Detect fraud to curb revenue losses.
Financial firms lose a significant amount of revenue due to fraud and theft each year. To combat this, these organizations are increasingly turning to cloud databases that can quickly detect anomalies between data and transactions at scale, allowing them to identify fraud as it occurs or even before it happens.
We’ve listed the products and solutions that commonly address the Fraud Detection use case below.
AWS provides diverse cloud services suitable for various data workloads.
Google offers serverless database services for flexible resource usage.
Couchbase Capella is a multi-modal NoSQL database focusing on non-relational databases.
MongoDB Atlas is a document-based database with a unified interface for CRUD operations.
Redis is an in-memory database specialized for real-time and transactional use cases.
Cloudera provides services for organizing, analyzing, and centralizing data.
Azure offers managed services with integrated data management and security.
FaunaDB is a NoSQL database that combines the best of relational and NoSQL architectures.
Snowflake supports analytics, data lakes, and data warehouses with automated management features.
MariaDB is a versatile fork of MySQL, supporting transactional, analytical, and hybrid workloads.
Supabase is a PostgreSQL relational database with real-time REST API and serverless functions.
Teradata specializes in data warehousing and analytical use cases with advanced analytics.
Oracle offers autonomous databases with advanced tuning and management.
PlanetScale offers a MySQL-compatible, serverless database with customizable features.
MarkLogic is a NoSQL database for transactional use cases.
Databricks provides an open-source lakehouse storage and SQL capabilities for data lakes.
CockroachDB is a cloud-native, distributed SQL database with auto-sharding for handling unpredictability.
Customize these feature priorities in Taloflow and get expert ratings for 15 different vendors and solutions, including None.
| Feature | Dimensions | Description | Priority |
|---|---|---|---|
| Activity Monitoring |
|
Monitor user activity and alerts when unusual behavior is detected. | Critical |
| Alerting |
|
Give timely wanings so that the problems can get resolved quickly. | Critical |
| Data Encryption |
|
Secure data with encryption in transit or when stored in the cloud. | Critical |
| Data Masking |
|
Selectively obscure column data from users while still allowing access to the column. | Critical |
| Group-Level Security |
|
Group users by requirements and enforce security mechanisms for them. | Critical |
| Identity Management |
|
Natively provides identity management or integrates with identity management tools for authentication and access control. | Critical |
| Key Management |
|
Natively provides key management or integrates with key management tools for creating, managing and controlling cryptographic keys. | Critical |
| Monitoring |
|
Monitor and alert on potential issues, including performance issues. | Critical |
| Operational Reporting |
|
Provides reports on day-to-day activities on the DBMS. | Critical |
| RBAC |
|
Authorize and restrict access to specific database functions based on the user's role within the organization. | Critical |
| Transport Layer Security |
|
Enables transport layer security between database servers and client applications. | Critical |
| User Authentication |
|
Verify the identity of users attempting to access database instances. | Critical |
| Clustering |
|
Increases processing power by connecting multiple database instances. | Important |
| Data Mining |
|
Extend SQL or stored procedures for data mining purposes. | Important |
| Graph Database |
|
Store multi-relationship data coming from graph databases. | Nice To Have |
| Graph Language Support |
|
Offers graph language support to query your data. | Nice To Have |
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