USE CASE: FRAUD DETECTION
Harness diverse data to recognize anomalies, detect fraud, and defend against attacks.
THE CHALLENGE
Protecting against advanced cyberthreats
The threat of cyberattacks continues to grow—in terms of both volume and complexity—with businesses losing tens of billions of dollars to identify theft, fraud, and more yearly. Human oversight is no longer enough to protect against dangerous attacks. While organizations consider generative AI (GenAI) as a solution to combat fraud, they are hesitant to expose sensitive data to AI models in the cloud and are wary of the complexities and tools required to get started. Organizations today seek modern solutions that offer the flexibility to run advanced GenAI and analytics workloads on their existing data to detect fraud and enhance data security, instead of compromising it.
OUR SOLUTION
A unified platform for secure data analysis
EDB Postgres® AI is a secure, unified, hybrid platform for transactional, analytical, and AI workloads, offering a sovereign environment to observe and analyze data. Integrate transactional data with unstructured data and leverage GenAI models and rapid analytics to detect anomalies and suspicious activity, reduce fraud and potential threats in near real time, and accelerate defensive response, all while relying on EDB Postgres AI to maintain high availability and performance across deployments.
Customer-controlled infrastructure
Deploy EDB Postgres AI in your environment of choice—even on-premises—delivering sovereign AI and ensuring complete control over AI operations.
Unified data solution
Seamlessly integrate structured, semi-structured, and unstructured data in a single location. Cross-reference structured transaction records with unstructured communication data to spot inconsistencies and anomalies.
Powerful GenAI semantic search
EDB AI Accelerator comes preloaded with the open source pgvector extension that adds vector data support to reliable, familiar Postgres—and enhances it with automated GenAI data pipelines and enterprise-grade support.
Enhanced observability
Leverage single-pane-of-glass management and observability across your hybrid data estate to monitor your database and detect and react to anomalies in real time.
Detect fraud and potential threats in near real time and accelerate defensive response.
Protect customers and your data
Reduce risk of fraud for customers on your own terms—build well-governed GenAI models for fraud detection, without exposing sensitive data to the cloud.
Integrate diverse data
Integrate all data types up to 18x more cost-efficiently to build comprehensive profiles of fraudulent behavior and spot inconsistencies quickly.
Detect patterns and react in real time
Improve human response time with 30x faster analytical queries than baseline Postgres—and build GenAI detection and response functionality with secure, automatic creation and retrieval of vector embeddings from financial data.
Gain visibility
Learn where your data is coming from, where it’s going, who has access, and if and when it’s being anonymized. With enhanced observability, monitor 200+ metrics and ensure performance, security, and availability.
EDB Postgres AI enables fraud detection and prevention
Bring rapid analytics and sovereign AI models to your core, transactional data to build comprehensive profiles, spot anomalies, and detect and act against fraud. Enable continuous model training to improve fraud detection over time while EDB Postgres AI ensures performance, availability, and security of your databases.
Related Products and Solutions
EDB Postgres AI
A modern Postgres data platform for powering mission-critical workloads from edge to core.
EDB AI Accelerator
The fastest way to test and launch enterprise GenAI applications, with the powerful EDB Pipelines extension that comes preloaded with pgvector.
EDB Analytics Accelerator
Unlock rapid analytics in Postgres with the PGAA extension.
EDB Postgres AI Software Deployment
Deploy EDB Postgres AI in sovereign, self-hosted environments—in public or private clouds, multi-cloud, or on-premises.
EDB Postgres Advanced Server
Enterprise-grade, Oracle-compatible Postgres.
Resources
Intelligent Data: Unleashing AI with PostgreSQL
Security Best Practices for Postgres
Operationalizing AI for Postgres
As organizations seek modern solutions to combat fraud detection, they must also 1) consider if they are willing to expose sensitive data to AI models in the cloud, 2) manage the complexities of getting started with development of modern AI solutions, and 3) choose solutions that can reliably process diverse data types to efficiently detect recognize anomalies and suspicious activity.
EDB Postgres AI enables organizations to 1) deploy AI models in their environments of choice—even on-premises, 2) kickstart AI application development with automated AI data pipelines to enable AI applications with just 5 lines of code instead of 130+, and 3) integrate transactional data with unstructured data in a single platform.
Here are some key analytics and AI features:
- Lakehouse ecosystem integration for seamless unification of Postgres with unstructured data in object storage environments.
- Vectorized query engine for complex analytical queries across data in Postgres and object storage.
- Native vector capabilities in Postgres enabling consolidation of AI workloads in your existing Postgres database environment.
- Managed AI data pipelines that automatically handle fetching source data, creating embeddings, updating embeddings when source data changes, and retrieving embeddings for semantic search.
Yes, you can leverage EDB Postgres AI for hybrid cloud, multi-cloud, or bare metal deployments. The flexible lakehouse architecture allows for seamless incorporation of data into AI applications without requiring multiple databases or performance-degrading data pipelines.
Postgres is extensible, enabling all types of data models—SQL, vector, JSON, time-series, key-value, and more. This enables EDB to bring analytics and AI, which requires diverse data types, closer to your core transactional data. For example, analyzing transaction patterns in financial services can help identify anomalies and detect fraud when it occurs.
PostgreSQL provides a scalable and flexible platform for AI implementations, supporting multiple programming languages and advanced data types. Its extensibility enables native vector data management directly within the database, streamlining workflows. Additionally, PostgreSQL's robust performance and reliability ensure that AI applications can handle large datasets and complex queries efficiently, making it an ideal choice for businesses aiming to implement AI technologies.
PostgreSQL supports real-time data processing through advanced features such as indexing, partitioning, and query optimization. These capabilities enable the database to handle complex queries efficiently and large volumes of data, ensuring quick response times. Additionally, PostgreSQL's support for asynchronous replication allows for real-time data updates across distributed systems, making it suitable for applications that require immediate data access and analysis.