Artificial Intelligence is making a growing impact on the finance sector in recent years. Apart from security, AI and machine learning are helping financial institutions from banks to credit unions and trading firms help clients to access, and invest their money safely, undertake risk analysis and improve operational costs. Here are some ways that AI is being used in the finance sector.
AI in Stock Trading
AI trading platforms are now being used across global stock markets. Using neural networks, machine learning and sentiment analysis combined with fundamental analysis, AI can make decisions on which stock to buy or sell.
AI-powered trading can detect market anomalies, inefficiencies and trading patterns across a massive scale across multiple markets and in real time, more effectively than manual scanning. AI algorithms can be trained to avoid high-risk margins and reduce human errors in a sector that is driven as much by emotion as it is by data.
Traders have a daunting number of stocks to pick from every day. AI can do the computation by also analyze the unstructured data that abounds in stock mentions in the news media, social media, and blogs to effectively screen the stocks. On many AI platforms, traders can select indicators they wish the system to consider as it makes decisions.
AIEQ is the first exchange-traded fund (ETF) to fully utilize artificial intelligence as a method for stock selection and actively manage portfolios. It uses IBM’s Watson, Google’s Deep Mind and EquBot’s proprietary AI algorithms to seek the best investment opportunities. The ETF’s AI can process vast troves of data per day to build predictive financial models on the companies listed on the stock exchange. It continually analyzes data and models to derive an optimal portfolio consisting of stocks with the highest opportunities for profit.
AI-powered automated trading is especially suited to highly volatile markets like cryptocurrencies. It offers a continuously learning market intelligence system that can do high-frequency trading, track price movements across crypto exchanges and analyze unstructured data across media.
AI and Bank Lending Decisions
AI can provide a more accurate and detailed assessment of a potential borrower’s ability to repay loans. It does this by using many more factors into consideration than were previously done using traditional credit score methods. This helps lenders clearly understand which applicant is credit-worthy and who is at high risk for default.
Loan application forms can be reviewed automatically via machine learning, which enables the bank officers to focus on the customer and loan process rather than spend time going through endless files of documents. A loan approval process is speeded up considerably with this and financial institutions can reduce operational costs.
AI in fraud prevention
Payment fraud has been growing in recent years with the rise of e-commerce and online transactions. ML and AI have been successfully used in scaling up fraud prevention over the past several years. In many ways, it provides the ideal ground to test and train ML algorithms. This is because each online financial transaction has a large amount of data and a customer’s buying patterns or spending habits have discernable trends. AI and ML systems can analyze the historical and real-time data on a customer’s buying habits and location to enable detection of any unusual transactions and immediately trigger an alert.
Many banks and financial institutions are now entrusting AI to handle the data and security processes in fraud prevention.
AI has revolutionized the finance sector in multiple ways – from customer service to investment, risk analysis and detecting and preventing fraud. As AI continues to work with larger data sets, it improves its accuracy and helps finance executives make better data-driven decisions.