Machine learning(ML) is today commonly employed in the Financial Services Sector (FSS) to create various models to predict a variety of conditions ranging from financial transactions fraud to outcomes of investments and also targeted marketing campaigns. The common ML technique used for the modeling is supervised learning using regression algorithms and usually involves large amounts of data that needs to be shared and prepared before the actual learning phase. Compliance with privacy laws and confidentiality regulations requires that most, if not all, of the data must be kept in a secure environment, usually in-house, and not outsourced to cloud or multi-tenant shared environments. This paper presents the results of a research collaboration between IBM Research and Banco Bradesco SA to investigate approaches to homomorphically secure a typical ML pipeline commonly employed in the FSS industry. We investigated and de-constructed a typical ML pipeline used by Banco Bradesco and applied Homomorphic Encryption (HE) to two of the important ML tasks, namely the variable selection phase of the model generation task and the prediction task. Variable selection, which usually precedes the training phase, is very important when working with data sets for which no prior knowledge of the covariate set exists. Our work provides a way to define an initial covariate set for the training phase while preserving the privacy and confidentiality of the input data sets. Quality metrics, using real financial data, comprising quantitative, qualitative and categorical features, demonstrated that our HE based pipeline can yield results comparable to state of the art variable selection techniques and the performance results demonstrated that HE technology has reached the inflection point where it can be useful in batch processing in a financial business setting.