我對可解釋性的三個思考：可解釋資訊，合規與風險權衡，詐欺機率分數

• 思考#1：可解釋性的需求，以利人類翻譯資訊
• 思考#2：可解釋的模型和法規，不可解釋的模型風險低
• 思考#3：可解釋性，給詐欺機率分數，然後可以快速進去看問題在哪裡

思考#1：可解釋性的需求，以利人類翻譯資訊

Fraud experts need for this task to have access to the right and interpretable information for each investigated operation in order to justify the suspicion and the resulting action.

human tasks create the need for model interpretability in order to treat each alert or understand globally the evolution of fraudsters’ behavior.

思考#2：可解釋的模型和法規，不可解釋的模型風險低

Intrinsically interpretable models, such as ..... , are characterized by their transparency and by a self-explainable structure. They are generally applied for use cases with legal or policy constraints (Zhuang et al., 2020), but they may well be not accurate enough for tasks such as fraud detection, which have high financial stakes. This explains why more accurate black box models look appealing as soon as a post hoc interpretability method is applied to provide explanations on either how they work or on their results.

Among these methods, some, called post-hoc specific, are specific to a type of model. ..... . The main disadvantage of the latter is that their use is restricted to a single type of model and it is therefore complicated to compare performances and explanations of several different models.

思考#3：可解釋性，給詐欺機率分數，然後可以快速進去看問題在哪裡

First, an anti-fraud software, carried by the publisher Bleckwen, is developed for instant cash transfer fraud, characterized by high operation frequencies and limited human involvement. This software is based on the improvement of a black box scoring model (XGBoost), resulting in a fraud probability score, completed with a local interpretative overlay: all operations over a given optimal threshold are suspended and must be investigated.

1. Weerts H, Ipenburg W and Pechenizkiy M (2019) Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models. Available at https://arxiv.org/pdf/1907.03334.pdf