Since business and hedge fund executives cannot stay competitive with robotic systems that can operate massive amounts of information and keep improving their predictions while making investment decisions, AI may become the main instrument for creating earlier challenging financial objectives.
Robotic systems will soon take up the majority of positions in the financial system, which is great news since the best college grads will now be able to move to industry sectors that provide more significant advantages to people and the planet, such as tech start-ups and healthcare.
AI and Historically High ROI
The majority of the world’s trading platforms use computing systems that rely solely on algorithms and adopt new approaches based on recent data. There have already been recorded examples of using electronically stored data feeds to copy real–time investments. Taking transaction prices into consideration, one of the concepts enabled a 73% yearly ROI over the last 20 years. This is similar to an annual real market revenue of almost 10%.
Utilizing artificial intelligence in financial markets for share trading is not a novel concept. Its alternative—algorithmic trade or black boxes—has currently been in use for over a decade and is becoming increasingly popular. Automated trading accounted for 85% of the business in 2012.
If this trend holds, computer algorithms will handle 90% of trading. Current algorithms are shifting to HFT transactions, in which shares are purchased and sold in fractions of a second. This methodology rapidly identifies and exploits these disparities and trevenue gradually decreases while the trading activity remains constant.
Recent research involving 23 hedge funds that use AI found that they outperform those controlled by people. They have obtained an annual return of 8.44% during the last 6 years, in comparison to standard funds, which have varied from 1.62% to 2.62%. The survey’s analysts refer to AI’s dominant position in the sector as the fact that it continually performs repetitive tests rather than simply accumulating data.
AI indefinitely operates massive amounts of data, such as books, Twitter posts, media, economic metrics, and even entertainment such as TV shows. This is how it learns to recognize overall dynamics and continually improves its financial sector forecasts. Hedge funds used to hire mathematic experts to generate statistical data sets, including historical information to develop algorithms that foresee market possibilities—AI, however, is more efficient and precise.
That is why financial behemoths are shifting to automated systems that outperform humans in predicting market dynamics and selling.
The 7 Best Ways to Use Artificial Intelligence in the Financial Sector
Banking Services Using AI-based Chatbots
Now artificial intelligence is actively developing, and at the same time, the range of its capabilities is expanding. Chatbots and AI-based virtual assistants have been reducing the need for personal contact, wasted time, and waiting for a response from support service for a long time. After all, artificial intelligence has proven its effectiveness: now, with the help of financial chatbots on AI, customers can independently check their balance, plan payments, monitor account activity, and conveniently ask a virtual assistant questions and receive personalized banking advice anytime and anywhere they like.
Personal Financial Assistant
Another bright advantage of AI is the possibility of financial consulting services with virtual assistants.
Conversational bots with artificial intelligence or consulting robots offer financial planning services based on algorithms with virtually no human control. They can inform clients about the latest financial trends, help them expand their portfolio, achieve tax efficiency, maximize savings, and much more.
Another direction of using AI is lending. It takes a lot of time for banks to process loan applications and respond, so it is automated to simplify the process.
For example, AI algorithms determine a person’s right to receive a loan, assess risks and even provide individual solutions. Artificial intelligence is not biased, so it can make a more accurate, fair, and timely decision about the right to receive a loan.
KYC (Know Your Customer) is a method used by banks and other organizations to verify the identity of a customer. It includes identification of the person, understanding of the nature of the clients’ activities and the origin of their finances, as well as risk assessment.
The KYC procedure is necessary to confirm the identity of clients and provide access to the services they need. The essence is as follows: the clients provide documents confirming their identity to the bank branch or through photo or video identification.
Collection of documents and other customer information
How else, besides verification, is AI connected with the client’s documents?
Data in the financial environment is one of the most important resources, and their huge volume and structural diversity make manual processing difficult even for financial experts.
The use of AI-based solutions for data and document processing increases the efficiency of processes, and also allows you to extract useful information. Data analytics, data mining, and natural language processing are examples of AI and ML solutions that help companies obtain important information.
Risk management with comprehensive data analysis
In addition to processing claims, AI is even capable of such complex manipulations as risk analysis in the financial environment. The ability of artificial intelligence in finance to analyze large volumes of structured and unstructured data can improve risk management and compliance capabilities. Thus, risk managers in financial institutions can identify risks more effectively and in a timely manner in order to make more informed decisions.
Consumers need guarantees that their money and personal information will be kept safe, and artificial intelligence can help in this. It is believed that the human factor is the cause of up to 95% of cloud violations.
Robotics allows companies to prevent fraud in advance and helps to improve security by studying and identifying the usual patterns and trends of data, as well as warning agents of any anomalies or suspicious actions. If fraud is suspected, AI models can be used to reject or flag transactions for further investigation.