Many companies that make decisions about the eligibility of their clients for certain products face unsatisfied clients when the decision is negative. In the event of a workplace accident, an insurance company might determine that the injured worker should resume work instead of providing support and compensation. Such a decision can be challenged by the client, triggering the resolution procedure. Court services are sometimes required to resolve such issues. The company can then decide whether an attorney is required to defend the original decision.
Throughout the years, companies collect data about original decisions, challenged ones, resolution methods, and final outcomes. The use of supervised machine learning can value these data and allow companies to make better decisions that are based on historical results.
The setting of the machine learning experiment involves the final decisions that were confirmed by the judges. Experts' initial decisions may conflict with some of these. Trying to anticipate outcomes with accuracy will therefore lead to systems that are more powerful than humans. Such a system supports accurate decisions as well as client satisfaction. There are also side benefits that can be derived, namely: