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AIGCxRPA builds smarter digital employees to help businesses achieve new productivity jumps
With the growth of business and technological development, the demand for intelligence in the financial industry has been further improved, and the requirements for RPA have also been improved, 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 technologies, exploring how to achieve the automation of financial business processes, intelligent generation of financial content, intelligent risk control and so on through AIGCxRPA。
The vision of AIGCxRPA may seem beautiful, but it will not be easy to realize it。So, how to specifically build AIGCxRPA products？How can it be applied to finance and even to a wider range of government-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 especially worthy of attention - Jin Zhiwei。According to IDC's 2022 China RPA+AI market share report, Jin Zhiwei accounted for 10 percent of the market.9 percent, top of the list。In the combination of RPA and AI, Jin Zhiwei has long-term technology and experience accumulation, and has made many achievements in the deep integration of AI technologies such as NLP, OCR, speech recognition, and face recognition with RPA。In the deep integration of AIGC, the latest AI technology, and RPA, Jin Zhiwei is once again at the forefront of the industry。Next, we will take Jin Zhiwei's innovation practice as an example to explore the technical product logic of AIGCxRPA, as well as industry application scenarios。
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 the financial business, any small mistake can lead to huge economic losses, the requirements for accuracy and compliance are extremely high, and the "illusion" of large models cannot be tolerated。
In order to meet the financial industry's requirements for the accuracy and compliance of generated content, large models cannot be relied upon alone。We need to combine professional data sets from the financial industry with AI technologies such as knowledge graphs to train larger models that are more professional and generate more accurate content。This large model can understand not only the natural language of humans, but also the programming language of computers, and can more accurately generate content that meets the needs of the financial industry。On this basis, the integration of large model, AIGC and RPA can be further explored。
It should be pointed out that the big model is actually a big language model, and its best application domain is language。Both natural languages and computer programming languages are languages, they are symbolic systems, and they both follow certain grammatical rules。The process design and scheduling of RPA are driven by computer language。Therefore, it is logically reasonable to take the large model and AIGC as the bridge to connect the natural language of human and the program language of RPA system。Through large language models, we can bridge the gap between natural language and computer program language to achieve more natural and efficient human-computer interaction。According to Jin Zhiwei's practice, 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 user's use threshold can be reduced。
1, AIGC, change the RPA development model。
The traditional RPA development model mainly uses manual programming code or drag-and-drop to design and implement automated processes。This approach requires a deep understanding of the process, as well as some programming skills。Although some RPA tools provide a graphical development environment, graphical operations are still not completely free of programming requirements in the face of complex processes。For those business people without a programming background, such a bar is undoubtedly too high。
Based on the large model, AIGC technology, the way RPA is developed is undergoing a profound change。The core capability of large models is to understand and generate language, so that users can directly express business requirements in natural language。Then, with AIGC capabilities, the RPA system can generate code based on these requirements, thereby controlling the relevant components and processes。
further，Kim Zhiwei is continuing to explore through the form of dialogue，Complete RPA code search, parsing, modification and generation functions: developers only need to enter the natural language description requirements，AIGC understands this requirement，And generate the corresponding task code;AIGC can interface with component libraries，Automatically select and replace components in your code。At the same time, large models can also help developers understand and modify the code, so that the code is more in line with the 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。
The advantage of this model compared to the traditional RPA development model is that it is natural and efficient。This approach requires very little programming ability on the part of developers, and it can even be said that business people only need to clearly state their requirements to complete the development of RPA。
In the field of RPA, continuously lowering the user access barrier is an important goal。An RPA product, only simple enough to use, can be better promoted and popularized。To this end, Jin Zhiwei combined AIGC with its low-code platform to further improve development efficiency while lowering the threshold。For example, you can generate commonly used CRUD pages, chart pages, auxiliary page design;Can dynamic SQL generation, SQL optimization, etc., to assist code development。
2. Reconstruct process design and process scheduling with natural human-machine dialogue。
Process design and process scheduling are the core functions of RPA, and also the important content of RPA product optimization。Under the traditional RPA development model, these two steps are usually performed manually。In the process of manual intervention, human understanding, logical inference and decision making may lead to inefficiency of development and increase of error rate, not to mention the complexity caused by differences in knowledge and understanding between individuals. These factors jointly limit the efficiency and stability of RPA technology in practical application。However, the emergence and application of large model technology provides a new possibility to solve this problem。
In terms of process design,Large model techniques can understand natural language descriptions of business requirements and then translate those requirements into executable process designs。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 sequence of requirements, the relevance of requirements, and so on。This makes the process design generated by the large model more in line with the business needs, but also more refined and accurate。According to Jin Zhiwei's practical experience, he completed RPA code development by designing dialogue templates such as initial dialogue, component development dialogue, flowchart drawing dialogue, task configuration dialogue, etc., and guided users to describe their requirements, with remarkable results。
