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Natural Language Processing (NLP)
Natural Language processing deals with how to recognize patterns in natural, unstructured text. Think of structured text as data in a database or excel table, for instance a register of names. By unstructured information we mean text in emails, documents, manuals etc. The term natural highlights that the text data has been generated by a human for another human.
The last years have seen tremendous improvements with regards to the quality of pattern recognition in unstructured data. The reason for this is next to hardware improvements mainly a group of algorithms, which go by the name of neural nets or deep learning. A key feature of these approaches is that given enough training data, they form their own set of rules in order to achieve a certain goal. This way millions of implicit rules may be defined to successfully recognize even rather complex patterns.
Criteria to discover attractive process automation projects, where textual information play a crucial role:
In our experience, only by combining knowhow of internal operations with natural language processing expertise, projects can be framed well. Feel free to approach us with questions, especially whether we deem your project to be technically feasible.
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Use Cases in NLP
Analysis of handwritten documents

#machine vision #nlp #financial industry

Handwritten documents can be read out and prefilled using machine learning algorithms. By further input, these documents can be edited again, which again benefits the accuracy of the algorithm.

#machine vision #nlp #financial industry

Handwritten documents can be read out and prefilled using machine learning algorithms. By further input, these documents can be edited again, which again benefits the accuracy of the algorithm.

In many industries, documents are filled out in handwriting by own employees or partner companies. These handwritten documents often have to be typed in order to process them further.

With Machine Learning algorithms, patterns in handwritten documents can be analyzed. The more documents are available and the more uniformly the documents are filled out, the better the algorithm recognizes the contents of the handwritten document.

The pre-filled documents are transferred to an interface and can be edited again by employees. The algorithm learns new patterns and can apply them to new cases.

Fraud detection in claims processing

#machine vision #nlp #financial industry

Neural networks are used to detect and filter out patterns of fraud cases. Conspicuous damage reports are reported to claim handlers and checked manually.

#machine vision #financial industry

Neural networks are used to detect and filter out patterns of fraud cases. Conspicuous damage reports are reported to claim handlers and checked manually.

Customers send insurance claims reports as text documents or pictures. Fraudsters assume that particularly low-value damages are not thoroughly examined.

Neural networks are used to identify and filter out patterns of past fraud cases or accumulations of conspicuous, current damage reports.

The selected damage reports are reported to the claim handlers and checked manually by insurance fraud specialists.

Text analysis customer service

#nlp #ecommerce #travel

Customer service agents enter information on complaints or complaints in forms. Information from these clusters can flow into the optimization of business processes.

#nlp #ecommerce #travel

Customer service agents enter information on complaints or complaints in forms. Information from these clusters can flow into the optimization of business processes.

Customer service agents enter valuable information on customer complaints in forms. This information is usually not available in a structured way and can therefore only be analyzed inadequately.

Machine Learning algorithms analyze the text and cluster the information. With an increasing number of examples, the algorithm learns to recognize patterns and classifies the information independently.

The clustered information can be analyzed and integrated into business processes such as website design, service, marketing or logistics.

Analysis of patient records

#nlp #health

Electronic patient records can be evaluated using machine learning algorithms. This automatically provides doctors with suggestions for diagnoses and therapy options.

#nlp #health

Electronic patient records can be evaluated using machine learning algorithms. This automatically provides doctors with suggestions for diagnoses and therapy options.

In the health sector, patient records are increasingly standardized (electronic patient records), so that they can be evaluated and are uniformly available for patients, doctors and health insurance companies.

Patient records can be evaluated using Machine Learning algorithms and patterns such as symptoms of diseases and side effects of drugs can be identified at an early stage.

Doctors automatically receive suggestions for diagnoses or therapy options and can use their personal impression to specify this information and determine the diagnosis or therapy.

Generation of standard documents

#nlp #production #logistics

Durch Spracheingabe und Machine Learning Algorithmen lassen sich Standarddokumente klassifizieren und automatisch ausfüllen. Dieses Dokument kann im Nachhinein kontrolliert und editiert werden.

#nlp #production #logistics

Durch Spracheingabe und Machine Learning Algorithmen lassen sich Standarddokumente klassifizieren und automatisch ausfüllen. Dieses Dokument kann im Nachhinein kontrolliert und editiert werden.

In the logistics or health industry, employees are on the move, but have high documentation requirements. These are usually standardized documents such as CMR bills of lading or FIATA carrier documents, but also internal standard documents.

The employee can record all relevant information via a language interface (e.g. an app). The Machine Learning algorithm classifies the information and inserts it into the corresponding input fields.

The software generates a completed document with all information stored by voice. The user can control and edit the document in the office or in the app.