Fannie Mae (The Federal National Mortgage Association) is a US government-sponsored enterprise that operates in the secondary mortgage market. It purchases and guarantees mortgages from lenders, such as banks and other financial institutions, securitizes them, and then sells them back into the secondary mortgage market as mortgage-backed securities. This provides market liquidity and helps stimulate activity. In 2018, Fannie Mae was ranked 21st on the Fortune 500.
Operations form the core of Fannie Mae’s business and ensure the company’s successful performance. They are required to soundly, safely, and speedily execute the processes necessary to support Fannie Mae’s business functions while meeting customer user experience demands.
In 2008, Fannie Mae was deeply affected by the US housing crisis and the company was placed under government conservatorship. This experience, combined with rapid and unpredictable consequences, led the company to understand its need for a strategic planning software or a digital tool that could provide deep insight into the organization’s processes, improve process handling, and assist in preparing for various future challenges. The resulting simulation model would enable Fannie Mae’s specialists to:
- Test and validate new processes.
- Quantify potential process improvements.
- Identify possible bottlenecks.
- Connect processes for larger-scale insights.
- Evaluate business resiliency strategies.
The company decided to begin with a quick proof-of-concept project. To achieve this, they focused on the processes of trade confirmation and trade assignment.
Trade confirmation entails the comparison of the trade agreement from both counterparties, verification of the accuracy of its execution, and strict SLA fulfillment.
Trade assignment is a three-party agreement: a selling party assigns their obligation to Fannie Mae who then assigns the trade to a third party or to itself as a buyer. Trade assignment must also go through a trade confirmation process.
It is challenging to get a holistic view of these two processes. They can vary by both the type of trade and the level of automation – from fully automated to completely manual and dependent on skilled analysts. Fannie Mae wanted their project management optimization model to fulfill the following tasks:
- Resource optimization in project management (number of analysts required to accomplish all processes and to identify possibilities for multitasking)
- Preparation for trade volume increases (what-if scenarios concerning the capacity needed in different cases)
- Sensitivity analysis and resilience testing (identifying potential bottlenecks and the analysis of possible process changes)
Agent-based modeling allowed the trade assignment and confirmation processes to be captured accurately and both the trade (task) and the analyst (resource) aspects were modeled. The analysts were modeled because they are necessary for manual processing. The Poisson arrival rate was taken for trades, and the triangular distribution for the manual processes.
The choice of AnyLogic as a simulation platform was based on a number of AnyLogic advantages. AnyLogic is flexible and powerful strategic planning software that made it possible to build a model in 90 days, from idea to final product. Furthermore, the visualization capabilities of AnyLogic enabled Fannie Mae to make the model easy to understand for the finance professionals who were the target users for the tool.
The main inputs for the risk analysis model are trade booked and trade assigned. In addition, variables can be set for trade exceptions: delay time by type and the proportion rate. System users can also define the number of analysts available to process different types of tasks at both headquarters.
Running the strategic planning model, the user can see the process overview visualization. The results are shown in charts, tables, and graphs. They show how many trades are processed over time and the capacity of different types of analysts. Running what-if scenarios makes it possible to see what will happen in various situations, such as peaks in trade volume of varying duration. The model can also suggest how many and which kind of analysts the company needs to deal with different trade volumes.
This proof-of-concept project showed that simulation is effective and helpful for financial operations management optimization. The completed model can help identify potential bottlenecks, simulate the effects of extreme cases (like fluctuations in trade volume), and propose workforce and project management optimization.
Watch the video of John A. Coaster presenting this case study at The AnyLogic Conference, or download his presentation.