Generally speaking, artistic intelligence is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. The research fields of artificial intelligence include robot, language recognition, image recognition, natural language processing and expert system.
In fact, it is difficult to define the scope of a discipline. Even for an ancient discipline like mathematics, sometimes it is difficult for us to sort out a clear boundary. For a rapidly developing discipline, especially artificial intelligence, which is still expanding its boundaries, it is difficult to make a relatively accurate judgment.
On the other hand, AI is highly practical and is a representative multi-disciplinary discipline. At present, artificial intelligence has been applied to various fields, including machinery, electronics, economy and even philosophy.
To be sure, artificial intelligence is quietly affecting the world through countless different applications. Artificial intelligence technology has powered many daily activities, from driving us to work to automatically adjusting thermostats, often without our knowledge. According to Gartner, 40% of major enterprises will implement AI solutions by 2020, and more than half of enterprises will double their existing AI solutions by 2020. This prediction was made before the outbreak of the covid-19 pandemic, but even considering this, the growth of artificial intelligence will continue to grow exponentially.
In some industrial artificial intelligence, machine learning and deep neural network have more applications. One of them is the financial industry, in which new technologies have been subverting and challenging traditional values.
Artificial intelligence plays a vital role in risk management, and in the financial world, time is money. For risk cases, the algorithm can be used to analyze the case history and identify any potential problems. This includes using machine learning to create accurate models that enable financial experts to track specific trends and pay attention to possible risks. These models can also be used to ensure more reliable information for future models.
Using ml in risk management means powerful processing of large amounts of data in a short time. Structured and unstructured data can also be managed through cognitive computing. Otherwise, all this means that the human team takes a long time to work.
In recent years, with the rapid growth of digital customer transactions, it is necessary to use a reliable fraud detection model to protect sensitive data. Artificial intelligence can be used to strengthen its rule-based model and assist human analysts. This in turn can improve efficiency and accuracy and reduce costs.
Artificial intelligence can also be used to review consumption history and consumption behavior, so that it can highlight abnormal situations, such as the use of a card in different global locations in a short time. AI can also learn from human corrections and apply decisions based on what should be emphasized.
All use cases of fraud management have different requirements for AI algorithms, and each use case uses them slightly differently. Transaction monitoring requires faster response time, error rate and accuracy, as well as the availability and quality of training data.
Shape security is a company that provides fraud detection services for Bank of America, mainly dealing with voucher filling, credit application fraud, gift card tracking and information extraction. The ML model used by the organization has been trained with billions of requests, so they can distinguish between real customers and robots.
In the banking industry, the intelligent chat robot driven by artificial intelligence can provide customers with comprehensive solutions and reduce the workload of the call center. Voice controlled virtual assistants are also becoming more and more popular. These assistants are usually supported by Amazon’s Alexa and have self-study function. They are able to check balances, account activity and schedule payments, and their functionality is increasing every day.
Many banks now have applications that provide personalized financial advice and help achieve financial goals. These AI driven systems can record revenue, daily expenditure and expenditure behavior, and then provide financial plans and suggestions. Mobile banking applications can also remind users to pay bills, compete for transactions, and interact more easily with banks.
Quantitative, algorithmic or high-frequency trading, as well as data-driven investment, have recently expanded in global stock markets. Investment companies are relying on computing and data science to accurately predict the future model of the market.
The advantage of artificial intelligence is that it can observe patterns from past data and predict whether they may repeat in the future. When there are some anomalies in the data, such as the financial crisis, artificial intelligence can study the data and find possible triggers, and then prepare for the future. Artificial intelligence can also personalize investment for specific investors and help them make decisions.
In many fields, artificial intelligence is being effectively used to better provide information for decision-making process. One area is credit, where AI can quickly provide an accurate assessment of potential borrowers at a low cost. Compared with the traditional credit scoring system, the credit scoring of artificial intelligence may be much more complex. They can help determine which applicants are more likely to default and which applicants do not have any reliable credit history.
The model driven by artificial intelligence also has the advantages of objectivity and unbiased, which may be a factor in human decision-making. For many people, having good credit is crucial, whether it’s buying commodities, looking for a job or renting a house.
Systems driven by artificial intelligence can become faster, more efficient and more reliable. These technologies have been more and more applied in the financial field, and have been widely adopted by financial companies. Those who accept the risks of adopting these technologies will often be rewarded with streamlined and more productive operations. Artificial intelligence has great potential for the financial world, and business leaders need to make the most informed decisions with the right data.