The self-learning AI for seamless straight through processing.

Completely digitalized processes ensure a high level of cost efficiency, no question about it. However, an obstacle along the journey to complete digitalization is still media breaks in the inbox. Simple text recognition (OCR) was able  to achieve at least some success here. The problem: the poor data quality. Aisaac goes two steps further here and provides better and more and more precise records as self-learning AI.

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6 good reasons for Aisaac:

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Straight through processing

Aisaac detects, structures, interprets and classifies records. The AI thereby generates the basis for consistent and efficient straight through processing.

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Self-learning

The machine learning unit from the AI uses different learning procedures (neuronal networks, pattern matching, etc.) to constantly optimize the data quality.

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Neutral to division and context

Thanks to the extensive experience of adesso insurance solutions in the implementation of open interface frameworks, Aisaac can be added to any technical context.

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Creates capacities

Aisaac frees your personnel from time-consuming, purely administrative activities. They can concentrate on complex administrative tasks.

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Human + machine

An experienced team takes care of the implementation. Quality assurance and training of AI so that you can profit from smooth processes.

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Process turbo

Aisaac noticeably accelerates input management and thereby creates the basis for efficient, consistent straight through processing.

Do you require further information regarding Aisaac? You can find our brochure here

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Intelligent input management starts with Aisaac.

Collect, understand, learn - Aisaac works in three steps that repeat in a loop:

1. Collect data
In the first step, Aisaac extracts the relevant text and metadata from a document. If a record is not clearly legible for the AI - for example, the scan is too blurry - correction and filter procedures will automatically be applied to improve the quality of the document.

2. Interpret and classify data
All images, text and metadata are added to the machine learning unit - the heart of Aisaac. The records are inspected according to previously defined or learned characteristics and classified correspondingly. A text with the keyword "Address change", for example, is added to the policy management system. A text that fulfills all criteria of a damage claim is added to the claims management system.

3. Learning from data
Aisaac detects patterns and similarities in documents and therefore learns independently. Furthermore, a sample is provided to the quality assurance department from adesso insurance solutions. If the data has to be corrected, the AI will learn from this as well.

The self-learning AI for seamless straight through processing at a glance:

Aisaac is distinguished by the following:

  • Automatic extraction of structured data from unstructured texts
  • Records are detected and classified, making the following possible: automatic transmission of structured data into further processing systems
  • Best possible detection rates through a combination of machine learning procedures, for example, multi-layer neuronal networks, support vector machines, statistic learning procedures, learning procedures in connection with pattern matching
  • Significant increase in straight throuagh processing rate, for example, when processing claims or invoices
  • Division neutrality of AI permits company-wide use or usage in partial areas

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