8OCTOBER 2021IN MY OPINIONRisk and Recommended Controls for Models and Artificial Intelligence SystemsBy Eric Bonnell, SVP, Manager - Technology and Asset Risk, Atlantic Union BankAs insurance companies look to incorporate modern technology into their business plans, it is important to understand the risks and expected controls to protect maintaining models and Artificial Intelligence (AI) systems. There are potential consequences of not having sound models on which to base decisions. To address this, you must have a good understanding of the assumptions, rules, and data used within the models that make up artificial intelligence systems.Virtual simulation of real-life events is a tricky concept. In theory, the number of variables for which you would account for within a perfect system would be infinite. As this is impractical, AI systems strive to reasonably operate within a degree of certainty by working with enough variables to cover the most impactful considerations. As this is an imperfect process, it is important to note assumptions to point out any significant variation due to the amount of historical data, the scope of data to be used, the level of certainty of the data, and how the data will be interpreted when the simulation is executed. For example, how can a morbidity rate be simulated within a degree of certainty? It is important to have enough historical data to cover the potential values (e.g., age at death), a reasonably accurate distribution of likelihood (e.g., how many died at each age and cause to predict likelihood and timing), as well as the health conditions within the populations, the areas of concentration of each condition (i.e., location or other specific conditions under which a particular condition is likely), and many other variables as are understood to provide meaning and guidance within the model.Know Your AssumptionsAs actuaries can tell you, multivariable model analysis can be very complex. Understanding assumptions about the data and rules will assist in the prudent usage of models, including:· The definitions of each data type and how well it represents the assigned variable or condition· what key variables will be considered fixed or absent in the calculations· the assumed level of quality of data and measurements· the limits and potential outliers within the measured data and resultant model calculationsEstablish Model RulesConsistency and transparency are paramount to build a reliable model. Understanding the variables used, the conditions affecting each variable, and the calculations that will operate on each variable are all important to correctly operating the model and upholding the expected level or range of certainty of the results.Eric Bonnell
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