In terms of process scheduling,Large model technology can automatically schedule the process according to the process design。Traditional process scheduling usually requires human participation to execute the process step by step according to the content and order of the process design。In this process, human negligence, misunderstanding and other problems may lead to the execution of the process error。Large model technology can automatically complete this process, reduce manual intervention, and reduce the error rate。For example, in the process running check, AIGC can be guided to call the interface to run the script;In process drawing, AIGC can be guided to call relevant components and agents to draw process nodes and flow charts;In task configuration, you can guide the AIGC configuration process to execute tasks and build a closed-loop RPA service。
The above are some of Jin Zhiwei's exploration of the integration of large model, AIGC and RPA. In this way, the entire technical product system of RPA is reconstructed and the capability of RPA is significantly upgraded。On this basis, Jin Zhiwei will integrate the AIGC capability of RPA products in customer service, finance, human resources, audit risk control and other business scenarios, and empower banking, securities, insurance, manufacturing, telecommunications, retail and other industries。
AIGCxRPA is an advanced technology concept, but to implement this concept into specific business scenarios, it also needs a specific carrier, and digital employees are this carrier。A digital employee is an automated entity that mimics a human employee and can understand and perform the tasks of a human employee。Digital employees give RPA a concrete business carrier, is the most natural human-computer interaction interface, and is also the bridge between users and RPA system interaction。
Even people with no programming experience will be able to tell a digital employee what they want to do in a conversation, and then the digital employee will do it automatically。Going one step further, digital employees can not only help business people complete tasks, but also continuously improve productivity and accuracy through learning and gaining experience。This is like a constantly learning and improving employee, it can adapt to different task needs, and constantly improve their work ability。As a result, digital employees have achieved a wide range of applications in various industries and play an important role in enhancing productivity。
However, the digital workforce of the past often lacked intelligence, limiting its application。Digital employees based on AIGCxRPA can significantly improve the intelligence level of digital employees。More "smart" digital employees have a wider range of applications, can do more things, and have greater application value。Let's take a look at some specific business scenarios to see the value that can be achieved by upgrading RPA digital employees with AIGC。
Taking financial risk control as an example, financial risk control is a key link in the financial industry, involving customer credit rating, loan approval, transaction monitoring and other links。These processes usually involve large amounts of data processing and complex decision logic, requiring a high degree of accuracy and efficiency。Traditional manual processing is time-consuming and error-prone, and it is difficult to meet the high requirements of risk control in the financial industry。
Large model-driven RPA digital employees can play to their powerful advantages in this scenario: First, large models understand natural language and can directly handle user queries and requests。For example, users can describe their loan needs to digital employees in natural language, and the large model can understand these needs and generate the corresponding query and action code;Second, AIGC can automatically generate reports and analyses that meet your 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 and trading behavior。With AIGC, digital employees can automatically generate these analyses, reducing 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 value in intelligent customer service and document understanding scenarios。For example, in terms of intelligent customer service, customer service in the financial industry is complex and needs to deal with a large number of customer inquiries, complaints and requests every day。Digital employees can quickly understand customer issues and needs in the form of conversations，And based on existing knowledge base and business rules，Automatically generate responses and operational instructions that meet your needs，These instructions are then automatically executed via RPA;Document processing aspect，Document processing in the financial industry is huge，Including contract review, credit data processing, statement analysis, etc，Digital employees can understand document content，Extract key information，And then through AIGC，Automatically write reports and analyses，Finally, the relevant business processes are automatically executed through RPA。This not only improves work efficiency, but also reduces error rates。According to Jin Zhiwei's actual customer service experience, significant efficiency improvement can be achieved in many application scenarios of financial institutions such as banks and brokerages。
Moving beyond the financial sector, let's take a look at the broader realm of government and corporate services。In these areas, digital employees that combine AIGC and RPA can provide better service by automating complex workflows, significantly increasing productivity and reducing human errors。
Government services cover complex and diverse business scenarios, such as public service application, government decision support, document processing, etc。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 parse public applications, and then automatically generate and execute processing processes according to preset rules。Compared with the traditional way, this way does not require a lot of human intervention, and can quickly and accurately complete the task, which greatly improves the public service experience。
For enterprise customer service, whether it is customer relationship management, product recommendations, or after-sales service, there is a large amount of data and business processes to deal with。In these businesses, digital employees can understand customer needs, automatically generate policies to meet them, and automate related business processes。Compared to traditional methods, digital employees integrated with AIGCxRPA not only improve service efficiency, but also reduce human error, thus improving the service quality of enterprises。
In the wave of technology, the potential and possibilities of AIGCxRPA are like an endless ocean waiting to be explored。Like a bridge, it connects human natural language with complex program code and realizes the programming and parsing 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, corporate services, and improve work efficiency。This is a deep integration of human and machine, a complete subversion of the way of working, but also a bold vision of the future work scene。
However, despite the potential of AIGCxRPA, we should also be aware that it is still growing, and there are many areas for us to break through and challenge。For example, how do you guarantee the security and reliability of the code generated by AIGCxRPA？How to improve the level of automation and intelligence while ensuring compliance and controllability of its operations？These are all problems that we need to study and solve in depth。Looking forward to the future, we look forward to innovative enterprises like Jin Zhiwei, which can continuously expand the technological boundaries, explore broader application scenarios, and bring greater convenience to our work and life。
