Development of a Web Application for Risk Management

With the increase in complexity of modern business operations, effective risk management is one of the key factors in ensuring the success of organizations. The integration of software applications has revolutionized the way companies identify, assess, and mitigate risks. This study deals with the design, development, and deployment of a comprehensive Risk Management Tool. The Web Application was implemented in Orbyta Tech with the objective of providing a better tool with respect to the ones already on the market. Current solutions often focus on specific risk domains and historical data, making it difficult to adapt to different organizational structures and predict risks in real-time. Our software is based on real-time data and machine learning to identify risks and trends. The tool offers customization and an intuitive interface allowing a pleasant User Experience. Our Risk Management Tool is composed of different common sections and modules. This was possible thanks to the Software as a Service (SaaS) model, which is typically based on a multi-tenant architecture. The Customer has the possibility to select one or more modules by starting a subscription. The Dashboard section displays statistics, allowing users to filter by date. The Project Management section is composed of the elements necessary for registering a Project in the application. In the Risk Management section, users can create, modify, and delete Risks, Causes, Impacts, Risk Categories, Key Risk Indicators, Risk Breakdown Matrix Entries, and Mitigations. These features offer comprehensive control over risk-related data, including searching, filtering, and Excel exporting capabilities. The Loss Events History section contains the Loss Events table for managing the company’s previous damaging events. The additional modules that were implemented as a start are Data Loss Prevention and Asset Loss and Logistics Risk Management. The former’s objective is to support the user in managing and mitigating Cyber-related risks, especially the ones related to data losses and data breaches. The latter, on the other hand, has the goal of managing risks that may impact the transportation (or storage) of goods by using risk assessment founded both on external and internal factors. It also gives the possibility to manage warehouses and shipments and see when a transit crosses a "Dangerous Zone". The users of this application are Risk and Project Managers, but also IT and Logistics Unit operators who have different roles and authorizations. The ABP.io framework provides a robust layered architecture and maintains adherence to Domain Driven Design best practices. The C# back-end with Entity Framework Core as the database provider accommodates business logic with a code-first approach, while the Angular front-end offers an intuitive interface. Machine learning was incorporated into the application using ML.NET. Part of the study focused on researching useful and accurate data sources. Forecast Algorithms were used to show trends of risk events based on past and present data. Classifier Algorithms were used to categorize events into predefined classes and perform predictions on newly inserted user data. The Agile methodology guided the development process, ensuring flexibility and quality. Lastly, the application was tested by adopting an Agile testing approach and deployed with Azure DevOps. In conclusion, this study’s aim was to emphasize the importance of effective risk management software and provide a powerful yet simple solution.