Market Analysis] In the tide of the development and application of artificial intelligence, various industries will have the possibility of being replaced by AI. What kind of impact can artificial intelligence bring in the financial industry and what innovations and changes will it bring?
In recent years, along with the accumulation of high-quality big data, thanks to the powerful computing power, especially the breakthroughs in deep learning algorithms, AI technology has risen comprehensively, giving rise to a series of product and business model innovations in the financial industry.
The impact of artificial intelligence on financial institutions is increasing day by day, and it is not only changing the way people deposit, invest and lend, but even preventing financial crimes.
The demand for artificial intelligence in the financial sector
The banking industry, as a highly data-driven industry, coupled with clear business rules and objectives, is an application scenario for data-driven technologies such as artificial intelligence and cloud computing.” This is based on 4 reasons. First, banks have always attached great importance to the use of IT technology, with a high level of information technology and a better technical environment and conditions. Second, banks have a high degree of information technology and have rich data deposits. Third, the traditional financial industry is more of a human-based service industry, the urgent need to reduce costs through artificial intelligence technology. Fourth, the bank has a certain amount of financial support, leaving room for trial and error.
At present, the banking industry “embrace” technology companies to carry out cooperation, mainly with two major veins: a vein is “bank + Internet companies” and “bank + technology service companies”. In the other vein, technology service companies are quietly working to bring sound changes to banks by embedding themselves in banks’ intelligent upgrading systems in the form of projects.
In the context of a strong technology country, technology innovation has been the focus of high-level planning, and the application of technology in the financial sector has become more extensive and in-depth. In recent years, regulators have issued a number of policies to affirm the importance and necessity of developing financial technology, while high-level documents to encourage the development of artificial intelligence have been launched one after another. In the background of the policy of supporting the integration of finance and technology, the market of artificial intelligence in the financial field continues to expand.
Artificial intelligence in the financial landing of innovative models
(1) intelligent investment
Now financial management has become a very normal thing for some families, every time they do financial management to go to the analysts to ask to choose various investment business, the analysts will also study the various information of customers according to the demand, and then according to their requirements to make recommendations. When artificial intelligence is applied to the financial industry, all this does not have to be so troublesome. Artificial intelligence can automatically retrieve everything about you and then input your needs to quickly recommend a set of investment and financial solutions that you need.
As you can see, with the help of machine learning, computers can already perform complex and tedious tasks such as stock trading. At the same time, a number of fund companies are already exploring the field and achieving results comparable to the judgment of human experts.
Previously, San Francisco-based startup SentientTechnologies developed an algorithm to identify trading patterns, predict trends and make successful stock trading decisions by capturing millions of data points. On Sentient’s platform, trillions of simulated trading programs created from large amounts of online public data are running. With the help of these programs, the algorithm identifies successful trading patterns for consolidation and develops new trading strategies. At the same time, the algorithm allows the system to complete the volume of 1,800 days of trading in the traditional way in a matter of minutes, and to achieve continuous autonomous optimization in trading. According to the company’s CEO Anton Wan Blondeau (AntoineBlondeau), its fund is completely operated by artificial intelligence, the overall idea is to do something that no one else and no other machine is doing.
The system allows the firm to adjust specific risk settings and is run without human intervention, said BabakHodjat, Sentient’s science officer.” It will automatically generate a set of strategies and give us instructions. It will also tell us when to exit, when to reduce exposure, that sort of thing.” Hogarth says.
And in terms of applications, there are currently many international success stories beyond Insight. Wealthfront and Betterment in the United States, MoneyonToast in the United Kingdom, FinanceScout24 in Germany, MarieQuantier in France, etc. have successfully introduced artificial intelligence into investment and finance, and now intelligent advisors have a large number of assets; an artificial intelligence-driven fund Rebellion had successfully predicted the 2008 stock market crash and gave Greece a bond F rating in 2009, when Fitch’s rating was still A. Through artificial intelligence, Rebellion was one month ahead of the downgrade; Cerebellum, the fund in charge of $90 billion, used artificial intelligence technology and has been in a profitable position since 2009.
