TK Solver Models Natural Disaster Outcomes for the Insurance Industry in South Africa
Adi Hazan is a “fixer.” A programmer of last resort, the guy people call when all other efforts have fallen through. As he describes it, “I take on projects where other people have failed or where there is a crisis . . . in general, I’m the last line of defense.” And it’s no wonder. Hazan sold his first program at age 12, and to this day is a voracious learner, blazing through 1000+ pages a week on “absolutely anything.”
One of Hazan’s most recent programming challenges arrived 9 months ago, when he was approached by an actuary who hired him to solve some outstanding problems in mathematical modeling of natural disasters. A legislative deadline loomed: The South African government had made modeling a legal requirement for the insurance industry starting in 2008. When Hazan was approached, no one had been able to produce a workable model. After weeks of testing actuarial programs without success, he broadened his search and came across TK Solver, and immediately recognized the potential.
So what does the program do? Hazan explains: “The program tries to predict what the maximum damage from earthquakes could be so that insurance companies can keep the correct money reserves for such an emergency. A more detailed explanation is that the system clusters the historical data into regions, and then models the frequency and magnitude for the regions using 30 different statistical curves in TK, optimizing the curves to fit the data. Once the best fit is chosen from the 30 curves, a simulation of 10,000 years is done using random inputs to see what size of damage one can expect every 250 years on the client’s specific set of insurance policies.”
TK’s speed, power and accuracy were critical to Hazan’s work. On TK’s speed, he states, “It’s vital to note that any series of calculations done in TK is at least 10 times faster than Excel. This is not trivial when you consider a 10 hour process coming down to an hour.” As to TK’s power, Hazan comments, “We were able to solve problems using built-in functions that cannot be done on any platform that costs less than $150,000. Specifically, we were able to fit a curve of rounded, discrete values to a mean and standard deviation–what this means is that we can round and then optimize, not optimize and then round and lose accuracy. This is impossible on normal algorithms or the Frontline Solver for Excel.” Finally, accuracy was imperative to Hazan’s program, and TK delivered. He remarks, “A number of the rarely-used functions in Excel simply gave us wrong answers in comparison. When the TK answers were wrong we were able to adjust the functions, whereas in Excel it was ‘take-it-or-leave-it.’”
Hazan also cites TK’s support as “unparalleled,” in particular the expertise Todd and Mohan provided, on both complex statistical issues and non-mathematical technical support. As he puts it, “Imagine a call center getting a call asking how to ‘expand the Marquardt optimization algorithm so that it can accommodate discrete values and still move to the local minimum of multi-variable curves.’”
As for response to the program Hazan reports that it has been “excellent.” TK’s versatility prevented him from having to side-step any mathematical issues, giving Hazan the ability to build something “more detailed and accurate than anything else on the market.”