Within the sphere of finance, Artificial intelligence (AI) has found applications in several areas including fraud and abnormal transactions detection, trade activity assessment and risk mitigation. This form of technology is now widely considered as one of the pillars of any organization. In the finance sector, AI systems are actively used to learn the normal behavior of a customer, his transaction activity, and study the big data of the financial market. The deployment of AI algorithms makes it possible for businesses to avert losses through fraud and many more in the course of performing their activities. Several of these algorithms, including Convolutional Neural Networks (CNN) are used to facilitate this process of pattern discovery in financial datasets made up of both structured and unstructured forms. More specifically, AI can assist in monitoring excessive transactions and other forms that do not conform to normal patterns. Deep reinforcement learning is yet another AI technique that is used to make trade executions automatically interact with the relevant market data and respond to changes in conditions. AI-based risk management systems support automatic hazard evaluation activities, leading to increased effectiveness in risk management. In this paper, the various types of AI algorithms used in a financial organization, like chatbots in customer service, data-driven insights, predictive analysis, and several use cases, are presented. By employing AI technologies, financial organizations can improve their operational efficiency, manage risks, and make choices in a rapidly transforming financial environment.