The financial services industry has been transformed by the introduction of artificial intelligence. Because finance involves mountains of data, the powerful computing that comprises the algorithms of artificial intelligence is the perfect tool for many routine and complex tasks within the system. Financial markets process tons of information every day, which is one reason why the financial industry has been invested in data and technology for many years, before other industries had even considered the usage of artificial intelligence.
How Artificial Intelligence Has Impacted the Asset Management Sector
During the past decade and a half, major changes based upon artificial intelligence have impacted the asset management sector of the financial industry. The mutual fund industry saw a rise in passive fund managers and a decrease of active fund managers, as technology and data has increased the competitiveness of passive investing more competitive and made active managing more difficult. As Desai notes, “in the last eight years alone, the ratio of passively-managed assets to actively managed assets has risen from 0.6 to 1.2 – a dramatic shift in market share.” Additionally, passive fund managers are charging customers far less than the traditional large fees charged to clients by active fund managers, transforming the asset management industry.
Additionally, the hedge fund sector of the financial industry is now favoring short-term quantitative investing over deeper, long-short strategies. Because AI can analyze tremendous amounts of data rapidly and create short-term strategies, the market has favored that approach.
How Artificial Intelligence Has Impacted the Banking Sector
Artificial intelligence has made great inroads into the banking sector of the financial industry. Some examples include:
- Fraud detection and prevention – Artificial intelligence algorithms can quickly identify unusual transactional patterns, anomalies in data and suspicious relationships and put a stop to fraud before it occurs.
- Chatbots – Chatbots that are powered by AI and use Natural Language Processing can interact with customers 24 hours a day, 7 days a week upon the customers’ request. They can handle typical questions and delve into more complex account issues, helping customers to open new accounts and, when applicable, directing them to human customer service representatives to assist them.
- Credit risk management – Artificial intelligence can be used to determine the creditworthiness of a borrower, using data to predict default probabilities, and improving the accuracy of credit decisions. These AI insights are replacing expert human judgment which had previously been relied upon to assess credit risk.
- Predictive analytics – Machine learning and data analytics have helped to foster more accurate financial forecasting and prediction. Stock price predictions, revenue forecasting, case management, and risk monitoring are all being powered by AI, which can analyze greater amounts of data faster than humans could do. Performance has vastly improved, lessening the need for human intervention in these predictions.
- Customer relationship management- Banks have begun utilizing facial recognition and voice command features in their online customer applications, making it more secure and more convenient for customers to log in to view accounts. AI is also being used to analyze the behavior patterns of customers, placing them into targeted marketing segments and improving the entire customer experience.
Challenges of Using Artificial Intelligence in Finance
Ninety-eight percent of financial institutions believe that artificial intelligence and machine learning can improve the ways in which they do business, giving them a competitive edge, according to a survey by Forrester. However, over 80 percent of machine learning projects in the financial industry are scrapped because of management issues, logistical issues, and last-minute problems. This indicates that more trained and talented artificial intelligence professionals are needed to work in the financial services industry.
Security and compliance regulations also affect the usage of artificial intelligence in finance, due to the sensitive personal information they deal with daily. Artificial intelligence that is to be used in finance must be able to protect this sensitive data and follow industry and regional guidelines when it comes to data protection.
Because the financial services industry deals with a large amount of data, the scope of that data creates complex problems for the usage of artificial intelligence. Financial institutions must have their mountains of data organized in order for machine learning to be able to accurately analyze and predict outcomes.
Examples of How Companies Are Currently Using Artificial Intelligence in Finance
Artificial intelligence is proliferating across the financial services industry. There are many examples of how AI is being used in finance. These are just a select few.
- Enova, Chicago, IL – Enova is using machine learning and artificial intelligence to assess financial analytics and creditworthiness of customers. It primarily serves non-prime consumers and small businesses.
- Kensho Technologies, Cambridge, MA – Kensho uses data analytics software and machine learning training to analyze documents and datasets, helping traders to analyze financial risk and answer complex financial questions.
- Kasisto, New York, NY – Kasisto created a conversational AI platform called KAI, which is being used to enhance the experience of customers in financial services. KAI can help banks to reduce the volumes of calls at call centers by giving customers self-service options. AI-powered chatbots are also utilized to provide customers with recommendations and help with day-to-day financial decisions.
- Vectra AI, San Jose, CA – Vectra has created a cybersecurity threat detection platform that is powered by AI. This automates threat detection, helping to discover attackers who are targeting financial institutions, enhances investigations after incidents have occurred, and helps to identify information that has been compromised.
AI Jobs in the Finance Industry
As of October 2023, the following are just a few of the artificial intelligence-related positions advertised within the financial industry:
- Research and Finance Analyst, Stanford University, Stanford, CA: $68,000 to $108,000 per year
- Investor Relations Associate for AI Group, Bessemer Trust Company, New York, NY: $80,000 to $115,000 per year
- Risk Analyst, OTHSolutions, Arlington, VA: $130,000 to $160,000 per year
- Battlespace Awareness Capability Portfolio Management Support, Data and Artificial Intelligence Analyst, Cherokee Federal, Arlington, VA: salary not specified
- Model Risk Management, ProIT, Inc., Pennington, NJ: $135,113 to $160,341 per year
- FIU Coordinator, Modeling & Analytics, South State Bank, VA: $66,800 to $84,600 per year
- Quantitative Analytics Tech Lead/Data Scientist, Freddie Mac, McLean, VA: $134,000 to $200,000 per year
- Asset Management DPAS Technician, Empower AI Inc., Arlington, VA: $67,600 to $85,600 per year