This final thesis was created with the aim to search the author's business plan, to explore its feasibility, reality and viability. Main goal of this thesis shoul bet he complex evaluation of the overall business plan. In the first part we can find a theoretical basis of thesis, there are key words and con-cepts and their thorough clarification. Complete and complex theory of business plan is not fully explained in this thesis, there are only parts, which are necessary as a relevant basis for this particular thesis are mentioned. The second part is based on the processing of the business plan itselves, it's detailed elaboration, analysis of individual parts, the processing of the results and findings of these revenues and, finally, clear and comprehensive business plan on the chosen topic. The conclusion provides an overall assessment of the project, evaluation of feasibility and reality and there is also the author's opinion on the entire project and its other options or possible opportunities.

Hantering av ostrukturerade och fysiska dokument inom vissa områden, såsom finansiell rapportering, medför betydande ineffektivitet i dagsläget. Detta examensarbete fokuserar på utmaningen att extrahera data från ostrukturerade finansiella dokument, specifikt balansräkningar i svenska årsredovisningar, genom att använda en AI-driven pipeline. Syftet är att utveckla en metod för att automatisera datautvinning och möjliggöra förbättrad dataanalys. Projektet fokuserade på att automatisera utvinning av finansiella poster från balansräkningar genom en kombination av Optical Character Recognition (OCR) och en modell för Named Entity Recognition (NER). TesseractOCR användes för att konvertera skannade dokument till digital text, medan en BERT-baserad NER-modell tränades för att identifiera och klassificera relevanta finansiella poster. Ett Python-skript användes för att extrahera de numeriska värdena som är associerade med dessa poster. Projektet fann att NER-modellen uppnådde hög prestanda, med ett F1-score på 0,95, vilket visar dess effektivitet i att identifiera finansiella poster. Den fullständiga pipelinen lyckades extrahera över 99% av posterna från balansräkningar med en träffsäkerhet på cirka 90% för numerisk data. Projektet drar slutsatsen att kombinationen av OCR och NER är en lovande lösning för att automatisera datautvinning från ostrukturerade dokument med liknande attribut som årsredovisningar. Framtida arbeten kan utforska att förbättra träffsäkerheten i OCR och utvidga utvinningen till andra sektioner av olika typer av ostrukturerade dokument. / The persistence of unstructured and physical document management in fields such as financial reporting presents notable inefficiencies. This thesis addresses the challenge of extracting valuable data from unstructured financial documents, specifically balance sheets in Swedish annual reports, using an AI-driven pipeline. The objective is to develop a method to automate data extraction, enabling enhanced data analysis capabilities. The project focused on automating the extraction of financial posts from balance sheets using a combination of Optical Character Recognition (OCR) and a Named Entity Recognition (NER) model. TesseractOCR was used to convert scanned documents into digital text, while a fine-tuned BERT-based NER model was trained to identify and classify relevant financial features. A Python script was employed to extract the numerical values associated with these features. The study found that the NER model achieved high performance metrics, with an F1-score of 0.95, demonstrating its effectiveness in identifying financial entities. The full pipeline successfully extracted over 99% of features from balance sheets with an accuracy of about 90% for numerical data. The project concludes that combining OCR and NER technologies could be a promising solution for automating data extraction from unstructured documents with similar attributes to annual reports. Future work could explore enhancing OCR accuracy and extending the methodology to other sections of different types of unstructured documents.

The aim of the Master's thesis is a comprehensive analysis of the company's financial health over a certain period. In the theoretical part of the work I describe the methods of analysis from basic ones to more complex assessment models. In the practical part, I apply the acquired knowledge directly to the construction company with using its accounting documents. The financial analysis consists of analysis of absolute, differential and proportional company's indexes, which are followed by comparison of the company and industry. Last part focuses on the credit-worthy and bankruptcy models of financial situation assessments as well as their final comparison. At the end, I summarizes the financial situation by using methods and propose measures to improve the future development.