Machine Learning Enhanced API Rate Limiting and Throttling Strategies in High Volume Fintech Systems

Kwame Ashantay Ajua

Machine Learning Enhanced API Rate Limiting and Throttling Strategies in High Volume Fintech Systems

Authors

Kwame Ashantay Ajua

API Security Engineer , Ghana

Keywords:

API Gateway, Rate Limiting, Throttling, Fintech, Machine Learning, Anomaly Detection, Traffic Forecasting

Synopsis

As fintech platforms scale rapidly, Application Programming Interfaces (APIs) become performance bottlenecks and security risks. Traditional static rate limiting and throttling mechanisms often fail to adapt to real-time spikes or anomalous traffic. This paper explores how machine learning (ML) can dynamically optimize rate-limiting strategies to improve availability, performance, and security in high-volume fintech environments. We present a comparative framework, showcase real-world use cases, and analyze current research and implementation challenges.

References

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Published

April 25, 2022

Series

Research paper

License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.