Financial services are finally entering the intelligence age. Across the board, artificial intelligence is being used to address a wide range of challenges. Those rapid advances in the field are causing massive change. After all, more than half of Fortune 500 companies have gone out of business since 2000 – and AI is poised to take the disruption to the next level.

However, research suggests that if financial institutions invest enough in AI, they can expect potential savings of between 20-25% across IT operations. The key is to invest early and continuously, and to constantly innovate. AI can actually impact several key areas in finance, such as:

Fraud detection

While previous financial fraud detection systems depended heavily on complex set of rules, using machine learning systems can detect unusual activity and flag them for security teams. AI can identify location, transaction anomalies, verify customer place of business and flag sensitive cross border movement.

Portfolio management

Algorithms known as ‘robo-advisors’ can be built to fine-tune a financial portfolio to a user’s goals and relative risk tolerance. These robo-advisors are particularly useful in attracting millennial consumers, who are much more at ease investing without a human advisor.

Insurance underwriting

Especially at insurance firms, machine learning algorithms can be trained on millions of examples of consumer data. For instance, an insurance company can use AI to collect information like a patient’s repeat hospital stays to inform possible care plans or targeted intervention, or potential problems with the claim. In this scenario, a claim representative would already have all the comprehensive information at hand.

Regulatory compliance

AI can remember and comply with all applicable laws such as anti-money laundering regulations. The benefit is removing human error from compliance. Natural language processing can be used to analyze legal documents, and thus provide a more comprehensive overview affected parties, processes and regulations.

Trading

Trading generates large quantities of data that typically requires machine learning tools to work effectively. Algorithmic systems often make millions of trades a day, hence the term “high-frequency trading’. Some hedge funds use AI partially, with managers maintaining control over risk management, while others outsource both the trading and risk management, with the manager playing a minimal role.

Customer service

Banks can use natural language processing in chatbots to improve customer service.. For example, the start up ‘Cleo’ provides an AI-powered chatbot based on machine learning that assess financial status, spending, etc.

Voice recognition

Similarly, a bank can provide biometric voice recognition to verify customer identities on the phone. The technology recognizes a customer’s unique ‘voice print’, which helps reduce fraud by cutting traditional security questions or pin codes.

Credit applications

AI can help lower the cost of assessing credit risks for individuals, and increase the number of individuals for whom firms can measure credit risk. Also, people without a credit history may be able to get a loan or a credit card due to AI. Over the past several years, many FinTech companies have started targeting customers not traditionally served by banks, particularly in overseas markets like China. Instead, big data can be used to assess non-credit bill payments like cell phones or utilities to create an accurate picture of a potential customer.

Stock predictions

AI can evaluate companies’ public remarks, such as on earnings calls using sentiment analysis on things like terms usage, or tone. It could then be compared with historical data to predict stock performance.

Sentiment indicators

This refers to sentiment analysis of investors. Similar to stock predictions, ‘sentiment indicators’ of investors can be sold to banks, hedge funds, high-frequency trading traders, and or really, any party interested in understanding key investors.

Stress testing

The increased use of stress testing following the financial crisis has posed challenges for banks as they work to analyze large amounts of data for regulatory stress tests. AI and machine learning tools can help their capital markets business for bank stress testing. The tools developed aim to limit the number of variables used in scenario analysis for the loss given default and probability of default models.

Bank-end help

Banks can use AI to analyze customer comments left at contact centers, automation of help desks, and data on social media. This constant information is actually extremely useful for any company to retain, particularly in tasks that take humans much longer to complete.