Abstract: The present disclosure describes devices and methods of providing a technology environment for analyzing unstructured data to generate structured data. A set of electronic documents, each electronic document associated with a type of product, may be accessed. A data instance for each of the documents may be generated. The data instance may include a plurality of data fields that are based on the type of product. The electronic documents may be analyzed to identify values for each of the plurality of data fields. Analyzing the electronic documents may comprise applying a respective character recognition algorithm to respective electronic documents, and assigning a confidence factor to each of the values. The data instances comprising the values for each of the plurality of data fields may be stored in a second database.

Abstract: Techniques are described for automatic creation of optimal auditable entities (AEs) using a machine learning (ML)-based clustering model. The clustering model, when executed on one or more computing devices within an audit system of a company, is configured to automatically cluster the company's business processes into AEs based on similarity analyses of business process attributes. More specifically, in some examples, the clustering model ingests business processes and their corresponding attributes from a database, automatically clusters together business processes to achieve maximum intra-cluster similarity scores, and outputs the final clusters as model AEs. The resulting model AEs may be used as functional units for internal audits of the company's business processes. The resulting model AEs may improve audit efficiency due to the model AEs including only highly similar business processes.