OCTOBER 2022CIOAPPLICATIONSEUROPE.COM9management that they will achieve the expectations and finally work through to reach those results within the organization.Banks have large amounts of data for data science projects, but the levels of security required by law for the data of financial institutions in Europe are a big stopper for the development of new AI and machine learning techniques compared to other sectors. Complying GDPR has been a big challenge for us, we overcome this situation by mapping all the data we use for modelling to the customer consent level and update it on a daily basis, with the counterpart of losing information of customers which are less engaged with the Bank, who don't check or sign their pending GDPR consent contract. Current challenges: AI Ethics, where we'll need to understand if model results are biased in a way, they will trigger economic, social, ethical or other breaches in modern society. Covid-19 challenges where historical data is no longer as relevant as current daily data, and where we need to play a bigger role in helping customers to overcome the crisis. Try always to link and measure your data science projects to strategic business lines and/or to KPIs that are relevant to the decision-making managers or partners in case of startups. If you achieve this in a way that the key people on your organization support your projects, then you'll be able to make real science projects with enough time, people and resources.The most complicated thing for machine learning models is to extract meaningful information from data and simplify results for better outcomes
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