8March 2019Model Risk Considerations in New TechnologyPlatformsFinancial institutions are increasingly using new, innovative technology across the product lifecycle to provide more competitive services, grow market share, and increase security. One challenge with the due diligence in selecting new vendors or choosing technologies to develop is fully understanding the technology, data, and analytics underlying these platforms. Specifically, models are often imbedded in the function of many lending and service technology platforms to optimize decision making, increase revenue or market share or control risks. · Loan, deposit or payment processing platforms may include models for assessing credit worthiness, underwriting and pricing or even product recommenders. · Additionally, these platforms often contain models that run in the background to monitor for fraud, cyber-risk and money-laundering.With new advancements in technology platforms, there is a lot going on behind the scenes, whether you are using a robo-advisor or a loan, deposit and/or payment technology platform to expand markets, increase revenue on existing clients, enhance security or reduce fraud. However, these advancements need to be implemented so that they work as intended, which is whyModel Risk Management should be partners in the process of developing or purchasing new technology platforms, as well as IT and data governance, and validate the models in these platforms before they are implemented.Additionally, in many of these platforms machine learning (ML) and artificial intelligence (AI) models are being increasingly utilized given the available computing power and rich transaction-level data sources as well as the volume of variables available from the transaction systems, credit bureaus, and alternative social-media sources. · However, their validation requires advanced analytical skills, in depth testing, and transparency into the drivers of the model (including their business sense, non-discrimination and reliability of relationships). · This is in direct contrast to statements that these are automated machines. Rather the development, validation and maintenance of ML and AI models require more effort, with human experts working alongside the machine.The benefits gained from ML and AI methods should be weighed against the burden of testing required to implement and maintain them.Given the frequent imbedding of models within these platforms, the model risk management function will thoroughly validate any models (e.g., regression, ML or AI models) to ensure that they are reliable and robust as well as implement ongoing testing in production. Pre-production validation will include an array of testing, but these tests provide valuable information as to how the solutions work and their limitations. All models have some level of error, and in a fast-changing environment that model error can lead to an unacceptable level of losses or lost opportunity if not controlled. For example, model development and validation testing should:· Address known data inaccuracies prior to model development and tested in validation, as otherwise the model will be unreliable if built on flawed data.· Analyze of changes in the population profile of customers or transactions over time and how this might impact a model's robustness.By Shannon Kelly, SVP, Director, Model Risk Management, Zions BancorporationShannon KellyIN MYOPINION
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