The significant application of artificial intelligence in finance will change five hidden dangers of finance

liu, tempo Date: 2021-07-08 15:41:53
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Data is the power source of digital transformation. AI can unlock the value of data and release the value of data in a new way, so as to: (1) predict and shape future achievements; (2) support people to carry out higher value work; (3) create intelligent workflow with automatic decision-making and excellent experience; (4) re conceive highly personalized business model.

AI is the transformational technology that defines our time. Up to now, artificial intelligence has experienced a sharp bubble cooling and entered the trough of the technology maturity curve. The industry began to return to reason, paying more attention to how artificial intelligence landed on the industry and promoting the digital transformation of enterprises.

So, what is the significance of AI application in finance? How can AI help the financial industry to improve its hidden dangers?

In 2020, artificial intelligence (AI), machine learning (ML) and deep neural network (DNN) are subverting the industry business and challenging the traditional mode of the financial industry.

There is no doubt that artificial intelligence is slowly influencing all walks of life in the world through a variety of applications. And artificial intelligence technology has also entered people’s daily activities, from people’s work to living environment, are unknowingly changed. For enterprises, according to Gartner’s survey, by 2020, 40% of the main businesses have implemented AI solutions, and nearly half of the existing businesses will double their existing business income through AI programs. This series of forecasts were made as early as 2020.

 significance of AI application in finance

Under the wave of artificial intelligence, in some industries, artificial intelligence, machine learning (ML) and deep neural network (DNN) have more applications. One of the challenges of using new technology in the financial industry is to subvert the traditional business model.

  1. Finance risk management

Artificial intelligence plays a vital role in risk management. In the field of financial industry, there is a saying that time is money, and it shows perfectly. So for risk cases, we can use AI algorithm to analyze the case history and identify any potential problems. This involves using machine learning to create precise models that enable financial experts to follow specific trends and be aware of possible risks. These models can also ensure more reliable information for future models.

The use of machine learning (ML) in risk management means that large amounts of data can be processed powerfully in a short time. Structured and unstructured data can also be managed through cognitive computing. Cloud computing and natural language processing (NLP) are combined to provide complex analytical solutions in understandable language.

  1. Financial fraud prevention

In recent years, with the rapid growth of digital customer transactions, the market urgently needs a reliable fraud detection model to protect these sensitive financial data. Ai artificial intelligence can be used to enhance the rule-based model to assist analysts. This can not only improve the efficiency and accuracy of data analysis, but also reduce the cost of consumption.

Through AI Artificial Intelligence, we can quickly understand the user’s consumption history and behavior, and judge whether the person shows violations. For example, we can query the card usage records in different locations around the world in a short time through AI. It can also learn from these human behaviors and make decisions according to the content of the query.

All use cases in fraud management have different requirements for AI algorithms, because their use is slightly different in each case. In order to improve the response time of transaction monitoring, reduce the error rate and improve the accuracy, data training is needed. The ML model trained by billions of requests can clearly distinguish the actual customers and robots.

  1. Finance personalized bank

Robot customer service is not uncommon in all walks of life. In the banking industry, it can also start the intelligent chat robot driven by artificial intelligence, which can provide comprehensive solutions for customers and reduce the workload of call center. And voice control virtual assistant is becoming more and more popular. It can check balance and account activity and arrange payment, and its function is upgrading every day.

Today, many banks have applications that provide personalized financial advice and help achieve financial goals. These AI driven systems track revenue, regular expenses, and spending behavior, and then provide financial planning and advice. These banking applications can also remind users to pay bills, compete for transactions, and interact more easily with offline banks.

  1. Finance – Quantitative Trading

Quantitative, algorithmic or high-frequency trading or data-driven investments have recently expanded in global stock markets. Investment companies rely on computing and data science to accurately predict the future pattern of the market, and can process a large amount of data and reduce it to a digital level that can be applied to specific stocks.

The advantage of artificial intelligence is that it can observe the patterns in the past data and predict whether they are likely to repeat in the future. When there are some anomalies in the data (such as financial crisis), artificial intelligence can study the data and notice the possible trigger factors, and then prepare for the future. AI can also provide personalized investment for specific investors to help them make decisions.

5.Finance — Credit decision

We all know that in many fields, ai artificial intelligence is effectively used to better guide the decision-making process. One of these areas is credit. Artificial intelligence can quickly and cheaply assess potential borrowers accurately. Compared with the traditional credit scoring system, artificial intelligence credit scoring system may be more complex. They can help identify applicants who are more likely to default and those who lack a reliable credit record. It is also suitable for enterprise evaluation, so that enterprises can evaluate customers with low credit record level. This can provide a transparent way to consider groups that are considered high risk.

The model driven by artificial intelligence also has the advantage of objective unbiased, which may be a factor for decision-making. For many people, having a good reputation is crucial, whether it’s making a big purchase, finding a job or renting a house.

The system driven by artificial intelligence can become faster, more efficient and more reliable. These technologies are finding more applications in the financial field, and are widely used by many financial companies. We should know that artificial intelligence has great potential in the financial field, and business decision-makers can make the most correct decisions with the right data.

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