The Trustworthiness of AI Algorithms and the Simulator Bias in Trading

The application of AI technology is changing dramatically investment decisions in the financial and banking industry. Neural networks (NN) are a special type of machine learning algorithm employed in training trading robots. They might be associated with advanced analysis of the specific software simulators used fundamentally in algorithm training and testing to alleviate risk in the trading activities. Our research focuses on a couple of key aspects: a methodical literature review using Natural Language Processing (NLP) tools, to delve into major themes directing to the efforts of understanding of the role of algorithms and NN in trading and investment banking. We discovered that these technologies play a major role in reducing risk and effectively taking up the mission of forecasting market fluctuations and evolving shortly in automatic trading strategies. The paper examines the possibility of harnessing simulation tools utilised in the capital investments markets for practicing and examining algorithms as well as methods for reducing biases and enhancing decision-making process. The discoveries have revealed that NN rules can be efficient in attaining patterns in historical data while forecasting stock prices precisely. In terms of large applicability, this research emphasises the requirement for countering emotional and cognitive behaviours that may impact trading results, and it exposes the most effective types of NN for designing trading algorithms. An algorithmic framework for improving biases innated in a financial banking trading activities is recommended, to improve impartiality, risk management, and trading execution.