There are many computational paradigms that could be used to harness the power of the herd of computers. In financial services, a share-nothing approach could be used to speed up CPU intensive calculations while the hierarchal nature of rollups requires tight synchronization. Some interesting use cases are: In Wealth Management, the SQL approach is traditionally used, but it lacks efficient support of hierarchal structures, iterative calculation, and provides limited scalability. Unlike traditional, centralized scale-up enterprise systems, an in-memory-based architecture scales out and takes advantage of cost-effective high volume commodity hardware that maximizes compute power efficiently. It makes the user experience better by speeding up response time utilizing distributed implementation of calculation algorithms. OData enables DaaS to expose financial data and calculation capabilities. In the insurance industry, in-memory computing was used for Monte-Carlo to estimate the value of life insurance policies. This is a very CPU-intensive task, which requires 2000 cores to build ~1 million simulated policies in 30 minutes (about 25 trillion numbers or 100TB of data), which then aggregates and compresses into 40GB of data for analysis. To speed up CPU-intensive iterative financial calculations, we use a share-nothing approach while the hierarchal nature of rollups requires tight synchronization. Several algorithms that are typical for the financial industry, different approaches on distribution and synchronization, and the benefits of in-memory data grid technologies will be discussed.