Four data challenges in financial services
Transforming financial services with
“Nasdaq’s data and AI vision is powered by Databricks Lakehouse. We use it to process huge amounts of complex financial and alternative data to create data and insights for our clients. Databricks is also an important part of our efforts to modernize data delivery and consumption. It enables us to seamlessly deliver data directly into analytical workspaces, so our clients can analyze and integrate mission-critical data quickly without having to move terabytes of data around.”
Why Lakehouse for Financial Services?
Unify data and AI on an open and collaborative platform that empowers you to minimize risk, deliver superior customer experiences and accelerate innovation
Governed approach to risk management and compliance
Simplify the complexity of regulatory reporting, risk management and compliance by securely streamlining the acquisition, processing and transmission of data to empower better data governance practices.
Personalized products and services
Unify a variety of data — from market to alternative data — enabling hyper-personalized experiences that drive cross-selling opportunities, customer satisfaction and share of wallet.
Real-time insights, smarter decisions
Rapidly ingest all your data sources at scale to make better investment decisions, quickly detect new fraud patterns and bring real-time capabilities to risk management practices.
Open data sharing and data monetization
Bring together vast amounts of internal and third-party data to share innovative financial solutions, monetize new data products and deliver advanced analytics capabilities to any cloud or tool without getting locked into proprietary technologies.
Partners and solutions
Move toward open formats and the standardization of data for analytics and AI
Rapidly deploy data into value-at-risk models to keep up with emerging risks and threats.
Legacy Cards and Core Banking Portfolios Modernization
Enable rapid conversion from external source systems and achieve a fully configurable and industrialized conversion capability.
Model Risk Management
Bring a more transparent approach to model risk management through automated documentation and integrated data visualization.
Cybersecurity at scale
Rapidly detect threats, investigate the impact and reduce risks with Splunk and Databricks
Take a quantitative view into sustainability and ensure companies are accountable for their actions
Modern risk management
Adopt a more agile approach to risk management by unifying data and AI in the Lakehouse
Identify fraud with geospatial analytics and AI
Use geospatial data to better understand customer spending behaviors in terms of both who they are and how they bank
Transaction enrichment with merchant classification
Automate transaction enrichment to better understand your customers’ behaviors and drive hyper-personalization
Rule-based AI models to combat financial fraud
Modernize fraud-prevention strategies to reduce operational costs and increase customer trust
Timely and reliable transmission of regulatory reports
Combine financial services industry data models with the cloud to enable high governance standards with low development overhead
Modernizing investment data platforms
Use the full power of financial market data to focus on product delivery for customers
Anti-money laundering (AML)
Enable AI-driven use cases like fuzzy match and image analytics to combat money laundering and financial terrorism