The banking industry sets off a wave of RPA丨RPA is applied to 9 major scenarios in the banking industry
In order to maintain competitiveness in an increasingly saturated market, banks have to find new ways to provide customers with the best user experience. Within the bank, the challenge of maintaining the highest level of security is increasing while maximizing efficiency and reducing costs as much as possible.
As a traditional financial institution, banks need to repeat certain operations at every moment of the day, such as calling out data by customer service, verifying customer information, and so on. These massive repetitive behaviors have seriously consumed the human and financial resources of banks. How to liberate this part of labor costs has become a thorny issue that many banks need to consider and solve.
To meet these needs, RPA (Robot Process Automation) is becoming a powerful and effective tool.
The banking industry is setting off a wave of RPA applications
Emerging financial technology represented by RPA is accelerating the development of commercial banks toward digital, intelligent, and ecological through deep integration with industry scenarios.
The emergence of RPA has given banks new hope to a certain extent. Through the exploration of RPA technology, and using the huge energy contained in RPA, we can achieve deep-level remodeling, optimization and upgrading of the existing service process and product system of banking companies.
Currently, the domestic banking industry is setting off a wave of RPA applications. E.g:
After the Industrial Bank applied RPA robots, the 15 process robots that went online in 18 months saved the company a total of 5.15 million yuan in operating costs, reduced human resources by about 22,000 people each year, and helped increase revenue by about 233 million yuan. , And the customer experience has been continuously improved.
After the implementation of China Everbright Bank's RPA, business processes have become smoother, and the efficiency of opening accounts at the counter has been significantly improved, effectively reducing the waiting time for customers. By automatically triggering "RPA" technology to log in to the People’s Bank of China’s account management system for qualification review of 132 corporate account openings, it only takes 1 minute to complete a single review, and the query accuracy rate reaches 100%.
In the field of telephone customer service business operations, ICBC uses RPA technology to fully automate tasks such as obtaining task acquisition, generating outbound survey information, confirming outbound calls with customers, and backfilling outbound results. The efficiency of business processing is doubled compared with manual processing.
Bank RPA
In the past, mature banking institutions would deploy multiple business platforms and management systems within the company in order to realize the informatization of business processes.
However, the success of informatization has not achieved the promise of automation. Most bank staff still frequently commute between multiple systems every day, performing a lot of manual work to coordinate and transcribe data and various processing transactions.
In order to solve this problem, the bank and its IT department are working hard to merge different original IT systems into a coherent workflow to facilitate the operation of employees and improve efficiency. However, the time spent on system integration, the costs involved, and the ultimate ease of use are all stumbling blocks that hinder banks from achieving automation.
With the rapid increase in the amount of data to be processed every day, the banking industry is now advocating the use of RPA (Robot Process Automation) technology to minimize errors and manpower and improve work efficiency.
RPA is applied to 9 typical scenarios in the banking industry
RPA robots can control the mouse and keyboard instead of employees, helping banks handle a large number of repetitive businesses. The following four typical application scenarios in the banking industry can be handed over to UiBot-RPA to automate the process.
Scenario 1: Mortgage loan processing
In view of the different speeds of review and lending by different banks, it usually takes at least 15-30 working days for banks to complete the mortgage loan to complete the entire process. For customers who are in urgent need of money, this is a long and anxious process because the application must go through various examinations (such as credit checks, credit checks). Minor data errors and errors from customers or banks may delay or even cancel the process. With the help of RPA, banks can now accelerate the completion of the process according to set rules and algorithms, and clear the bottleneck of process delays and data accuracy
Scenario 2: Interbank
Account Reconciliation As it involves online banking logins of various banks, the security of password storage in RPA automation is very important. At the same time, the RPA robot must be able to identify the bank’s security password login, because some banks’ pop-up boxes cannot identify interface elements, and some require a soft keyboard for input even if they can be identified.
UiBot KeyBox perfectly solves these two problems: high security-you can store passwords in it (UiBot KeyBox can store 30 different password combinations), and you can simulate password input without identifying elements or soft keyboards.
In addition, the login of some banks will involve verification codes, which are basically a combination of four to five digits in English. Although general OCR can be used for identification, the pass rate is not very high. However, using a third-party manual coding platform, the accuracy rate is close to 100%, which is more applicable.
Scenario 3: Bank head office reconciliation When
bank head office branch reconciliation, you need to pay attention to the most important point: among the payable and receivable information of more than a dozen branches, some are manually processed and produced by business personnel, and may be manually written by the branch at any time The reason is that there is no match. Therefore, it is necessary to design a flexible information configuration table for three-level fuzzy matching. There are two main types of checking amounts: banks and enterprises and internal enterprises.
Scenario 4: The bank declares value-added tax
every quarter, the bank must declare the value-added tax, and calculate the distribution of various taxes for each taxation agency. The amount of data ranges from 150,000 to 500,000. It is very important to choose the right data processing in this process. RPA can log in to the internal production system, download the input transfer form, automatically generate the report, and then process the data of the VAT subject form and tax distribution form. With the Excel customized data processing module that is more suitable for business logic, data processing can be realized within 65 seconds to 90 seconds.
Scenario 5: Banks query the balances of internal multi-account accounts.
In the core teller system of the bank, it is often necessary for staff to query the balances of internal accounts in large quantities and generate reports with the query results. This process is very consistent with one of the characteristics of RPA to solve the problem: manual batch repeated operations. RPA logs in to the core teller system, cyclically inquires about the account balance and automatically generates a report to complete the inquiry efficiently.
Scenario 6: Account closure process The
bank receives a request to close the account every month. Sometimes, if the customer does not provide the proof required to operate the account, the account can also be closed. Considering the huge amount of data that banks need to process every month and the checklist they need to follow, the scope of human error will also expand. Banks can use RPA to send automated reminders to customers, asking them to provide the required proof. The RPA robot can process account closure requests in the queue based on the set rules with 100% accuracy in a short time.
Scenario 7: Credit Card Processing In the
past, banks would spend several weeks verifying and approving customers' credit card applications. The long waiting period often causes customer dissatisfaction and sometimes even causes the customer to cancel the request. Now, with the help of RPA, banks can speed up the dispatch of credit cards. RPA bots can collect customer documents in just a few hours, perform credit checks and background checks, and make decisions based on the set parameters of whether the customer is eligible for a credit card.
Scenario 8: KYC process
KYC stands for "Know Your Customer". Knowing customers is a very important compliance process for every bank. KYC needs at least 150 to more than 1,000 FTEs to inspect customers. According to a Thomson Reuters survey, some banks spend at least $384 million a year on KYC compliance. Considering the costs and resources involved in the process, banks have now begun to use RPA to collect customer data, filter and verify it. This helps the bank complete the entire process in a shorter time, while minimizing errors and manpower.
Scenario 9: Bank verification and general ledger The
bank must ensure that its general ledger is updated with all important information, such as financial statements, assets, liabilities, income and expenses. This information is used to prepare the bank’s financial statements, which are then accessed by the public, media and other stakeholders. Considering the large amount of detailed information required to create financial statements from different systems, it is important to ensure that the general ledger is free of any errors. The application of RPA helps to collect information from different systems, verify the information and update the system without any errors.
Banks and RPA suppliers are constantly using high technology to reduce costs, improve work efficiency, improve decision-making and optimize customer experience. With the further extensive application and integration of RPA automation, deep learning and external data ecosystems, the banking industry will usher in more in-depth changes.