全球風險管理專業(yè)人士協(xié)會(GARP)致力于為風險管理條線上的各級人員，包括各大金融機構(gòu)的風險從業(yè)者和監(jiān)管機構(gòu)人員提供風險教育和最新行業(yè)資訊。金程網(wǎng)校將持續(xù)轉(zhuǎn)載“GARP Risk Intelligence”系列文章，介紹科技、企業(yè)文化與治理、能源等領(lǐng)域?qū)Σ僮黠L險、信用風險、市場風險和資產(chǎn)負債管理的影響。讓我們一起全面認識風險，防范風險，化解風險。

　　人工智能的快速發(fā)展可能推動銀行業(yè)轉(zhuǎn)型。然而，麥肯錫風險部門專家警告，這并非一個無風險事件。

　　如果基于人工智能的機器學習模型未能被適當?shù)卦O(shè)計和驗證，它們固有的復雜性、基于模型作出的決策和它們所使用的大量數(shù)據(jù)會導致各種意外，或是難以控制的風險后果。例如，銀行可能在無意間違反反歧視法，或未能遵守反欺詐或反洗錢的相關(guān)法規(guī)，或者盲目持有高風險或無利可圖的投資頭寸。但是，麥肯錫公司確信解決方案近在眼前。

　　麥肯錫公司合伙人Derek Waldron和幾位同事發(fā)表的文章《機器學習和人工智能去風險(Derisking Machine Learning and Artificial Intelligence)》為模型風險管理提供了支持：“機器學習的額外風險是可以被減輕的。特別是在金融領(lǐng)域，可以根據(jù)監(jiān)管機構(gòu)的要求對現(xiàn)有的驗證框架進行針對性修改。簡而言之，我們需要降低模型風險。”

　　1、與美聯(lián)儲模型風險管理監(jiān)督指導意見(SR11-7)保持一致

　　The de-risking steps proposed by McKinsey would apply to the validation frameworks already employed by banks and supervisors – specifically, those consistent with the SR 11-7 guidance of the Federal Reserve Board and Office of the Comptroller of the Currency. Banking organizations must be attentive to possible adverse consequences of all – not just machine learning – models. The regulators also call for active model risk management, including effective validation.

　　He notes that overall model risk governance and validation processes are “owned” by the chief risk officer and, in that person's organization, the head of model risk management. Under guidance, there potentially can be hundreds of individual validators who then need to go through each of the steps necessary to validate the models. Many of these individuals would bring relevant technical skills.

　　2、六個模型驗證新因素

　　Specifically, McKinsey is proposing the addition of six elements to the validation process: model interpretability, model bias, feature engineering, hyperparameters, production readiness, and dynamic model calibration.

　　The firm also advises the modification of 12 elements, such as in areas of modeling techniques and assumptions, that are part of traditional validation frameworks.

　　“The good news is that banks do not need to reinvent a whole new validation framework for this,” Waldron says. “The added risk can be mitigated with some very well targeted enhancements to the existing frameworks, with six specific new elements.”

　　In the case of algorithmic bias, McKinsey suggests the development of “challenger” models that use alternative algorithms to benchmark and ultimately correct model performance.

　　3、首先需要定義"公平"

　　To address instances of possible bias against groups or classes or people, McKinsey advises that banks first decide what constitutes fairness for a specific model, and whether that would require demographic blindness, demographic parity, equal opportunity, or equal odds. Validators then need to decide whether developers have taken the necessary steps to ensure fairness.

　　The article explains that models can then be tested for fairness and, if necessary, corrected at each stage of the model development process, from the design phase through to performance monitoring.

　　Feature engineering is the process of creating and manipulating predictors or predictor variables, which guide machine learning models so that an effective predictive model is produced. Careful validation is important, as this is a process that can go terribly awry. The article notes that auto-machine learning, or AutoML, packages, which are designed to automate feature engineering, generate “large numbers of complex features to test many transformations of the data. Models produced using these features run the risk of being unnecessarily complex, contributing to overfitting.”

　　Waldron says that better validation of the model's feature engineering could have readily picked up the miscalculation. “Feature engineering is often much more complex in the development of machine learning models than in traditional models,” Waldron explains. Thus, with AutoML, ”there is a risk that transformations which appear predictive at first may in fact just be overfitting the data.”

　　4、及時行動

　　To facilitate effective validation processes involving machine learning models and, in particular, feature engineering practices, McKinsey recommends the creation of a policy about how much supporting rationale may be required from each predictive feature in their machine learning models.

　　Waldron acknowledges that there are challenges in addressing a range of validation issues. He notes that in a McKinsey survey last year of model risk management leaders, 50% believed that insufficient technical talent was a top challenge for managing model risks that are amplified by machine learning and artificial intelligence. Clearly, risk management executives are concerned about their ability to identify staff with the requisite skills.

　　At the same time, Waldron stresses the importance of banks taking steps sooner rather than later to improve their validation processes, to help ensure the accuracy of machine learning models.

　　“Only two or three years ago, the use of machine learning and artificial intelligence was more theoretical and in the future,” the McKinsey partner notes. “Today, we are seeing leading banks being confronted with pipelines of machine learning and artificial intelligence models that need to be validated, so this is really a problem in the here and now that we expect to see continue to grow.”

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