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.
Daily monitoring of liquidity has become a crucial job inside any bank. We implemented the Extract-Load-Transform (ETL) operations of liquidity tools for different trading desks for the modelling and risk-reporting departments in a large Dutch bank, which allows the bank to run its liquidity-monitoring tools daily.
Liquidity input data come from various sources and all have different formats. Some are Excel files, while some others are comma-separated text files. Moreover, date conventions are not standard and depends on external factors, such as Excel settings. In addition, all the different pieces of information have to be adjusted before they can be used. Such adjustments include specific selection and join operations.
Our input tool has been developed in C#. We have created in-memory databases and used LINQ to perform DB operations. The code has been unit-tested. Log text files and Excel output files are created daily. The tool has been accompanied by a self-contained user manual explaining the business logic, configuration setting and command-line arguments, exceptions that may arise during the execution.
Ella places two printouts on John’s desk and points at the total columns. “Why don’t these numbers match?” she asks. John looks for a second, hoping Ella has made a simple mistake. But she’s right, they should match. John spends the rest of the day crawling through dozens of spreadsheets, attempting to find the source of the discrepancy. Eventually the problem is found, but only after turning up five or six similar issues. The team collectively pauses for breath when they consider the number of decisions made on faulty data. It is another week before John has fixed the problems and can return to his original task.
This is a particularly familiar story to many growing financial companies, which often prompts the question “how did things get like this?”. To find the answer, we need to investigate a misunderstood dynamic in spreadsheet and tool development. Consider the following diagram:
Figure 1 – The Square Law of Spreadsheets Effects Diagram. Continue reading →
In our experience, each client is different, context is always king. However, because many financial institutions start off with end-user programmers, people who program for themselves, usually in Excel with VBA, the organisations tend to follow similar growth paths. In the end, financial institutions end up with a consolidated engineering team, what is often called the ‘Engineering Hypermarket’. The hypermarket is a place where the financial institution can ‘shop’ for:
- A bug fix.
- A new pricer.
- A desk quant who can sit in the trading room assisting with features, analysis or coding.
The hypermarket is characterised by high levels of automation, discipline and rapidity; they do things quickly. Building a hypermarket is a bootstrapping process that starts with the smoke test. Continue reading →