Shulou(Shulou.com)06/03 Report--

This paper makes an inventory of the application of AI in the financial industry and what kind of helpful effect it has on the industry.

2019 is a very restless year for Chinese finance.

The withdrawal of Internet finance officially began in January when a special group released document 175. in October, Huang Qifan released the news that the people's Bank of China might be the first to launch digital currency, and then to the strict censorship of the privacy of financial App information in December.

It can be said that the main theme of China's finance in 2019 is "eliminating fake technology and adding real technology". Small loan enterprises under the banner of Fintech have fallen batch after batch, while advanced technologies such as AI, big data and blockchain are constantly entering the commercial landing stage.

Especially for AI, the financial industry has always been the "top student" of technology input--

With its good foundation of high digitalization and informationization, the financial industry is easier to get through the technology entrance than other industries.

In particular, the front stage of the financial industry, business reception, product sales and other processes, also belong to a highly labor-intensive industry, the cost reduction and efficiency effect brought by the application of AI will be more significant.

In addition, the financial industry has made more active attempts in technology, and a large number of financial institutions have their own technology research and development departments, so that the whole industry has a better foundation for accepting AI empowerment and does not need a long period of market education.

In this way, the financial industry has naturally become an important position for AI industrialization, as well as in 2019.

From the beginning of verification, AI has opened the financial canal.

Generally speaking, the development of financial industry AI in 2019 is still running smoothly on a good basis. I believe that many people can intuitively feel that even as a simple C-end user, they can also deeply feel the convenience brought by technological change. In terms of the front desk business of finance, "verification" is almost a necessary link. This link includes the confirmation of people, tickets, documents and other information. It is nothing more than dealing with the familiar problems of "proving you are who you are", but these are often the ones that take up a large part of the workforce in the financial industry.

From a technical point of view, solving these problems is not complicated. Through human face detection, OCR recognition, image recognition model and so on, we can meet the deartificial requirements in the verification process. In this way, many business transactions can be performed remotely, as long as you take photos and upload them on the mobile phone.

However, the difficulty of using AI to complete verification in the financial industry does not lie in the simple technical ability:

First of all, from the characteristics of the financial industry, its requirements for data security and privacy issues are naturally higher than other industries. For example, at the end of this year, the Ministry of Industry and Information Technology strictly inspected App data authorization and removed a large number of banks and financial products App, many of which were considered from the perspective of data security.

On the other hand, as mentioned earlier, the digital and information foundation of the financial industry is more perfect than other industries.

Especially when the CBRC proposed in the 13th five-year Development Plan of China's Financial Industry Information Technology: by the end of the 13th five-year Plan, all the important information systems facing the Internet scene in the banking industry had been migrated to the cloud computing architecture platform, and the migration rate of other systems was not less than 60%. Between 2016 and 2017, there was a general upward cloud trend in the financial industry.

When the financial industry already has a cloud foundation and relatively high requirements for data security, the difficulty of AI application has changed from technical capability to deployment mode. For other industries, face recognition, character recognition and other models can be applied by directly docking API, but for the financial industry, this deployment model may be relatively rough.

So in 2019, there are two main trends in the industry as a whole:

One is that technical service providers adjust their cloud solutions to meet the special needs of the financial industry through private cloud, hybrid cloud and other deployments, and form a smooth docking with the original digital basic disk.

The other is that financial institutions choose their own R & D or procurement technology to update the capabilities of their cloud platforms with AI. It may be that the financial industry has become more efficient in conquering AI technology, or that technology companies have a growing sense of service.

In a word, these two trends have brought great impetus to the application of AI in the financial industry. we can see that in the link of "verification", most banks and mutual funds products can rely on face recognition and image recognition, and complete remote online account opening, thus reducing the risks of identity fraudulent use, document fraudulent use and so on.

Financial AI 2019: write bonus questions

We want to emphasize that the financial industry broke through the problem of AI deployment in mid-2019 because it was like opening a canal, bringing not only a few recognition algorithms, but also the possibility of a steady flow of technology into fertile land. So we can see that, in addition to verification, which is the most general and common scenario. The financial industry has made more attempts in 2019, which are different from the gimmick and experimental behavior such as "JPMorgan Chase uses AI to manage funds" in the past, but are closer to improving real efficiency and opening up new business scenarios.

One of the most typical is intelligent customer service.

As a universal labor-intensive part of various industries, it is also natural for the customer service department to be "targeted" by AI. But what is different in the financial industry is that intelligent customer service is not only used in general product promotion, after-sales consultation and other processes, but also widely used in the collection process.

If we compare the number of intelligent customer service seats with the number of ordinary employees to calculate the "amount of AI", we are likely to find that the "amount of AI" of collection companies is the highest in financial enterprises.

The reason is that the collection work is highly dependent on telephone contact, talk to users through intelligent customer service, and then use big data to analyze and classify users' voice, assist employees to make decisions, and put forward different strategies for different types of users. It not only improves efficiency, but also makes the whole workflow more stable and controllable. It can be said that it has completely changed the operation mode of collection work.

For example, in this year when regulation plays an important role, AI auxiliary financial supervision has also begun a variety of attempts, the emergence of the term Regtech-- regulatory technology.

In regulatory science and technology, many kinds of AI technologies are comprehensively applied. For example, the Australian Securities and Investment Commission (ASIC) and the Singapore monetary authorities are using big data analysis to identify anomalies that can be used to identify trading trajectories. The Shanghai Stock Exchange uses machine learning to collect and model the information of investors in order to identify illegal users. The Tokyo Stock Exchange also uses Hitachi's Hitachi AI technology to identify market manipulation and other wrongdoing.

In short, the regulation of the application of science and technology, so that AI is not only used to improve the efficiency of a single enterprise, but also to participate in the prevention of systemic financial risks. In particular, it can ease the regulatory lag, which has been holding the financial industry by the throat.

Finally, there is scene innovation.

Interestingly, the transformation of the financial industry by many technology companies in 2019 is no longer satisfied with the virtual digital level, but began to touch the real space.

Tencent, JD.com and other companies have introduced a concept similar to the "financial unmanned cabin", deploying face recognition, voice interaction and other technologies on the end side through microphone arrays and intelligent cameras.

Through end-to-side deployment, through a unified hardware configuration, the technical model no longer needs to face the trouble of improving robustness due to the diversity of mobile devices. For example, because the configuration of the front camera of different devices is different, the environment of the user will also affect the light. Therefore, the recognition algorithms of face verification and document verification should be robust. But with integrated hardware configuration, there is no need to worry about these problems.

At the same time, similar to the emergence of the concept of "unmanned cabin", bypassing the entrance of the bank App, the contact point of AI is moved directly offline, so that many users who are not used to using App can also meet AI capabilities offline, which not only reduces the labor burden, but also makes business management more unified.

The above are just examples. In fact, in 2019, financial AI is very intensive in terms of popularity and innovation. In the "China AI Landing White Paper" issued by IDC, it is also mentioned that the financial industry is the most active in the application of AI, regardless of the number of projects landed or the maturity is relatively higher. The top student finished the basic questions and added points, so in short, he gave a very good answer.

Concluding remarks

Seeing the achievements of AI in the financial industry in 2019, we also seem to see a possible development path in other industries. The role of AI for the industry is from frame-by-frame image recognition to a thorough transformation of business logic, and even contribute to higher-level supervision and development issues.

Although this year, we can still see AI in the financial industry made a lot of "jokes", such as many banks' smart phone customer service is still silly fufu does not understand people, or there are always some small loan companies wearing the skin of AI block chain quantum computing to sell P2P products, but stumbling, we are still moving towards hope.

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