The company's top management uses innovative methods in making management decisions in companies, which can be used to improve the overall business. This author's study was focused on discovering the importance of document archiving, especially if the electronic movement of documents is applied in the company. The conclusion reached by the authors of the study is that there is a strong correlation between the standard and innovative forms of document archiving (control has been established in the company). The author's next conclusion would be that in the business of companies that have introduced financial management and control, electronic movement of documents is basically taking place. In companies that have not introduced the mentioned form of control, the classic flow of documentation dominates. The last conclusion would be that the highest level of security in archiving is achieved in companies that have introduced electronic archiving.

The process of introducing forensic accounting into the regular business processes of companies is connected with key decisions of top management to introduce and establish overall improved control of all parts and sectors in companies. The process of introducing forensic accounting essentially arises from the decision of the company's top management to establish a change in the previous accounting policy, a new one, in which there will be an implemented system of forensic accounting. The process of implementing forensic accounting is a very complex process. With its implementation, it is possible to improve overall management in a very short period of time. Forensic accounting can improve the functioning of all parts of the company, because its application reduces irrational, speculative management, as well as theft and unauthorized alienation of part of the company's assets. Top management's interest in the implementation of forensic accounting should be reflected in the improvement of overall management in the company. The benefits of implementing forensic accounting are numerous, such as: increasing the general security of the entire company's operations, improving the company's business results, reducing business costs in all organizational parts of the company, improving the overall financial reporting of top management from all organizational parts of the company, reducing fraud that can be committed by employees, reducing the alienation of company assets and others. The practical application of the implementation of forensic accounting can very quickly improve the trust of the workers in the actions of the top management in matters of management, and in this way, better overall results of the company's operations can be expected. The work of employees in a company that has implemented forensic accounting is largely connected with the implementation of the innovative IT sector, and it can contribute to the improvement of the overall financial reporting of top management.

The insurance industry is undergoing a significant transformation driven by advancements in technology, particularly machine learning. As insurers seek to enhance operational efficiency, risk assessment, and customer experience, machine learning offers promising applications across various domains, such as underwriting, claims processing, and fraud detection. Despite the potential of machine learning, its integration into traditional insurance practices faces numerous challenges, including data quality, regulatory concerns, and organizational readiness. The aim of this paper is to examine the possibilities and characteristics of the application of machine learning in insurance, in order to determine the machine learning approach that is most often used and that provides the best results. Drawing on insights from systematic literature reviews, the framework will provide a comprehensive understanding of how machine learning can reshape insurance practices. By exploring these aspects, this paper contributes to a more structured and informed approach to implementing machine learning in the insurance industry.

In the rapidly evolving landscape of data-driven business, marketing intelligence has become a cornerstone for organizational success. This entails the systematic collection, analysis, and interpretation of vast data sets, empowering businesses to make strategic marketing decisions. Employing techniques like data mining, machine learning, and sentiment analysis, marketing intelligence uncovers hidden patterns, identifies opportunities, and predicts challenges. A pivotal outcome is enhanced customer understanding through detailed profiles derived from sources like social media, website interactions, and purchase history. This depth of insight allows for the creation of personalized, precisely targeted campaigns, fostering stronger customer relationships and loyalty. Moreover, marketing intelligence facilitates data-driven decisions aligned with overarching business goals, optimizing resource allocation, budgeting, and strategic planning. Utilizing software solutions such as RapidMiner, organizations gain efficiency in data analysis, machine learning, and predictive analytics. Net Promoter Score (NPS) acts as a key metric, gauging customer sentiment and likelihood to recommend products or services. This paper focuses on evaluating customer satisfaction in the insurance sector, aiming to propose enhancements for service quality based on existing research results. Through a comprehensive examination of customer satisfaction levels, the study seeks to provide actionable insights for continual improvement.