In the post-epidemic era, many companies expect to improve operating efficiency through financial intelligence transformation, reduce overall financial operating costs, strengthen data support for corporate risk management, and improve their ability to respond to market changes.
However, for enterprises, despite the obvious advantages of building a Financial Shared Service Center (FSSC), FSSC alone is not enough to achieve financial intelligence.
Today, the application of RPA in the field of corporate finance has become more mature. The combination of RPA and AI has further accelerated the development of smart finance.
Therefore, more and more companies are beginning to use RPA+AI to help financial intelligence to reduce costs and increase efficiency.
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01
RPA opens up "data islands" between heterogeneous systems
With the continuous deepening of enterprise informatization, there are more and more heterogeneous systems within the enterprise, the independence of the system itself is getting stronger and stronger, but the correlation between the systems is getting weaker and weaker.
This resulted in a large number of "data islands" of heterogeneous systems.
And financial process processing usually involves multiple heterogeneous systems. Due to the large amount of business, manual data transfer across systems not only affects efficiency, but also has high personnel occupancy, and is also extremely error-prone.
After the emergence of RPA, financial robots can replace manual operations with automation in accordance with business content and process characteristics, process data across systems and platforms, quickly and not prone to errors, and assist financial personnel in completing a large number of single, repetitive, and cumbersome basic businesses, thereby improving financial processing Efficiency and quality reduce financial compliance risks.
02
Smart OCR recognizes unstructured data
The data in the information system is divided into:
Structured data (tables, databases, etc.)
Unstructured data (text, pictures, video, voice, etc.)
Unstructured data has complex formats and diverse standards, and technically unstructured information is more difficult to standardize and understand.
RPA has powerful functions in data collection. Nevertheless, because RPA has no thinking ability, it is more suitable for processing standardized and structured data.
With the help of AI-OCR, RPA can use AI's autonomous learning and cognitive capabilities to identify more complex unstructured data and convert it into structured data that computers can understand, so as to better communicate between various business systems. Effective collection and integration of structured data and unstructured data.
UiBot Mage
Three major AI capabilities
Text recognition (let RPA recognize text in documents, bills, forms, pictures)
Text understanding (let RPA understand the content of the text and make decisions)
Human-computer dialogue (let RPA interact with people in natural language)
Four core advantages
Built-in OCR, NLP and other AI capabilities suitable for RPA robots
Provide pre-trained models, no AI experience required, ready to use out of the box
Seamlessly connect with Creator, let the robot have AI capabilities by dragging and dropping
Suitable for various business scenarios such as financial reimbursement, contract processing, bank account opening, etc.
03
Application of RPA+AI in the financial field
The processing of corporate financial bills faces three major pain points:
Many types of bills and large quantities
Reimbursement documents are difficult to identify
High requirements for bill processing timeliness
How to use UiBot Mage to automatically process financial bills?
Take retail financial reconciliation as an example:
The merchandise sold in the store needs to be paid by the customer at the payment counter in the mall, and the second link is returned to the store after the payment is completed. Shopping malls and stores will regularly settle settlements through the credit card records of POS machines.
In this process, the store must reconcile the sales records in the system with the actual payment amount (ie, the amount of the small ticket). There is a lot of data, and the manual verification workload is large.
UiBot Mage solution
1. Scan the receipt into an electronic version.
2. Identify electronic receipts through OCR, extract key information such as date and sales amount.
3. Reconcile the information identified by OCR with the sales records in the system. (Even if a small ticket contains sales records of multiple items-for example, 6 small tickets correspond to 10 sales records, the RPA+AI robot can also accurately check the accounts)
4. The robot marks the items that are completely aligned with the receipt and the sales record in green. Manually only need to process a few unaligned accounts, which greatly reduces the workload of manual checking.
UB Store financial RPA solution is dedicated to reducing costs, improving efficiency and optimizing processes for enterprises. Through tailor-made affordable and high-quality RPA+AI solutions, all aspects of the transaction are guaranteed, so that customers can truly experience "convenient, safe and professional" cost-effective transactions, so that more corporate financial personnel can enjoy RPA robots, Dividends from process automation.