AI and Machine Learning: How These Technologies are Revolutionizing the Financial Industry

The computational power of artificial intelligence (AI) and machine learning (ML) has exploded in recent years.

Artificial intelligence is the recent technological evolution of computer systems and software, taking the world by storm. Tasks that were once required to be completed manually by a human can now be replaced by computer programs that are able to imitate human reasoning. Oftentimes, AI programs can complete these tasks faster than a human could and with less human error. AI is also able to process large amounts of data that would be humanly impossible for individuals to do. This allows AI programs to recognize patterns in data and help in human decision-making processes hundreds of times faster than any human. These abilities can be compounded through machine learning, or AI computer systems that are able to learn and adapt over time without following explicit instructions. Using algorithms and statistical models, machine learning programs are able to analyze and draw inferences from patterns of data, imitating human behavior and judgment of data and patterns better than any human could.

Technological progress is human nature, and the added application of machine learning and artificial intelligence in fintech has taken a fair share of work off the everyday developments within financial technology. Advancements in workflow, customer service, and fintech business methods have transformed new opportunities for improved automation of various processes within the financial technology industry. Innovations in artificial intelligence and machine learning have created more efficient and improved processes for data-driven decision-making and have automated tasks and processes that would have otherwise taken hours, days, or weeks longer, simply through the use of advanced technological advancements.

While AI and ML have created so many opportunities in industries such as medicine, education, transportation, operations, and logistics, financial technology has not fallen far behind as it continues to be one of the fastest-growing industries in the world.

AI and ML have impacted financial services such as risk management, financial sales forecasting, financial fraud analysis, customer support, service personalization, asset and investment management, financial product and service recommendation, and stock price prediction. While plenty of use cases exist, how do financial technology companies benefit by using these technological advancements over existing processes?

Machine Learning and Artificial Intelligence: How Fintechs Benefit

Innovative AI and ML solutions have invaded fintech companies for the past decade, with applicable use cases present in almost every field of financial technology. In general, AI and ML are able to assist in the following cost and time-related improvements to the consumer experience:

• Increased cost-efficiency: AI and ML efficient processes require less human personnel to be involved with the computational decision-making functionality of fintech algorithm operations.
• Advanced financial fraud prevention: AI and ML solutions can detect fraudulent activity better than any human or non-AI/ML program can. This is because AI and ML systems often learn and gather more data to influence future decision-making behavior as they experience more use cases.
• Reduced biases: AI and ML systems reduce biases by providing predefined checks that enable solutions to be predictive and stable rather than reactionary and unstable.
• Boosted customer engagement: Personalized and customized settings motivate more engaged and technologically knowledgeable consumers to be more involved in the design and implementation of a financial product or service.
• Enhanced scalability: Systems that are built with AI and ML capabilities can be easily adjusted to the scale of one’s business needs. Because the system is constantly learning, analyzing, and gathering data, an AI/ML financial product or service can expand and grow with the consumer base as it learns more functionality. These systems can even recommend ways to scale and grow its solution with the added customers.
• Improved time-management functionality: By computing and analyzing millions of times faster than a team of human personnel could, fintech startups and companies are able to save countless hours on projects, improving overall time management and efficiency.

Artificial Intelligence and Machine Learning: Fintech Applications and Use Cases

Credit Scoring

Predictive analytics and decision-making capabilities are some of the most significant advantages artificial intelligence and machine learning provide to the financial technology industry. In particular, banking institutions and other fintech firms are able to utilize artificial intelligence and machine learning algorithms to optimize credit scoring and the circulation of finances by providing loans regulated with machine learning-based credit scoring systems instead of standard rule-based systems. While rule-based credit scoring systems operate based on data about age, gender, occupation, and other general information, machine learning-based scoring systems can work with far more delicate attributes and settings that can often not be scored by traditional credit scoring algorithms or human analysis. This allows for a far more reliable and accurate credit score reading for individuals which leads to better decision-making by banking institutions. By taking into consideration an individual’s expense and saving preferences, along with other digital trackers and aspects, banking institutions can make far more personalized decisions about the future performance of loans depending on the individual.

Security, risk management, and fraud detection

AI and ML can make security, risk management, and fraud detection easier and much more accurate. AI and ML models can spot suspicious activity and prevent fraud far better than the human eye or non-AI/ML models can. These systems have become a powerful tool to stop money laundering and financial crimes and provide more trust, safety, and reliability to the consumer.

Quantitative and Algorithmic Trading

Quantitative and Algorithmic Trading has never been easier with AI and ML. Systems have been perfected to such a degree that even standard individuals can develop their own quantitative algorithmic trading strategies to execute profitable transactions in the stock market. By conducting research on historical data and building statistical models with AI and ML and machine learning, one can spot potentially profitable investments faster than most other financial models and financial competitors. This technological development has opened unforeseen opportunities for fintech companies enabling them to make any desired number of trading orders on multiple markets while effectively mitigating risk.

Personalized Banking

AI and ML have allowed fintech companies and financial institutions to build greater personal bonds with clients. By tracking a client’s location, time, and expense habits with voluntary consent, fintech applications are able to use AI and ML to predict relevant financial services or products through advertisements that these consumers are more likely to purchase or use.