Unlike e-commerce and online advertising companies, recommender systems are a relatively new concept in the financial services industry. Several additional aspects of financial services industry may not exist in other domains. First, there are strict privacy guidelines that govern the usage of customer transaction data in addition to considerations such as banking regulations and brand reputation. Second, financial institutions have only recently started to deploy the type of large-scale distributed computing infrastructure such as Hadoop needed to build and serve recommendations to millions of customers. In this talk, we will share our experience of building one of the most sophisticated recommendation platforms in the financial industry, by blending different recommendation algorithms while adhering to the above principles, as well as incorporating our legacy and newer (Hadoop) infrastructure into the end-to-end recommendation platform architecture. We will discuss how we use customer purchase history to derive meaningful insight and deliver merchant and other offers, while dealing with cold-start, customer preferences and other issues.