Data intelligence industry innovation service media
——Focus on digital intelligence and change business
The financial industry has long been the best testing ground for new technologies. As a highly digital and information-intensive industry, the financial industry has natural advantages and needs for the absorption and application of new technologies. The rise of the digital economy has accelerated the financial industry's desire and demand for intelligence. Among the various technologies to realize financial intelligence, RPA is undoubtedly an extremely important means. By configuring software robots, the RPA system simulates and learns human business operation processes in application software, and then executes various business processes on a large scale to help financial institutions automate business processes, improve operational efficiency, and reduce manual errors.
With the growth of business and the development of technology, the demand for intelligence in the financial industry has further increased, so the requirements for RPA have also increased, and AIGCxRPA has become the most cutting-edge technology and application exploration direction. The financial industry has once again become a testing ground for the latest technology, exploring how to realize the automation of financial business processes, the intelligent generation of financial content, and the intelligentization of risk control through AIGCxRPA.
The vision of AIGCxRPA seems beautiful, but it is not easy to realize this vision. So, how to specifically create AIGCxRPA products? How to apply it to finance and even wider government and enterprise business scenarios? To answer these questions, service providers in the industry need to conduct in-depth exploration.
In this field, there is a service provider that deserves special attention-Jin Zhiwei. According to the 2022 China RPA+AI Market Share Report released by IDC, Jinzhiwei ranked first in the market with a market share of 10.9%. In terms of combining RPA and AI, Jinzhiwei has accumulated long-term technology and experience, and has achieved many achievements in the deep integration of NLP, OCR, speech recognition, face recognition and other AI technologies with RPA. In the deep integration of AIGC, the latest AI technology, and RPA, Jinzhiwei is once again at the forefront of the industry. Next, we will take Jinzhiwei's innovative practice as an example to discuss the technical product logic of AIGCxRPA, as well as industry application scenarios.
Data source: IDC 2022 China RPA+AI Market Share Report
AIGCxRPA, refactoring RPA technology product logic
Jin Zhiwei has been deeply involved in the financial field for a long time, and knows that finance has strict requirements for the controllability and certainty of the generated content. In financial business, any tiny mistake may lead to huge economic losses. The requirements for accuracy and compliance are extremely high, and the appearance of "illusion" of large models cannot be tolerated.
To meet the accuracy and compliance requirements of the financial industry for the accuracy and compliance of generated content, large models cannot be relied upon alone. We need to combine professional data sets in the financial industry and AI technologies such as knowledge graphs to train more professional large models with more accurate generated content. This kind of large model can not only understand human natural language, but also understand computer programming language, and can more accurately generate content that meets the needs of the financial industry. On this basis, we can further explore the integrated development of large models, AIGC and RPA.
It should be pointed out that the large model is actually a large language model, and its best application field is language. Both natural language and computer programming language are languages, they are both symbol systems, and both follow certain grammatical rules. The process design and process scheduling of RPA are all driven by computer language. Therefore, it is logically reasonable to use the large model and AIGC as a bridge to connect human natural language and the programming language of the RPA system. Through the large language model, we can bridge the gap between natural language and computer programming language to achieve more natural and efficient human-computer interaction. Judging from the practice of Jin Zhiwei, with the help of large models and AIGC capabilities, the efficiency of RPA can be greatly improved in code production, process design, process scheduling and other links, and the threshold for users to use can be lowered.
1. AIGC, changing the RPA development model.
The traditional RPA development model mainly designs and implements automated processes by manually writing program codes or using drag-and-drop methods. This method requires users to have a deep understanding of the process, but also requires a certain programming ability. Although some RPA tools provide a graphical development environment, when faced with complex processes, graphical operations still cannot completely get rid of the programming requirements. For those business people without a programming background, such a threshold is undoubtedly too high.
Based on large models and AIGC technology, the way of RPA development is undergoing profound changes. The core capability of the large model lies in understanding and generating language, so that users can directly express business needs in natural language. Then, through the AIGC capability, the RPA system can generate corresponding codes according to these requirements, thereby controlling related components and processes.
