Financial services organizations are facing greater financial and regulatory risks for failing to detect fraud or meet “know your customer” (KYC) and anti-money laundering (AML) compliance. They’re facing steep penalties—in 2021 alone the financial sector faced $5.4 billion in fines for non-compliance with KYC, AML, and data privacy regulations. Much of the problem lies in fragmented technology systems that cause data to be siloed in lines of business, geographies, and transaction and lending systems, as well as workflow applications. These outdated systems prevent financial services companies from being able to track and be alerted to fraudsters. According to PwC, organizations need to protect their perimeter by “consolidating data from disparate, disconnected systems into a centralized platform that can track the end-to-end life cycle of users (fraudsters or not) and generate meaningful alerts.”
To address financial crime, risk and compliance teams are leveraging Snowflake’s multi-cluster, shared data architecture to ingest all of their data into a single platform with near-infinite scalability. With Snowflake, financial services organizations can consolidate their data and technology silos to perform advanced analytics and enhanced due diligence, and achieve near real-time fraud and anomaly detection. In addition, Snowflake’s security and governance controls enable teams to access and analyze critical customer and account information in a compliant manner. In addition to features and capabilities such as Dynamic Data Masking and end-to-end encryption for data in transit and at rest, Snowflake also offers Global Data Clean Room functionality, which enable data sharing, double-blind joins, and restricted queries that allow different organizations to share and match customer data without having to expose any underlying data.
Here’s how a few of our financial services customers are using Snowflake to protect their data and organization from incidents of financial crime:
Block (formerly Square) partnered with Snowflake to build fraud detection capabilities that help its customers avoid risk. Block designs and builds tools that empower sellers to start, run, and grow their businesses. To help promote merchant success and reduce risk, Block ingests and analyzes large amounts of structured and unstructured data. Snowflake’s multi-cluster, shared data architecture scales to virtually eliminate resource contention at Block, allowing teams to perform correlation analyses and identify difficult-to-find fraud that may be occurring. Snowflake’s affordable cloud rates also make it possible for Block to store more than 1 petabyte of data. And when it comes to ensuring security and data governance, Block is able to stay compliant by leveraging Snowflake’s Object Tagging, Dynamic Data Masking, and Row Access Policies features.
Billie is among the fastest-growing financial services startups globally, providing German companies with flexible B2B “buy now, pay later” transactions. Fraud is an unavoidable consequence of offering financial services, but Billie is fighting back. With Snowflake housing all of its data, the company has a holistic view of users and can now better identify potentially fraudulent transactions. Billie has saved $3.3 million in potential fraud losses using Snowflake’s fraud prevention mechanism. Snowflake has even helped Billie secure a $100 million investment by demonstrating proactive fraud prevention improvements over time. Using Streams and Tasks, in combination with Snowflake’s data analytics and reporting capabilities, Billie can now set up rules and alerts and incrementally update specific lines of data, all without aggregating an entire data set.
Kount’s Identity Trust Platform analyzes signals from 32 billion interactions per year to prevent fraud and enable personalized customer experiences. To power machine learning (ML) models that help more than 9,000 brands protect their customer journeys, Kount collects and analyzes large amounts of data for account creation, login information, and payment transactions. Kount’s AI relies on massive amounts of near real-time and historical data to determine a customer’s trust score, which is defined within 200 milliseconds of an attempted transaction. Realizing the need for a modern data infrastructure, Kount turned to Snowflake Kount’s Data on Demand solution, which is built on Snowflake, provides access to transaction data that empowers customers to perform in-depth analysis, generate personalized reporting, and create customized ML models. Using Snowflake secure data sharing technology, Kount provides this solution through Snowflake Marketplace to simplify the discovery and combining of data for richer insights.
Discover how your organization can use Snowflake to mitigate financial crime incidents:
- Learn more about the Snowflake Financial Services Data Cloud
- Read our Financial Services Success Guide: 8 Ways Financial Services Organizations Deliver Innovation and Security with the Data Cloud
- Read our blog post: All Your Snowflake Data Clean Room Questions Answered
- Watch our webinar: Best Practices for Data Sharing: More Effective Media Through Secure Collaboration
- Read our industry brief: Snowflake’s Financial Services Data Cloud