An article on the official website of Runhe Software on September 14, 2026 introduced the financial test FDE solution, clarifying that FDE is Forward Deployed Engineer, and stated that it has completed verification in a large city commercial bank’s digital renminbi project, involving tools such as case generation and message search. The client is not named, and the available materials are not sufficient to independently recalculate the project effects.
Messages with the same text do not mean they correspond to the same business step.
The following is ChinaOPC’s independent analysis of the testing service and is not an undisclosed implementation detail of the project. Messages can be understood as structured information transmitted between systems. When looking up a field or responding to a prompt, engineers also need to know which request it came from, which process step it is in, and what happened before. If only a piece of text found is given to the model, it is easy to lose the conditions that determine the meaning.
For example, a response prompt may appear after an initialization failure or after a repeated request. The surface text is similar, but the reproduction method and expected results are different. A small team can have the search results display the test scenario number, request direction, time sequence and associated records at the same time, and then let people familiar with the business determine whether it is a required sample. This example is used to illustrate the method and is not an actual message from the client system.
Before generating a case, there must be a starting point that can be reconstructed
The initial data and system status in the test case determine whether subsequent steps can be executed. If the sample omits preparatory conditions, even if the AI writes out the complete steps, it may not be able to repeat it in another environment. You can first ask business experts to confirm the starting point of the scenario, and then let the tool add steps and check items; leave a place for unknown conditions to be confirmed, and prevent the model from generating a seemingly reasonable default answer.
Some conditions come from business rules and some come from the test environment. The two should be explained separately: business rules determine under what circumstances the expectation is true, and environmental conditions explain whether it can be verified currently. A test that is not executed because the dependent system is unavailable is a different conclusion than a test that is found to have an incorrect behavior after execution. If unified writing fails, it will be difficult for the team to know whether to modify the function or restore the verification conditions first.
After finding similar samples, you also need to confirm where they are expected to come from.
Historical messages can be used to locate clues, but they cannot automatically become the correct answer to a new test. Teams need to associate business rules, interface conventions, or confirmed expectations with samples. AI can suggest similar cases and missing check items; the explanations it gives cannot replace the actual basis, especially the results of the last execution cannot be used to prove that this time's expectations must be correct.
Scenarios newly added to the model can be kept as suggestions first, and professionals can check whether they actually exist and are worthy of testing before being included in the formal collection. When there is no original basis, it is often more useful to leave the question alone than to fill in a conclusive conclusion. Technical discussions should not infer real customer account status from test data, let alone use demonstration samples as production business results.
A small team can provide a reproducible search process
If you want to pilot such a service, you can start by delivering a search path from the scenario number to the request, response and expected basis around a small amount of test data that is allowed to be used. Have another tester repeat the operation along the path to see if additional inquiries from the original author are still needed. This verification is whether the data really helps the work, not just whether the model can quickly generate longer explanations.
Runhe's report also shows that some knowledge modeling and promotion are still expanding. This article distinguishes between disclosed project verification and subsequent coverage plans based on this, and does not write a scenario that all financial testing tasks are mature. For OPC readers, it is worth studying how enterprise testing work is broken down into specific tasks; this large service provider itself does not belong to the OPC entrepreneurial sample identified in this article.
Extended reading: How model comparison fixes business standards and How AI code checks user tasks, compare the relationship between model performance and real work basis.