Furthermore, Jinzhiwei is continuing to explore the functions of searching, parsing, modifying and generating RPA code through the form of dialogue: developers only need to input the requirements described in natural language, and AIGC can understand the requirements and generate corresponding task codes; AIGC can connect with the component library to automatically select and replace components in the code. At the same time, the large model can also help developers understand and modify the code to make the code more in line with requirements; after the code is generated and adjusted, the developer can run the code directly. During this process, AIGC will automatically check the correctness and executable of the code to ensure that the code can run normally.
Jinzhiwei intelligent interactive RPA - automatic code generation
Compared with the traditional RPA development model, the advantage of this model lies in its naturalness and efficiency. This method has very low requirements on the programming ability of developers. It can even be said that business personnel only need to clearly express their needs to complete the development of RPA.
In the field of RPA, it is an important goal to continuously lower the threshold for users to use. An RPA product can be better popularized only if it is simple and easy to use. To this end, Jinzhiwei combined AIGC with its low-code platform to further improve development efficiency while lowering the threshold. For example, commonly used CRUD pages and chart pages can be generated to assist page design; dynamic SQL generation and SQL optimization can be used to assist code development.
Jinzhiwei intelligent interactive RPA - AIGC+ Jinzhiwei Qingsong low-code platform
2. Reconstruct process design and process scheduling with natural man-machine dialogue.
Process design and process scheduling are the core functions of RPA and an important part of RPA product optimization. In the traditional RPA development mode, these two steps usually need to be done manually. In the process of manual intervention, human understanding, logical inference, and decision-making may lead to low development efficiency and increased error rates, not to mention the complexity brought about by differences in knowledge and understanding among individuals. These factors together limit the efficiency and stability of RPA technology in practical applications. However, the emergence and application of large model technology provides new possibilities for solving this problem.
In terms of process design , large model technology can understand the natural language description of business requirements, and then convert these requirements into executable process design. In this process, the large model can not only understand the specific content of the requirements, but also understand the context of the requirements, including the order of the requirements, the relevance of the requirements, and so on. This makes the process design generated by the large model more in line with business needs, and more refined and accurate. Judging from the practical experience of Jinzhiwei, it completes RPA code development by designing dialogue templates such as initial dialogue, component development dialogue, flowchart drawing dialogue, task configuration dialogue, etc., and guiding users to describe requirements, and the effect is remarkable.
Jinzhiwei guides users to complete the RPA process design through dialogue
In terms of process scheduling, the large model technology can automatically schedule the process according to the process design. Traditional process scheduling usually requires manual participation, and the process is executed step by step according to the content and sequence of the process design. In this process, problems such as human negligence and misunderstanding may lead to errors in the execution of the process. The large model technology can automatically complete this process, reducing manual intervention and reducing the error rate. For example, in the process operation inspection link, AIGC can be guided to call the interface to run scripts; in the process drawing link, AIGC can be guided to call related components and agents, and process nodes and flow charts can be drawn; in the task configuration link, AIGC can be guided to configure the process to execute tasks and build an RPA business closed loop.
The above are some explorations of Jin Zhiwei's integration of large models, AIGC and RPA. In this way, the entire technical product system of RPA is reconstructed, and the RPA capability is significantly upgraded. On this basis, Jinzhiwei applied the RPA products integrated with AIGC capabilities to various business scenarios such as customer service, finance, human resources, and audit risk control, and empowered various industries such as banking, securities, insurance, manufacturing, telecommunications, and retail.
Jinzhiwei serves many business scenarios in industries such as finance, government affairs, and manufacturing
Make digital employees smarter and help thousands of industries achieve new productivity leaps
AIGCxRPA is an advanced technical concept, but to implement this concept into specific business scenarios, a specific carrier is needed, and digital employees are exactly this carrier. A digital worker is an automated entity that mimics a human worker and can understand and perform the tasks of a human worker. Digital employees give RPA a concrete business carrier, which is the most natural human-computer interaction interface and a bridge for users to interact with the RPA system.
Even people with no programming experience can tell digital workers what they want to accomplish in a conversational format, and then the digital workers will automatically complete it. Furthermore, digital employees can not only help business personnel complete tasks, but also continuously improve work efficiency and accuracy through learning and accumulating experience. This is like an employee who is constantly learning and improving. It can adapt to different task requirements and continuously improve its working ability. Therefore, digital employees have been widely applied in various industries and play an important role in improving productivity.
