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Databricks Lakehouse for Financial Services Differentiates with an Open Source, Multi-Cloud Platform Hosting Use-Case Accelerators

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Following the release of Databricks’ Lakehouse for Financial Services, Data Management Insight caught up with Junta Nakai, financial services global industry lead, to discuss the details of the open source, multi-cloud platform that includes use-case accelerators, real-time analytics, and AI, and is designed to help financial institutions modernise and innovate their approach to data. The Lakehouse also works with TickSmith’s Software-as-a-Service (SaaS) platform that provides data monetisation solutions built on the platform.

“For financial services institutions around the world looking to modernise and innovate, the two most important assets are no longer capital and scale, but data and people,” says Nakai. “The Databricks Lakehouse for Financial Services brings these two resources together on a secure, collaborative and open source-based data platform that allows financial services institutions to leverage data across clouds and drive innovation with AI.”

Databricks was founded by the creators of Apache Spark, Delta Lake and MLflow, hence its commitment to open source solutions and the platform’s integration with Legend, a Fintech Open Source Foundation (FINOS) financial sector project creating an open ecosystem based on common standards for financial data.

Platform capabilities

Key inclusions in the Lakehouse for Financial Services are use-case accelerators, partner solutions, tools for real-time data sharing with financial data providers, real-time analytics and AI capabilities.

“Lakehouse for Financial Services provides the banking, insurance and capital markets sectors with the ability to get started on high value use cases. Databricks has built 14 use-case accelerators specifically for FSIs that enable customers to deploy big data analytics and AI quickly across use cases such as risk management, ESG investing and hyper personalisation. We have also created open source libraries that address common big data challenges found in FSI such as fraud prevention using geospatial data.”

The company’s partner solutions include Deloitte’s FinServ Governed Data Platform, which is  powered by the Databricks platform and offers a cloud-based, curated data platform that builds a single source of truth that allows firms to intelligently organise data domains and approved provisioning points, and enable business intelligence, visualisation, predictive analytics, AI and machine learning (ML), natural language processing (NLP), and robotic process automation (RPA). Other partners and customers include Avanade, FINOS, Gemini, Nasdaq and TD Bank.

Real-time data sharing is supported by Databricks’ Delta Sharing that provides standardised, real-time data sharing with financial data providers such as Nasdaq, FactSet and ICE, making it easier for users to consume, share and monetise data through the Lakehouse platform.

On the platform’s AI capabilities, Nakai says: “The average FSI leverages less than 10 ML models in production compared to thousands used by retail and media companies. Lakehouse for Financial Services enables FSIs to do more with AI. For example, by taking advantage of MLFlow coupled with Delta Lake time travel capability, a next generation of model risk management for independent validation can be built.”


Building on these capabilities, Nakai says Lakehouse for Financial Services differentiates from other cloud-based data and AI solutions by ensuring the data stays in open formats and open standards. He comments: “Our FSI customers are in control of their own data. This is a departure from how other vendors that sell cloud data warehouses operate. Lakehouse for Financial Services is open, while cloud data warehouse vendors take data and put it in their own proprietary formats.”

Looking forward, he concludes: “All this means an exciting future ahead for the platform and its customers, and we’re excited to continue supporting FSIs in pursuing innovation, getting a firm grip on their data and AI, and embracing multi-cloud.”

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