Most organisations have a risk register. Many also have a heat map, a scoring scale and a quarterly risk report. Yet one question often goes unanswered: does any of it actually help management make a decision?<\/p>\r\n
Heat maps and qualitative scores are familiar and easy to produce. But when management faces a real question, such as whether to invest, mitigate, accept a risk or shift budget, a coloured cell rarely gives them what they need. Research on how risk management is practised keeps finding the same pattern: risk information gets collected, but it doesn\u2019t reach the decision.<\/p>\r\n
Risk quantification is often presented as the answer, and just as often dismissed as too complex, too data-hungry or too academic. This webinar takes a different view. Quantification is not the goal. Better decisions under uncertainty are. Getting there doesn\u2019t require perfect data, sophisticated models or false precision.<\/p>\r\n
Prof. Dr. Stefan Hunziker (Lucerne University of Applied Sciences and Arts) and Dr. Graeme Keith (Stochastic ApS) bring research and practice together. They show why traditional risk assessment so often falls short and take apart the most common myths about quantification. They also explain how risk information can directly support investment, mitigation, budget and strategic decisions. Nikolai Tsenov (Swiss GRC) moderates the session and closes with a practical answer to the question everyone asks: where do I start? He also gives a brief look at how RiskQuant turns the risk register you already have into a decision basis for the board.<\/p>\r\n
What to expect<\/strong><\/p>\r\n Who should attend<\/strong><\/p>\r\n This webinar is for professionals and leaders in risk management, ERM, finance and controlling, internal audit, compliance and governance. It is also for members of executive management and boards who rely on risk information. It is especially relevant for organisations that want their risk assessment to do more than fill a register: to support real decisions, with a practical first step into quantification.<\/p>\r\n\r\n