(2) Risk prevention
In banks often invest a lot of human and financial resources every year to analyze and avoid credit risk, market risk, operational risk, etc., using various data to do modeling and analysis to avoid losses. The use of artificial intelligence, the use of domain knowledge mapping, unsupervised algorithm and multi-level recursive model-based anomaly detection and complex network analysis and graph of semi-supervised conduction model and other technologies, the construction of transaction behavior deviation prediction, account behavior deviation warning, group risk identification and other models, can effectively solve the traditional financial sector anti-fraud system facing the comprehensiveness, accuracy, singularity, data arithmetic and mining depth The problem is that the fraudulent system is not only a comprehensive, accurate, single, data arithmetic power and depth of mining.
With the promotion of e-commerce, online fraud has become more and more rampant. However, combating cyber fraud is not an easy task. In 2015, JavelinStrategy Market Research released a study showing that e-tailers lost $118 billion due to wrongful denials of legitimate transactions. One third of the cases of wrongful denial of legitimate transactions result in customers abandoning the transaction. In addition, in the U.S. alone, the financial loss caused by such cases is 13 times higher than the amount lost in genuine fraud cases.
In such a context, AI can detect fraudulent transactions that are not detected by human analysts by analyzing different data points and using machine learning algorithms. At the same time, it can also improve the accuracy of real-time approvals and reduce the rate of false rejections.
Today, there are many organizations that have started to prevent fraud with the help of artificial intelligence. mastercard’s recently launched intelligent decisioning (DI) technology is a good example. DI is understood to capture models from cardholders’ spending records and habits to establish behavioral benchmarks so that each newly concluded transaction can be compared and evaluated. The application of this technology is a major breakthrough compared to traditional crime prevention techniques that mostly assess all transactions with the help of a generic approach.
Some companies take a more comprehensive approach. For example, SiftScience collects a large amount of data from more than 6,000 websites with fraud detection capabilities and uses an intelligent engine that correlates a variety of different data points, including payment information and other behaviors on the website, to build a model of user behavior and detect fraudulent transactions through device tracking and data analysis across multiple channels.
(3) Authentication and security
Face recognition technology from major technology companies has now come to maturity and has been applied to banks in a number of ways. Many banks’ office areas are using face recognition technology to screen the identity of people, so as to determine whether there are any external people entering the inside of the bank’s office area.
(4) Intelligent customer service
With the improvement of voice recognition system and natural language understanding technology, artificial intelligence will definitely become an indispensable part of the future customer service, and many companies have now started the service of artificial intelligence customer service, algorithms gradually replace the manual to the workplace, and finally achieve an intelligent customer service center. Intelligent customer service powered by natural language processing (NLG) and machine learning algorithms to provide users with a personalized dialogue experience is becoming more and more popular.
Smart customer service is also more commonly used in the financial industry, for example, to help users manage their money. As an example, the Plum chatbot can be launched when a user clicks on a Facebook chat window to make a small installment deposit. When signing up, users simply need to associate Plum with their bank account. Plum’s artificial intelligence system then analyzes the user’s income level and spending habits and, based on that, predicts the amount of deposit he or she can accept. Small amounts are then deposited into the user’s savings account in appropriate installments and the user is regularly notified.
In addition, Cleo, an intelligent customer service, can track the income and expenses of multiple accounts, communicate with customers like a personal accountant, answer their questions, and also provide financial guidance to help users do future money planning and management. At the end of this year, Bank of America plans to launch Erica, a smart customer service (a play on the bank’s name) that will help customers make quicker, more informed decisions by interacting with them in voice and text on the bank’s mobile client. For example, Erica can be ordered to send money to a friend or make a payment without opening the application interface (UI). The AI engine of smart customer service can also analyze and manage customers’ personal finances, such as providing suggestions to achieve savings goals based on their income and spending models.
The current state of development of AI in the financial industry
In 2018, McKinsey released a research report stating that by 2030, AI will add $13 trillion to the economy, contributing to the world economy and having as much impact on change as the industrial revolution with the steam engine. It can be said that artificial intelligence has become a new opportunity for human economic and social development.
Industry experts say that the traditional financial industry, with banks at its core, is one of the more realistic landing scenarios for artificial intelligence. Driven by the growing maturity of new technologies such as big data, cloud technology, artificial intelligence, big data and the Internet of Things, traditional banks have carried out financial technology innovation, and information technology, digitalization and intelligence have generally become the goal of each bank’s development.
