data from Snowflake to create personalized marketing campaigns. For example, customized loan offers can be sent to customers based on a customer’s financial goals and their current portfolio.Â
Customer Lifecycle Management – banks can leverage this to predict customer needs and proactively engage with customers, which can lead to an increase in retention.
Risk Management and Compliance
AML and KYC Compliance – Failing to meet AML and KYC compliance requirements can result in large fines, up to tens of millions or more. Commercial banks can prevent this by centralizing customer, account, and transaction data to be analyzed and monitored for AML/KYC compliance.
Fraud Detection – Snowflake’s AI/ML capabilities can be used to detect fraud in real-time and act accordingly.
Basel III Capital Compliance – Snowflake can be used to store and analyze loan, asset, and risk data sources to monitor the bank’s capital and ensure compliance with Basel III.
Financial Forecasting and Planning
Balance Sheet and Profitability Modeling – for this use case, Snowflake can replace clunky spreadsheets and be used for robust modeling. Its advanced analytics capabilities and integrations with 3rd party risk analysis tools can help banks model different scenarios and provide insights for strategy.
Credit Risk Optimization and Planning – similar to the point above, commercial banks can transition from traditional credit risk methods to a more sophisticated approach to managing credit risk.
Next Steps
As you explore the capabilities of Snowflake for commercial banks, try the following steps:
Dive deep into the Snowflake documentation and check out the following phData blogs on monetizing financial data on Snowflake and implementing a product recommendation system on Snowflake.
Set up a 30-day Snowflake trial account and get hands-on experience by experimenting with loading and querying data and trying out workloads with Snowpark.
Explore the ecosystem around Snowflake as many data, service, and solution providers seamlessly integrate with the platform.
Consult with phData, Snowflake’s Partner of the Year for the past four consecutive years, for expert guidance, implementation, and best practices. Our experience in deploying modern data solutions across diverse industries brings valuable insights and support to help you incorporate Snowflake into your technology stack.
