In your company, contracts have to be reviewed regularly and with a high manual effort. In most cases, the content to be reviewed is similar. The problem is that every contract is worded and structured differently, which makes automation impossible.
In your company masses of correspondences have to be processed and allocated every day. Each mail must be analysed at great expense before the next step can be taken. It is quite possible to assign certain categories, but the problem is that similar facts are formulated very differently, which apparently makes it impossible to automate this process.
In your company, documents have to be reviewed regularly and with a high manual effort. In most cases, the content to be reviewed is similar. The problem is that every document is worded and structured differently, which makes automation impossible.
In our library, you can define content for which new incoming documents are to be analysed against. semantha® finds the passages based on their meaning, regardless of the choice of words and without any “training” with your data. Thus, relevant contents of a document are found quickly and efficiently.
Comprehensive information analysis is becoming increasingly important to reduce risks and improve investment management.
The increasing number of work instructions means that bank advisors can only find important content with great effort, as many specifications are not sufficiently structured. Finding subject areas in unstructured texts therefore offers the necessary added value to find & comply with the relevant specifications.
Regulations on bank contracts and guidelines are subject to change. Use semantha® to identify all affected contracts and speed up your processes.
Harmonization of company-wide regulations, such as company agreements, work instructions & SOP’s across different locations
With semantha® we have developed an AI tool that overcomes the weaknesses of conventional matching tools. The application does not compare whether certain terms occur in documents, but reads the documents at the level of meaning. And it does so within a few moments.
Your company has large masses of unstructured text documents. One of the daily challenges is to search these documents for specific content, whereby normal search functions only offer a simple “keyword search” that does not lead to the desired results. This is particularly due to the fact that, on the one hand, it is not known exactly how a topic was formulated and, on the other hand, the same topics are also formulated quite differently in the various documents.
In your company, many procedures on the same topic are processed. Despite always having similar presentations in the pleas and repetitive correspondence with companies, insurers and clients, these documents have to be read and analysed at great expense. Although content can be divided into certain categories, the problem is that similar issues are formulated in completely different ways, which makes it seem impossible to automate the processes.
You have to reply to numerous pleas, such as lawsuits or statements of defence, and repeatedly come across similar submissions. In most cases, you can use excerpts from past responses as a basis for the new plea. However, the classification of the individual submissions of the newly received pleas is the biggest challenge.