Supervised learning is ideal when data is available, but the algorithm is unknown or missing. Supervised ML methods include random forest trees, decision trees, regression and neural networks. They’re quite often used to find patterns or “profiles” of potentially improper transactions and risk-driving variables.

当数据可用，但算法未知或缺失时，监督学习是理想的。监督机器学习方法包括随机森林树、决策树、回归分析和神经网络。它们经常被用来寻找潜在不正当交易和风险驱动变量的模式或“概况”。

Unsupervised learning, on the other hand, is ideal when there’s less information about the risks, but you want the data to help define itself by grouping like events (or transactions) together. This can be particularly helpful in fraud detection when you’re looking for anomalies or patterns in data without applying any preset rules.Techniques that can be used in unsupervised learning include K-means clustering and Apriori algorithms. 

当关于风险的信息较少时，但希望数据通过将类似事件（或事务）分组在一起来帮助定义他们自身时，无监督学习是理想的，。在不应用任何预设规则的情况下查找数据中的异常或模式时，这对舞弊检测特别有用。可用于无监督学习的技术包括K均值聚类和先验算法。

Reinforcement allows a user to decide the best action based on the current state and learned behaviors that maximize the rewards. This approach is often used in robotics where the computer trains itself continually using trial and error. The machine learns from experience and tries to capture the best possible knowledge to make accurate business decisions. In fraud detection, reinforcement techniques can be helpful with some of the necessary data extraction and cleanup required to prepare data for analysis, for example.

强化允许用户基于当前状态和学习行为来决定*佳行为，从而*大化奖励。这种方法通常用于机器人技术，其中计算机不断地使用试错法训练自己。机器从经验中学习，并试图获取尽可能好的知识，以做出准确的业务决策。例如，在舞弊检测中，增强技术有助于准备分析数据所需的一些必要的提取和清理。

Transfer learning focuses on storing knowledge gained while solving one problem and applying it to a different, but related, problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. Transfer learning is good when problems are similar, the time to train a model is limited and results are needed fast. Bayesian networks and Markov logic networks are effective transfer learning methods. In an anti-fraud context, transfer learning can help uncover conflicts of interest by finding hidden patterns and relationships, such as in an unauthorized employee and vendor relationship. 

转移学习侧重于存储在解决一个问题时获得的知识，并将其应用于不同但相关的问题。例如，学习识别汽车时获得的知识可以应用于识别卡车。当问题相似、训练模型的时间有限且需要快速获得结果时，转移学习是很好的。贝叶斯网络和马尔可夫逻辑网络是有效的转移学习方法。在反舞弊环境中，转移学习可以通过发现隐藏的模式和关系（如未经授权的员工和供应商关系）来帮助发现利益冲突。

Finally, there’s deep learning. According to IBM, deep learning attempts to mimic the human brain — albeit far from matching its ability — enabling systems to cluster data and make predictions with incredible accuracy. Deep learning is ideal when there’s lots of unstructured time series data or data that’s not independent. Deep learning drives many AI applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep-learning technology lies behind everyday products and services, such as digital assistants, voice-enabled TV remotes and credit card fraud detection, as well as emerging technologies, such as self-driving cars. 

*后，还有深度学习。根据IBM的说法，深度学习试图模仿人脑——尽管远不及人脑的能力——使系统能够对数据进行聚类，并以令人难以置信的精度进行预测。当存在大量非结构化时间序列数据或非独立数据时，深度学习非常理想。深度学习推动了许多人工智能应用和服务，这些应用和服务可以提高自动化，在不需要人工干预的情况下执行分析和物理任务。深度学习技术是日常产品和服务的基础，如数字助理、语音电视遥控器和信用卡舞弊检测，以及自动驾驶汽车等新兴技术。