“In recent years, fintech is having a significant impact on banks’ business development, and many financial institutions are realizing the importance of technological transformation.” Ye Wangchun, chairman and CEO of Ping An Financial One Account, said that Ping An Group has invested a total of more than 50 billion yuan in the research and development and application of innovative technology over the past decade, while,combined with its own 30 years of continuous accumulation in the financial industry, Ping An has formed four major advantages in the development of financial technology in scenarios, data, talent and investment.
Currently, ecological openness and technological transformation have become the two key words in the development of the financial industry. In the two-way exchange process of promoting the technological transformation of traditional financial institutions and the gradual opening of new financial institutions for technological empowerment, the application of artificial intelligence in the financial market has become more extensive and gradually deepened.
The early layout of artificial intelligence financial institutions have tried to apply artificial intelligence throughout the business system. For example, the bank’s application of AI is not limited to the peripheral online intelligent customer service, telephone intelligent navigation, counter face recognition and other scenarios, but gradually penetrate into the core processes of product development, marketing, risk control, customer management and customer service.
AI technology is reshaping the wealth management industry from all aspects, which can not only solve the pain points of imbalance between supply and demand, interest-oriented, high cost, high threshold, and uneven service level commonly faced by traditional human financial advisors, but also make portraits of investors through big data, machine learning and other technologies, so that institutions can better understand customer needs, asset status, risk preferences, etc., and truly realize personalized services for thousands of people. From the regulatory level, the cooperation of AI technology with other technologies can also make the wealth management service process more open and transparent, and have complete service records to provide support for effective regulation.
In the future, innovating intelligent financial products and services according to the business characteristics of different scenarios, exploring the application paths and methods of relatively mature AI technologies in multiple fields, and thus building a whole-process intelligent financial service model, will promote the development of financial services towards proactiveness, personalization and wisdom, and help build a data-driven, human-machine collaboration, cross-border integration and co-creation and sharing intelligent economic form.
At present, institutions in the industry have started to try to use intelligent financial robots to communicate with users in natural language and open dialogue, and provide users with various financial services including account inquiries, product consultation, market analysis and investor education. Through the use of artificial intelligence for customer service, it seeks to solve the problem of matching users with products and to meet more users’ still unmet needs for financial services.
The application of artificial intelligence in the financial industry trends
In the future, artificial intelligence technology in the financial field will present several major development trends: first, the financial services industry model will be more personalized and intelligent; second, artificial intelligence services will go upstream of the value chain; third, the financial big data processing capacity is greatly improved; fourth, artificial intelligence will be the future of technological innovation *, bringing far-reaching impact on people’s lives.
The future form of finance will change significantly, traditional outlets will shrink and gradually transform, while with the upgrade of 5G and wearable devices, there will be more and more interfaces for financial services, and automated finance rooms, open banking, knowledge mapping, etc. are gradually becoming a reality.” AI, 5G, IoT and other new tools allow the financial industry to reach users more effectively, but the real core values and transactions have not changed.
Problems in AI service finance
The development of artificial intelligence today has entered the stage of “quality change” from “quantification”, but the current empowerment of technology in the financial sector has only achieved the leveling and supplementation of data, and the breakthrough of AI algorithm in the real sense has not appeared. Most of the technologies currently used in financial business scenarios are fully supervised learning, which often requires a large amount of labeled high-quality data to be able to train models. However, in the actual business scenario, the data are mostly presented in a form that lacks labels, or even a very large sample size, and many problems obviously cannot be solved if they rely only on full supervision.
In the field of finance, financial wisdom in the stage of completing the data supplement, the use of open source algorithms can be completed at first.
The era of “algorithm year” will be brought, “algorithm” will take over “data” as the driving force to promote the rapid and steady growth of AI industry. At the same time, the AI process of financial and industrial industries will enter the Moore acceleration stage, and the overall level of the industry will be greatly improved, with obvious competitive advantages. As a domestic emerging AI technology innovation enterprise, RealAI will continue to conduct independent research and development of basic AI technology and continue to plow deeper in vertical fields such as finance and industry to seek greater breakthroughs. Source: ConsumerDaily.com comprehensive analysis modeling, but the second phase of refined operation puts higher requirements on self-research algorithm hardcore technology.