In today’s fast-paced digital world, the banking industry relies heavily on robust and secure APIs to deliver seamless services to customers. FastAPI, a modern web framework for building APIs with Python, has gained significant popularity due to its exceptional performance, scalability, and ease of development. In this blog post, we will explore the importance of FastAPI for developing banking APIs and how it can empower financial institutions to deliver efficient and secure services to their customers also discuss the implementation of automated test cases using the BDD framework.

Unmatched Performance: FastAPI is built on top of Starlette, a high-performance asynchronous framework. It leverages Python’s asynchronous capabilities to handle multiple requests concurrently, resulting in blazing-fast response times. For banking APIs that require quick response times, FastAPI ensures that transactions, queries, and account information can be retrieved swiftly, providing customers with an excellent user experience.
Type Safety and Documentation: FastAPI’s strong typing system, powered by Pydantic, allows developers to define clear data models and request/response schemas. This type of safety ensures that the data passed to and from the API is accurate and consistent. Additionally, FastAPI generates interactive and automatically documented APIs based on the defined models, making it easier for developers and other stakeholders to understand and consume the API.
Security and Authentication: Banking APIs handle sensitive customer data, and security is of utmost importance. FastAPI provides built-in security features such as OAuth2 authentication, token validation, and request validation, enabling developers to implement robust security measures to protect customer information. Furthermore, FastAPI seamlessly integrates with other security frameworks and tools, allowing the implementation of various authentication and authorization mechanisms, including two-factor authentication and encryption, to meet the stringent security requirements of the banking industry.
Scalability and Extensibility: FastAPI’s asynchronous architecture enables horizontal scaling, allowing banking APIs to handle a large volume of concurrent requests. Financial institutions can easily scale their API infrastructure based on user demand without sacrificing performance. Additionally, FastAPI’s modular design and compatibility with other Python libraries provide developers with the flexibility to extend functionality by integrating with existing banking systems, databases, or third-party services.
Automated Testing and Debugging: FastAPI encourages and facilitates automated testing with tools like pytest and pytest-bdd. These testing frameworks enable developers to write comprehensive tests, ensuring the correctness and stability of the API. FastAPI’s integration with the Swagger UI and ReDoc documentation tools further simplifies testing and debugging by providing an interactive interface to explore and validate API endpoints.

Here’s an example of a parameterized FastAPI code that creates a banking REST API to connect to a SQL Server database, extract account summary and user details, and return the JSON response. The parameter values are passed using a separate configuration file. Let’s go step by step.

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