Data-driven innovation finds its place in another industry. That of Financial Services Institutions (FSIs). Even though it sounds quite uncommon at first, this is a novelty we’ll get used to pretty quickly. Actually, there is a vast amount of data within these institutions, which makes this only a little surprising. If it wasn’t for the vendor lock-in and complex legacy architectures that impede AI and data from becoming essential business factors, we’d have seen such developments earlier.
Understanding the need for a prompt solution, Databricks has recently launched Databricks Lakehouse for Financial Services, a modern data platform well-suited for capital markets, banking, and insurance sectors to serve various customer use cases. Customers can easily benefit from solutions that address their specific business and technical requirements.
“For Financial Service Institutions around the world looking to modernize and innovate, the two most important assets are no longer its capital or sheer scale, but its data and its people,” said Junta Nakai, RVP, financial services global industry leader at Databricks. He added that Lakehouse for Financial Services unifies these two core elements in a single data platform that allows a seamless flow of data throughout the cloud securely.
Introducing Lakehouse for Financial Services
Lakehouse for Financial Services focuses on bridging the connection between people and data with the help of its versatile capabilities such as open standards and certified implementation partners, data model frameworks, open standards, and pre-built solutions accelerators. Lakehouse tackles some essential issues such as:
- Vendor lock-in risk. FSIs are constantly impeded by a limited number of technologies and data formats. Lakehouse allows teams to utilize the tools they prefer since it relies on open standards and open-source.
- Multi-cloud. Partnering up with Lakehouse is compatible and provides support for popular cloud vendors allowing a multi-cloud infrastructure and preventing systemic risks.
- Access to Real-time data for BI. Lakehouse surpasses all the obstacles raised from traditional architectures by facilitating the access of data analysts and data teams to recent, real-time data.
- Support for diverse data sets. Critical use-cases usually include an ample amount of unstructured data sets such as text and images. Lakehouse removes the data sets limits that occur in traditional data warehouses by working with unstructured, structured, and semi-structured data sets, and allowing advanced data sharing.
- Use cases including AI. Implementing AI in financial ecosystems is challenging due to the regulations, the legacy processes, and the siloed infrastructures. Lakehouse provides AI transparency using MLflow and other tools and has even implemented AI for model risk management to cope with risks that arise from model errors.
What Makes Lakehouse for Financial Services Efficient in Tackling These Challenges?
Lakehouse for Financial Services was built to allow seamless navigation of data within the organization and improved functionality of financial services with innovative and moderated risk management solutions suitable even in highly-regulated environments. Here are six features of Lakehouse that assist in this:
Pre-built Solution Accelerators for Financial Services Use Cases
Databricks offers 14 financial services solution accelerators that combine with Lakehouse to successfully tackle sophisticated but common use cases. Six common use cases that we could distinguish are:
- Transaction Enrichment. Retail banking can prevent fraudulent actions and develop a more accurate customer segmentation by implementing a geospatial data library and hyper-personalization.
- Market Surveillance and Post-Trade Analysis. Asset managers can produce transactional cost analyses and backtest their investing tactics and strategies by using a library that brings together market data and separate data sources.
- Regulatory Reporting. This Lakehouse accelerator follows the respective open sharing protocols and open data standards to streamline the flow of regulatory data.
- GDPR Compliance. Compliance is made easier by dealing with the technical aspects more simply and maintaining strict audit capabilities.
- Common Data Models. Organizations can seamlessly standardize data by using certain accelerators and framework sets.
Industry Open Source Projects
Databricks recently became part of FINOS (FinTech Open Source Foundation) which involves other worldwide FSIs such as JP Morgan and Goldman Sachs. This intends to expand the cooperation and development in the field of financial services, and offer a simplified and secure transmission of financial data in the banking systems.
Part of Databricks’s goal to standardize data by facilitating accessibility and providing advanced insights is also the integration of Data Lake functionalities with the LEGEND ecosystem. The latter allows financial analysts to consider financial calculations in relation to data and build efficient data pipelines without extra costs.
Facilitated Deployment of the Lakehouse Environment
The security standards can be easily automated by customers while using Lakehouse for Financial Services. The scripts and utility libraries developed specifically for financial services to automate notebooks’ setup and tackle other issues, including governance and security issues.
A Data Model Framework For Standardizing Data
Organizations face different hurdles when standardizing data. Besides incorporating accelerators, organizations can use a framework for common data models provided by Lakehouse. For instance, Lakehouse integrates with Financial Regulation (FIRE) with the help of one solution accelerator, which aids the data standardization process and allows the distribution of data to downstream tools.
Open Data Sharing
Databricks launched Delta Sharing in 2021 as an open protocol that assisted in the secure process of data exchange among organizations in real-time without being limited to the sharing capabilities of the platforms containing the data. For instance, Ticksmith was an early adopter of Delta Sharing which now allows FSIs to handle data products in a single environment from inception to delivery.
Implementation Partners
Avanade is also another company with which Databricks is cooperating to be able to ship risk management solutions to FSIs. The joint solution produced by this partnership is built on Azure Databricks and allows customers to put data into value-at-risk (VaR) models in a manageable manner.
Databricks has further deepened its efforts to provide enhanced data regulation and management solutions by partnering up with Deloitte FinServ Governed Data Platform, which focuses on creating one source of truth for financial institutions while involving predictive analysis, NLP, business intelligence, AI/ML and visualization.
Final Thoughts
Lakehouse for Financial Services aims to innovate the way financial institutions handle data and execute their processes. Including a wide array of solution accelerators, data model frameworks, and implementation partners, Databricks promises a smooth sailing towards standardization of data and implementation of AI.
Blue Orange Digital can help you discover further the data solutions that exist for your organization during a 15-minutes free consultation call. Book here.
