#AIImpactOnForex Data quality issues in Forex AI development pose a significant challenge to building reliable and profitable trading strategies. The accuracy, completeness, and consistency of historical and real-time Forex data are critical for training effective machine learning models. Noise, errors, missing values, and inconsistencies in the data can lead to flawed model training and poor predictive performance. Furthermore, the representativeness of the data is crucial. If the historical data used to train the AI does not accurately reflect current market conditions or includes biases, the resulting trading strategy may not generalize well to live trading. Ensuring data integrity through rigorous cleaning, validation, and preprocessing techniques is a fundamental step in developing robust AI-driven Forex trading systems. The adage "garbage in, garbage out" holds particularly true in the context of AI applied to financial markets.