AI-Powered Fraud Detection Systems: These systems can be developed to analyze numerous transactions in real-time to identify patterns and behaviors that indicate fraudulent activity. They can be more effective than traditional methods, which often rely on pre-set rules.

Automated Financial Advisors (Robo-Advisors): AI can be used to create automated financial advisors that provide investment advice or portfolio management online with minimal human intervention. They can analyze market trends, consider personal financial goals, and suggest investment strategies.

Chatbots for Customer Service: AI-driven chatbots can provide round-the-clock assistance to customers, answering queries about account balance, transaction history, financial products, and more.

Predictive Analytics for Loan Approval: AI can be used to develop systems that analyze vast amounts of financial and non-financial data to predict credit risk and assist in loan approval decisions.

Algorithmic Trading Systems: These systems use AI to analyze market data, predict price movements, and execute trades at optimal times, often faster and more accurately than human traders.

AI-Powered Financial Planning Tools: These tools can analyze a user's income, expenses, financial goals, and risk tolerance to provide personalized financial planning and budgeting advice.

RegTech Solutions: AI can streamline regulatory compliance in the financial industry, automating the process of monitoring regulatory changes, managing risks, and ensuring compliance.

Sentiment Analysis for Market Predictions: AI can analyze news articles, social media posts, and other public sentiment data to predict market movements and inform trading decisions.

AI in Insurance Underwriting: AI systems can be built to analyze vast amounts of data, assess risk, and determine insurance premiums more accurately and efficiently than traditional methods.

Blockchain and AI Integration: AI can enhance blockchain systems used in finance, such as by improving data management in distributed ledgers, enhancing smart contracts, or assisting in blockchain network management.

As with all applications of AI, those in finance need to carefully manage challenges such as data privacy, algorithmic bias, and the need for explainability and transparency in AI decisions, particularly given the heavily regulated nature of the financial industry.