Data is the financial services industry's breath. It enables banks, insurance companies, fintech startups, and other players to provide better products and services, improve customer experiences, comply with regulations, and gain a competitive edge to overcome competition and prepare for economic turbulence. However, data also comes with challenges, such as high costs, security risks and privacy concerns. One of the main challenges facing the financial services industry is how to deal with data silos. Data silos are isolated pools of data that are stored and processed separately by different departments, business units or organizations. Data silos can hinder data sharing, collaboration and innovation, as well as increase data duplication, inconsistency and complexity.
Traditionally, the solution to data silos has been to centralize data in a single repository or server, such as a data warehouse or a data lake. However, this approach also has drawbacks, such as:
- High costs: Centralizing data requires significant investments in infrastructure, storage, processing and maintenance. Sensitive data requires additional backups in different locations, which can be costly.
- Security risks: Centralizing data creates a single point of failure and a tempting target for hackers and cybercriminals.
- Privacy concerns: Centralizing data may violate data protection laws or regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which restrict the collection, processing and transfer of personal data across borders or jurisdictions.
- Data scalability and performance: As business grows, data velocity grows. Centralizing data lacks on flexibility to handle increasing amounts of data, which usually compromises the quality or accuracy of the results.
- Data silos and fragmentation: When dealing with multiple data sources that are stored in different locations, formats, and systems, centralizing data makes it difficult to access, integrate, and analyze your data in a timely and consistent manner. Additional problems include complex ETL processes to reduce data duplication, inconsistency, and incompleteness.
To overcome these challenges, a new approach is emerging: federated data processing and data mesh architectures. Federated data processing is a technique that enables the extraction of insights from multiple isolated datasets without needing to share or move that data into a central repository or server. Federated data processing leverages distributed computing, data mesh and machine learning to perform data analysis across different locations while preserving data privacy and security -- we call this "Virtual Data Lakehouse". A Virtual Data Lakehouse is a layer on top of existing data architectures, like data lakes, delta lakes, databases, filestores like S3, or files like Excel, and unifies all those data sources into one combined data platform.
The VIrtual (federated) Data Lakehouse has several benefits for the financial services industry, such as:
- Reduced data costs: Federated data processing eliminates the need to store and process large amounts of data centrally, which can save up to 35% of data costs.
- Enhanced data privacy: Federated data processing ensures that personal or sensitive data remains within its original location and is not exposed to third parties or unauthorized access.
- Increased data quality: Federated data processing enables data owners to maintain control and ownership of their data and ensure its accuracy, completeness and timeliness.
- Improved data innovation: Federated data processing allows data owners to collaborate and share insights across different domains and organizations without compromising their data sovereignty or competitiveness.
To illustrate how Blossom Sky works in practice, we showcase two real-world use case stories from the financial services industry.
Use Case Story: Virtual and Federated Data Lakehouse for Credit Scoring
Credit scoring is a process that evaluates the creditworthiness of a borrower based on their financial history, behavior and characteristics. Credit scoring is used by lenders, such as banks or fintech companies, to determine whether to approve a loan application, what interest rate to charge and what credit limit to offer.
Credit scoring relies on large amounts of data from various sources, such as credit bureaus, bank statements, social media profiles, online transactions and more. However, these sources are often fragmented and siloed across different organizations and jurisdictions. This makes it difficult for lenders to access and analyze all the relevant data for credit scoring purposes. Moreover, some of these sources may contain personal or sensitive information that cannot be shared or transferred due to legal or ethical reasons. For example, credit bureaus may not be allowed to share their credit reports with foreign lenders due to GDPR regulations. Similarly, social media platforms may not be willing to share their user data with third parties due to privacy policies.
Federated Datalakes can help solve this problem by enabling lenders to perform credit scoring across multiple datasets without moving or sharing the underlying data. For example:
- A lender can use federated learning, a type of federated data processing that applies machine learning models across distributed datasets, to train a credit scoring model using data from multiple credit bureaus without accessing their individual credit reports.
- A lender can use federated analytics, another type of federated data processing that performs statistical analysis across distributed datasets, to calculate aggregate metrics such as average income, debt ratio or default rate using data from multiple bank statements without accessing their individual transactions.
- A lender can use federated querying, yet another type of federated data processing that executes queries across distributed datasets, to retrieve relevant information such as education level, occupation or social network size using data from multiple social media platforms without accessing their individual profiles.
Blossom Sky's Virtual Data Lakehouse is using federated data processing techniques and a data mesh architecture to help lenders improve their credit scoring accuracy and efficiency while reducing their data costs and enhancing their data privacy.
Use Case Story: Virtual and Federated Data Lakehouse for Trading
Federated data processing is a technique that allows multiple parties to collaborate on data analysis without sharing their raw data. This can help to preserve privacy, security and compliance while enabling data-driven insights and innovation. We talk about a common use case of how Blossom Sky helps to improve a trading platform in the financial services industry. Imagine a global trading platform that connects various market participants in multiple geographic regions, such as brokers, dealers, exchanges and clearing houses. The platform allows them to execute trades, manage orders and settle transactions. However, the platform faces some challenges in terms of data quality, latency and scalability. For example:
- The order information from different sources may be inconsistent, incomplete or outdated, leading to errors or delays in order matching and execution.
- The transaction information from different venues may be fragmented, noisy or inaccurate, affecting the market visibility and liquidity.
- The custodian information from different banks may be confidential, sensitive or regulated, limiting the data sharing and integration.
To address these challenges, the trading platform can leverage federated data processing and data mesh technology to enable cross-party data collaboration without compromising data privacy or sovereignty, this includes:
- The trading information can be processed in a federated manner using federated learning (FL), which allows multiple parties to train a shared machine learning model on their local data without transferring them. This way, the platform can aggregate and analyze the transaction information from different venues without collecting their raw data.
- The order information can be processed in a federated manner using secure multi-party computation (SMPC), which allows multiple parties to jointly compute a function on their inputs without revealing them. This way, the platform can validate and reconcile the order information from different sources without accessing their raw data.
- The custodian information can be processed in a federated manner using homomorphic encryption (HE), which allows one party to perform computations on encrypted data without decrypting them. This way, the platform can integrate and query the custodian information from different banks without exposing their raw data.
By implementing Blossom Sky as a Virtual Data Lakehouse, the companies trading platform can improve its performance, functionality and competitiveness in the financial services industry. It can also create new opportunities for data monetization, innovation and collaboration among different market participants.
Data is essential for the financial services industry to provide better products and services, improve customer experience, comply with regulations and gain a competitive edge. However, dealing with data silos is a major challenge. Centralizing data has been the traditional solution but it has drawbacks such as high costs, security risks and privacy concerns. Federated data processing offers an alternative solution that can reduce data costs and enhance data privacy and performance in financial services. This is an important consideration for institutions in the financial industry to excel.
Blossom Sky stands for federated data lake technology, data collaboration, increased efficiency, and helping to create new insights by breaking data silos in a unified manner through a single system view. The platform is designed to adapt to a wide variety of AI algorithms and models. Blossom Sky integrates with all major data processing and streaming frameworks like Databricks, Snowflake, Cloudera, Hadoop, Teradata, Oracle, Apache Flink as well as AI systems like Tensorflow, Pandas, PyTorch.
Want to learn more? Please get in touch with us via databloom.ai/contact or write us directly: [email protected]