However, the previous digital employees were often not sufficiently intelligent, which limited their application. AIGCxRPA-based digital employees can significantly improve the intelligence level of digital employees. Smarter digital employees have a wider range of applications, can do more things, and have greater application value. Next, let's use some specific business scenarios to see the value that can be realized by RPA digital employees upgraded with AIGC.
Taking financial risk control as an example, financial risk control is a key link in the financial industry, involving multiple links such as customer credit rating, loan approval, and transaction monitoring. These links usually involve a large amount of data processing and complex decision logic, requiring a high degree of accuracy and efficiency. The traditional manual processing method is time-consuming and error-prone, and it is difficult to meet the high requirements of the financial industry for risk control.
RPA digital employees driven by large models can exert their powerful advantages in this scenario: First, large models can understand natural language and can directly process user queries and requests. For example, users can describe their loan needs to digital employees through natural language, and the big model can understand these needs and generate corresponding query and operation codes; secondly, AIGC can automatically generate reports and analysis that meet the needs. In the financial risk control scenario, it is usually necessary to analyze a large amount of data to judge the customer's credit status, transaction behavior, etc. Through AIGC, digital employees can automatically generate these analyzes and reduce manual workload; thirdly, RPA digital employees can automatically perform a series of operations, such as data query, calculation, report generation, etc., which can greatly improve the efficiency of financial risk control work.
In the financial field, the application of integrating AIGCxRPA digital employees goes far beyond risk control scenarios, and it can also play a huge role in scenarios such as intelligent customer service and document understanding. For example, in terms of intelligent customer service, the customer service links in the financial industry are complex and complex, and a large number of customer inquiries, complaints and requests need to be handled every day. Digital employees can quickly understand customer questions and needs in the form of dialogue, and automatically generate replies and operating instructions that meet the needs based on existing knowledge bases and business rules, and then automatically execute these instructions through RPA; in terms of document processing, the financial industry has a huge workload in document processing, including contract review, credit data processing, and report analysis. This not only improves work efficiency, but also reduces error rates. Judging from Jinzhiwei's actual customer service experience, it can achieve significant performance improvement in many application scenarios of financial institutions such as banks and securities firms.
Jinzhiwei's service financial customers
Further, jumping out of the financial industry, let's take a look at the broader government and corporate service areas. In these fields, digital employees integrating AIGC and RPA can greatly improve work efficiency, reduce manual errors, and provide better services by automating complex workflows.
Government services cover complex and diverse business scenarios, such as public service applications, government decision support, and document processing. In these businesses, digital employees can automate various tasks by understanding and executing instructions in natural language. For example, for public service applications, digital employees can automatically receive and analyze public applications, and then automatically generate and execute processing procedures according to preset rules. Compared with the traditional method, this method does not require a lot of human intervention, and can complete tasks quickly and accurately, which greatly improves the service experience of the public.
For enterprise customer service, whether it is customer relationship management, product recommendation, or after-sales service, a large amount of data and business processes need to be processed. In these businesses, digital workers can understand customer needs, automatically generate strategies to meet them, and automate related business processes. Compared with traditional methods, digital employees integrated with AIGCxRPA not only improve service efficiency, but also reduce human errors, thereby improving the service quality of enterprises.
In the tide of technology, the potential and possibility of AIGCxRPA is like an endless ocean, waiting for us to explore. It is like a bridge that connects human natural language with complex program codes, and realizes the programming and analysis of natural language. It can also be transformed into an omnipotent digital employee, able to automate various business processes for us in various fields such as finance, government affairs, and corporate services, and improve work efficiency. This is a deep integration of humans and machines, a complete subversion of working methods, and a bold vision of future working scenarios.
However, despite the unlimited potential of AIGCxRPA, we should also clearly realize that it is still growing, and there are still many areas that we need to break through and challenge. For example, how to ensure the security and reliability of the code generated by AIGCxRPA? How to ensure the compliance and controllability of its operation while continuously improving the level of automation and intelligence? These are the problems that we need to study and solve in depth. Looking forward to the future, we look forward to innovative companies like Jinzhiwei that can continue to expand the boundaries of technology, explore broader application scenarios, and bring greater convenience to our work and life.
Text: Yuemanxilou / Data Ape
