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AI in Insurance: OCR AI in Input Management | Update 2023

Florian Zyprian

AI-driven input management through OCR and NLP

In insurance companies, it has long been nothing new to digitize processes via input management systems. These systems take over the processing of incoming mail up to archiving. The primary goal is to prepare data in a structured manner, which is then passed on to downstream systems, such as an ERP system. However, these tools are often outdated and very expensive. An extension of input management by combining various artificial intelligence (AI) solutions such as automatic text recognition (OCR) and text processing (NLP) is already used for 62 % of customer interactions in insurance companies today [1]. The intelligent OCR uses Indexing and extraction of text fields or whole text sections in documents or emails and increases the accuracy of rule-based approaches by 6 % to 93 %. In addition, insurance companies save time by using intelligent automation solutions such as hyperautomation.

How does AI OCR work?

Automated document processing with OCR technologies


Figure 1. automated document processing with OCR technologies.

The sequence of an automated document processing with OCR is shown in Figure 1. In general, they all follow the same structure:

  1. Input

    The data input (the document) is taken from a database, from one of the front-end systems such as a Robotic Process Automated Bot, an email, or others. More Low-code and no-code providers you will find in our following article.

  2. Preprocessing

    The files are pre-processed to be processed regardless of the file type, the quality of the scan and the number of pages.

  3. intelligent detection

    Neural-based automatic document classification technology enables sorting of documents by type (e.g., driver's license, bank statement, tax form, contract, invoice) and user-defined subcategories (e.g., vendor A invoices, vendor B invoices) by identifying text content and image patterns.

  4. Assignment and categorization

    The neural machine for classification defines a document type and selects a correct document definition for further content processing.

  5. Subject data extraction

    After recognizing certain fields, the structured or semi-structured text is extracted from the document and exported to the target system.

If desired or required, AI OCR enables human review by setting a confidence level threshold. This human feedback helps the AI to learn continuously. The human feedback, also called human-in-the-loop can be provided flexibly and individually via the Document Validation UI be built into each process. If a specified threshold is not reached, a manual check is performed before exporting the data to the target system. The final output of this process can be an XML, JSON, CSV, XLSX/XLS, TXT or HOCR file.

Process automation with hyperautomation in insurance

In view of the pandemic and the resulting economic crisis, it is becoming increasingly important to optimize and stabilize processes in insurance companies. The advancement of automation technologies such as OCR, RPA (Robotic Process Automation) and AI are resulting in economically and technologically advanced process automation solutions - hyperautomation. The goal of many companies is to improve service quality or increase revenue and make existing processes even more robust for the company's digital future. The use of hyperautomation enables the automation of processes beyond rule-based standard applications.

Automatic fraud detection through AI in insurance companies

The insurance industry is increasingly struggling with cases of fraud, which cause billions of euros in damage every year. According to the German Insurance Association, 10 % of claims paid out in Germany go to the accounts of fraudsters [2]. To better detect the fraud attempts, technical solutions are needed that can always adapt to new circumstances and fraud patterns and go beyond rule-based approaches of input management. This is because the error rate there is high and additional manual effort is required. AI and OCR can be used to check claims for conspicuous content patterns and automatically detect anomalies. With the use of AI, an average claim amount of approximately €3,000 and the detection of 1,029 fraud cases, a savings potential of over €3.1 million could be achieved in an insurance company. 

AI in insurance individualizes the customer approach

Individualization and personalization are among the megatrends of the 2020s. Standard solutions do not inspire customers much and the demands for an individual customer approach are increasing. Insurance companies can use this development as a great opportunity for cross-selling and up-selling by using an AI-based solution as support. Based on customer information, individual e-mails can be generated automatically and the quality of communication sustainably increased. In the process, automatically generated texts can no longer be distinguished from manually created texts and the response rate can be increased from approximately 1.5 % to up to 35 %. The AI application allows for automatic learning through new input, closing knowledge gaps and making new connections on its own. Pre-trained language models such as GPT-3 are powerful text generators that independently write coherent texts and are used for successful customer engagement [3]. 

Through AI in insurance Understand documents better

The transfer of insurance documents between insurers, brokers and other partners is largely standardized by BiPRO Standard 430, but not automated [4]. AI processes data in millions of documents and helps employees find cross-selling potential in customer portfolios and save money in contract negotiation and input management. By using AI, content in documents can be retrieved in a structured way. Work steps such as typing, renaming, filing and validating are almost completely eliminated. This makes it possible to process these documents purely digitally, enrich them with known master data and harmonize them across systems. AI software learns to understand and structure information from documents 24 times faster than a human. As a result, insurance companies benefit from faster and more efficient processing of their documents.


[1] Capgemini Research Institute (2020). Smart Money.

[2] Friedrich, S. (2018). Du Lügst! in GDV Positions magazine, issue 3/2018, pages 24-26.

[3] Tan, B., Yang, Z., AI-Shedivat, M., Xing, E. P., & Hu, Z. (2020). Progressive generation of long text. 

[4] BiPRO e.V. (2021). Standard 430. 

Photo by Adrianna Calvo


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