An Intelligent Deep Reinforcement Learning Framework for Dynamic Risk Assessment in FinTech Platforms
Abstract
FinTech platforms must have dynamic risk assessment to analyze and react to the financial behavior and market responses that are changing at a very rapid speed. This paper presents a Deep Reinforcement Learning framework based on the Deep Deterministic Policy Gradient algorithm to improve real-time risk assessment and decision-making. Conventional risk assessment approaches typically rely on fixed formulations or deterministic models, which are inflexible and ineffective for high-dimensional, continuous data. To mitigate these shortcomings, the proposed framework formulates risk assessment as sequential decision-making procedure, in which the agent learns optimal policies through trial and error in a financial data environment and maximizes long-term riskreduction returns. The model then creates a new policy continuously, based on emerging data on market conditions and user behavior, enabling it to score risks dynamically and in a personalized manner. The suggested approach is implemented in the credit risk assessment and portfolio management case of a simulated FinTech platform. It has been shown that the model outperforms traditional models, achieving higher prediction accuracy$(98.7 \%)$, adapting to new trends more quickly (99.5 %), and delivering better long-term performance (97.2 %). This demonstrates its success in developing smart and responsive FinTech risk management systems.