Companies across the Financial Sector are using AI and Machine Learning to model customer behaviors, avoid risk, and streamline critical business processes. Consequently, building and testing Machine Learning models has become an important discipline for many financial institutions.<\/p>\n
Traditionally, the success of Machine Learning models, and often AI algorithms, is measured by key metrics that evaluate how consistent, accurate, and reliable these tools are. In an Enterprise Setting these metrics alone are rarely sufficient. However, before we dig deeper into this discussion, lets level-set some basic concepts:<\/p>\n
What is a Machine Learning model? <\/strong>Loosely speaking, a Machine Learning model is an algorithm that has been trained on a subset of data to recognize and reproduce certain patterns.<\/p>\n What are the metrics used for?<\/strong> Metrics are used to evaluate the performance of these models; the metrics used depends on the desired business outcome (R^2 or rMSE) or the distribution of the data (F1-Score vs. Accuracy). This is where many validation processes stop. Choosing the right metric is important, but usually not enough. In fact, successful machine learning models share four common characteristics:<\/p>\n Successful models adhere to security and privacy regulations<\/strong><\/p>\n Security and privacy guidelines are crucial for financial institutions. Not only do businesses need to protect their data, but they are federally mandated to safeguard the Personal Identifiable Information (PII) of their clients and customers. Likewise, AI applications need to be compliant with security regulations.<\/p>\n Because of those security demands, good models for security and privacy regulations aren\u2019t necessarily quantifiable through metrics. Instead, these models:<\/p>\n They apply to a clear business need<\/strong><\/p>\n Accurate performance metrics are critical to a model\u2019s success, but you can only get an idea of what those metrics are when you have clearly defined business goals.<\/p>\n More specifically, clearly defining business goals in terms of a Machine Learning model means formulating the right goals and ensuring that they are\u00a0measurable\u00a0<\/em>and\u00a0achievable in order to define<\/em> what accuracy means for your models.<\/p>\n To clarify business needs along those lines, successful projects usually ask the following questions:<\/p>\n After answering those questions, the performance metrics and the level of accuracy in a given model can serve those goals, rather than serving some abstract conception of effectiveness.<\/p>\n They are fundamentally robust<\/strong><\/p>\n Machine Learning models are a simplified version of the reality. By consequence, metrics calculated by ML models do not measure that reality, they measure how well the models are performing on a very specific set of data on a very limited subset of variables that might not affect the outcome in real life.<\/p>\n Because of that, high performing Data Science teams focus on building robust models. This typically involves:<\/p>\n\n
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