Difficulties in explaining machine learning (ML) models is causing concern as banks look to the technology for default risk analysis, according to market participants. "Many different types of'black-box' models have been developed out there even by banks claiming that they can accurately predict mortgage defaults. This is only partially true," said Panos Skliamis, chief executive officer at SPIN Analytics in an email. "[These models] usually target a relatively short-term horizon and their validation windows of testing remain actually in an environment too similar to that of the development samples. However, mortgage loans are almost always long-term and their lives extend to multiple economic cycles, while the entire world changes over time and several features of ML models severely influenced by these changes of the environment," he said. The black box problem refers to the inability of an end user to understand the processes occurring between input and output in a machine learning model.