The financial sector that’s where he’s expected the greatest impact of Generative AI and where the use cases are of greater maturity. The push of AI in the financial sector alone – according to the results of the research “AI 4 Financial Services: Impacts and use cases of Generative Artificial Intelligence for the Italian financial world”, carried out by The European House – Ambrosetti in collaboration with Microsoft Italy – can take up to 23 billion in value added for Italy equivalent to 1.3% of Italian GDP. Currently 100% of financial institutions declare that they want to use or already have Generative AI projects in the implementation or testing phase and the financial sector is one of the first movers in the race for artificial intelligence both in terms of maturity of use cases and for potential economic impacts.
Generative AI use cases in the financial sector they are transversal to all company processes. The effects of this technology range from making internal processes more efficient, to engaging customers and allowing the development of new business models. However, to realize the full potential of generative AI, challenges and risks related to integration, training, compliance, reliability and ethics must be addressed. The financial sector is, in fact, leading the race for generative AI, but must continue to innovate and experiment with new use cases.
How is AI used in finance?
The key areas where AI is commonly applied in the financial sector, according to the classification made by IBM – are:
Algorithmic trading – AI can be used to develop trading algorithms that can analyze market trends and historical data to make decisions and execute trades faster than humans.
Automation and efficiency – AI can automate repetitive and time-consuming tasks, allowing financial institutions to process large amounts of data more quickly and accurately.
Competitive advantage – AI can help financial institutions foster innovation and stay at the forefront of technology, which can give them a competitive advantage.
Compliance – AI can automate monitoring and reporting requirements to ensure regulatory compliance
Credit scoring – AI can analyze a variety of data, including social media activity and other online behaviors, to evaluate customers’ creditworthiness and make more accurate credit decisions.
Cost reduction – By automating tasks, financial institutions can reduce manual work, streamline workflows and improve operational efficiency, thereby reducing costs.
Customer service – By answering questions and completing routine tasks 24/7, AI-powered personal assistants and chatbots can reduce the need for human intervention, provide personalized customer service such as real-time credit approval and offer consumers greater fraud protection and improved cybersecurity.
Data analysis – AI can analyze massive amounts of data and extract insights and trends that would be difficult for human data scientists to detect, thus enabling more informed decision-making and a deeper understanding of market behavior.
Fraud Detection – AI algorithms can prevent financial crimes, such as fraud and cyberattacks, by identifying unusual patterns in financial transactions. This helps improve the security of activities such as online banking and credit card transactions.
Loan Processing – Thanks to AI, it is possible to better predict and evaluate loan risks and simplify the process and approvals for applicants by automating activities such as risk assessment, credit scoring and document verification.
Personal finances – AI tools can help people manage their personal finances by analyzing goals, spending patterns and risk tolerance to develop budgeting advice and savings strategies.
Portfolio Management – AI can analyze market conditions and economic indicators to help investors make better decisions and optimize their portfolios.
Predictive analytics — AI can enable predictive modeling, which can help financial organizations anticipate market trends, potential risks and customer behavior.
Risk Management – AI can analyze data to help financial organizations assess and manage risks more effectively and create a safer and more stable financial environment.
Sentiment analysis – AI can analyze news sources, social media and other information to gauge market sentiment, which can help predict market trends and influence decision-making.
Use cases
Various types of financial institutions leverage AI to improve efficiency, decision making, and user experience. Some examples of AI in the financial sector include:
Customer service – Conversational AI and natural language processing (NLP) power chatbots that allow banking customers to access account information quickly and efficiently, 24/7.
Prevention of cyber attacks – AI can use data science to analyze patterns and trends and alert companies to unusual activity.
Financial planning – Robo-advisors use sophisticated algorithms to provide personalized, cost-effective investment advice based on clients’ goals, risk tolerance and market conditions.
Fraud detection and prevention – Deep learning can be used to analyze customer purchasing behavior and trigger an alert when unusual spending patterns are identified.
Loan eligibility – Lenders are managing risk by deploying AI neural networks to rapidly analyze data to determine customers’ creditworthiness.
Trading – Investment firms use AI for algorithmic trading, which is where trades are made at high speed based on real-time data and market trends.
