We are very happy to be working with one of the leading banks in the field of food and agri worldwide. Rabobank is an international financial services provider operating on the basis of cooperative principle serving approximately 8.8 million clients around the world.
The balance sheet risk of the bank is managed by the ALM team. Ugly Duckling will be supporting this team in an automation project with the aim of increasing efficiency and reducing operational risk.
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.
We were asked by a large Dutch bank to help with the automatic processing and testing of data. We used our Robot technology as part of the implementation.
Ugly Duckling has developed a number of products that help to process large amounts of data. We were asked by a Dutch bank’s Asset and Liability Management group to use this software to develop a prototype for a Robot that we code-named ‘Jan’.
Jan was able to monitor directories, mark up suspicious data, clean garbage data and, over time, monitor the quality of the data. The Robot Jan was able to do in 30 seconds what an analyst would take hours to do.