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Certainly, business performance relies on consistent decision making. Decision Management technologies achieve just that. While Business Owners typically take responsibility for putting together strategies, data insight can add precision to these automated decisions. Indeed, Predictive Models like a credit score or willingness to repay constitute invaluable inputs to business rules in the Financial Services industry. On one hand, Business Analysts can collaborate with Data Scientists to brings these models to good use. On the other, Machine Learning ‘made easy’ can empower them.
In this webinar, we will cover several techniques that add precision to automated decisions. In other words, we will explore how predictive models improve the quality and performance of decisions. The first approach is to streamline the collaboration between Data Scientists and Business Analysts. There are several aspects to consider, ranging from the management of the variable library, to the actual handshake, to the testing and validation of the models.
Subsequently, we will explore in more details how these Business Analysts can take ownership of model development. The objective is not to replace the Data Scientists and their sophisticated models. Rather, it is to complement them with expertise-driven models that Business Analysts can quickly develop on their own.
This webinar is hosted by Carole-Ann Berlioz, Chief Product Officer at Sparkling Logic
