The previously-implemented semi-automated business-used prototype of the daily fund transfer price (FTP) for the mortgage domain was moved to production from business to IT departments of a large Dutch bank and became an IT system under FAM responsibility.
The resulting fully-automated application was built on a consistent technology stack, which includes .NET, Visual Studio, C#, SQL-Server, SSIS package, QRM. The tool is accompanied by the corresponding graphical user interface (GUI) which was developed in ASP.NET.
This project turned out to be very challenging, especially because of: i) the interactions among various bank departments, including IT, FAM, FTP Centre and Mortgage Group; ii) the large technology stack involved. In all these cases we were in the lead.
We were asked by a large Dutch bank to help model the pipeline option for their residential mortgage portfolio. We developed a model and implemented it in the bank's ALM software.
The pipeline of the mortgage portfolio consists of those mortgages that have been offered to clients, but not yet settled. Two groups of contracts exist: those with the option to pay the lower of the offer rate and the actual mortgage rate at the day of property transfer and those without. In both cases the client doesn’t need to wait till the end of the offer period before drawing funds. The timing of the funding needs of the client is considered to be independent of the option value. Other factors like delivery dates of the property and personal preferences are considered to dominate over economic considerations in this case. The choice was made to use historical client behavioral data to compute the probability of drawing funds at a certain period. This choice was made over considering new clients homo economicus and using option theory to calculate the economic value of this phenomenon.
Another factor we considered is the fact that the customer has the option not to accept the mortgage offer from the bank and seek funding elsewhere (i.e., not all offers convert to actual sales). We call this the acceptance option. Based on historical data the fraction of those offers that convert to sales are estimated for each period.