Input Management to Insight Management | Insurance

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Input to Insight Management

In insurance companies, digitising processes using input management systems is nothing new. These systems take over the processing of incoming mail through to archiving.

AI-driven input management through OCR and NLP

The primary aim is to prepare data in a structured manner, which is then passed on to subsequent systems, such as an ERP system. However, these tools are often outdated and very expensive. Enhancing 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]. Intelligent OCR uses keywording and extraction of text fields or entire sections of text in documents or emails and increases the accuracy of rule-based approaches by 6% to 93%. Insurance companies also save time by using intelligent automation solutions such as hyperautomation.

Process automation with hyperautomation in insurance

In view of the pandemic and the resulting economic crisis, it is becoming increasingly important to optimise and stabilise processes in insurance companies. The further development of automation technologies such as OCR, RPA (Robotic Process Automation) and AI is resulting in economically and technologically advanced solutions for process automation – hyperautomation. The aim of many companies is to improve service quality or increase sales and make existing processes even more robust for the digital future of the company. 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 that cause billions in losses every year. According to the German Insurance Association, 10% of claims paid out in Germany are made by fraudsters [2]. In order to better recognise fraud attempts, technical solutions are needed that can constantly adapt to new circumstances and fraud patterns and go beyond rule-based input management approaches. This is because the error rate there is high and additional manual effort is required. AI and OCR can be used to check damage reports for conspicuous content patterns and automatically recognise anomalies. With an average claim amount of around €3,000 and the detection of 1,029 cases of fraud, the use of AI enabled potential savings of over €3.1 million to be achieved in one insurance company.

AI in insurance companies individualises the customer approach

Individualisation and personalisation are among the megatrends of the 2020s. Customers are not very enthusiastic about standard solutions and the demand for an individualised customer approach is 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. Customised emails can be generated automatically on the basis of customer information and the quality of communication can be sustainably increased. Automatically generated texts can no longer be distinguished from manually created texts and the response rate can be increased from approx. 1.5% to up to 35%. The AI application allows automatic learning through new input, closes knowledge gaps and independently establishes new connections. Pre-trained language models such as GPT-3 are powerful text generators that independently write coherent texts and are used to successfully address customers [3].

Understanding documents better with AI in insurance

Although the transfer of insurance documents between insurance companies, brokers and other partners is largely standardised by BiPRO standard 430, it is not automated [4]. AI processes data in millions of documents and helps employees to find cross-selling potential in customer portfolios and save money in contract negotiations 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 harmonise them across systems. AI software learns to understand and structure information from documents 24 times faster than a human. This allows insurance companies to benefit from faster and more efficient processing of their documents.

Sources

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

[2] Friedrich, S. (2018). Du Lügst! in the magazine Positionen des GDV, 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). Norm 430.


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