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Abstract
The integration of artificial intelligence (AI), particularly self-learning algorithms, into financial operations has transformed how investment banks manage the vast influx of structured and unstructured data. Structured data, such as transaction logs and market prices, and unstructured data, including emails, news feeds, and social media, present distinct challenges in processing and analysis. This paper explores how self-learning algorithms, including reinforcement learning and deep learning models, contribute to more efficient decision-making, risk assessment, fraud detection, and regulatory compliance within investment banks. A 2023-centric analysis is provided, with a specific focus on how these technologies are leveraged to meet evolving data challenges. We present recent implementations, assess previous literature, and suggest future directions for maximizing operational intelligence through self-adaptive systems.
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