Senior investment managers and leaders have historically very good understanding of the market. They are the ones who understand the data intuitively.

With time the world has become more integrated and businesses have diversified. This has resulted in diversified data. Even though the markets are getting complicated like never before the senior investment professionals still are the very best in understanding. They still understand their data. The challenge they find is deriving knowledge from that data because current market data if seen individually can be easily understood by them, but when there are many data points, it becomes overwhelming to even the seasoned professional to derive knowledge from it.

From my experience in various projects with such professionals, I came to know that when they come to the risk analysts with data and problem, they are generally not looking for any sophisticated statistical model. They are looking for a user interface tool kit which simplifies their data mining process and hence extract the knowledge from the data easily.

I would like to share some of my personal project experience.

**Tool box for a hedge fund manager**

A hedge fund manager had a portfolio of revolver loans. He had to sell that portfolio to his client. In order to make him the sale, the manager has to show his client what can be the possible worst loss the portfolio can give.

The data he had was history of last 10 years of each of his loan customer and what was the outstanding loan that customer had. The customers who breached the limit 80% withdrawal from the allocated amount were considered risky. He came to our team with a request to develop a tool which could mine knowledge from his shared data by performing historical simulation of random sub sample. We offered him to perform additional statistical analysis but he was not interested.

So we developed a tool which could perform multiple sample size simulation as per his choice he wanted to demonstrate his client that among all the customers:

- On a given year, what percentage of the customers were in the risky territory.
- Which sectors were the most risky?
- How much dollar value of the portfolio of each segment was at risk?
- What is the average out standing to loan for a random sample?
- What is the standard deviation of the outstanding to loan for the chosen sample?

Our tool gave him freedom to choose multiple sample at a time, his choice of time period. All the visualizations were in graphical and simple tabular format. By being empowered with his own data he was able to make the sale!

**Expected Loss calculator for a Loan Portfolio Manager**

A senior loan portfolio manager managed multi-billion dollar loan portfolio. He had exposure in various segments.

He identified a set of risk factors which impacted the exposure of his portfolio. He request us to develop a tool box using which he could measure the change in exposure.

We developed an excel based tool where he could vary the risk factors and check the change in portfolio valuation sector wise.

**PCA component analysis for an Interest Rate Strategist**

Principal Component Analysis is perhaps the most effective tool in dealing with highly correlated data. Interest rate curve data is one of the best example of such data. Their is high auto-correlation within a historical series as well has very high co-linearity within the tenors. Hence PCA is favorite statistical tool for interest rate strategists.

Many years ago, even though excel was favorite tool for business professionals, excel based tools were not easily found over the internet. An interest rate strategist came to us with a request to develop an excel based tool which calculated PCA for any historical series of currency of his choice.

We offered him to include additional sophisticated statistical models to help him in his analysis. He was only interested PCA analysis. After further discussion, we came to know that he relied on his own judgment about the data and required a tool which could do PCA and addition could give very good visualization of the results.

**Conclusion**

Often investment managers and leaders reach out to risk analysts with their business problems associated with data mining. These people are in the market have developed their expertise in its understanding. The problems they come with are not generally within regulatory domain but are about data mining.

When the business problems are not within regulatory domain, analysts instead of providing them additional statistical analysis from what required should instead focus in empowering them better visualization of their requested analysis. They should develop the requested tools which give them more authority in modifying their analysis with various perspectives.