FROM FUNDING RISK TO PREDICTIVE CONTROL: WINCODE AI SYSTEM FOR PREVENTING FINANCIAL MISMANAGEMENT IN SPORTS

Many sports institutions fail not due to a lack of funding, but due to an inability to convert allocated resources into compliant and sustainable projects. This paper investigates whether an AI-supported system can reduce financial mismanagement by replacing static grant writing with predictive structured control. The study presents the WinCode system, an AI-driven platform consisting of four interconnected modules: MILES for logical project and financial modelling, SCORE for iterative evaluator simulation, EXECUTE for real-time task and risk tracking, and ECHO for auditready reporting and transparency. Empirical evidence is drawn from pilot workshops conducted with Erasmus+ sport project managers and first-time applicants. The results indicate that the application of predictive logic modelling significantly improves funding success rates, particularly for organisations with limited internal administrative capacity. By reframing project design from a speculative process into a controllable and verifiable system, the WinCode methodology supports scalable, transparent, and financially sound implementation of sports projects.