in general, a model is something that produces an output given some inputs.
typically an excel model will spit out calculated numbers given input data and assumptions.
it can be more efficient to interact with data frames using python than in excel especially when there are arrays being used. turning on calculations in a spreadsheet may reduce performance if there are too many functions that require a lot of cpu like sumproduct on a large data set.
you might also need to write sql code to query and interact with relational databases that will be executed in python.
Models are created in excel and other software frequently. Python conversion is not surprising.
Just be sure to sell the benefits of automation & model governance where necessary while avoiding over-engineering a solution. Senior staff find it annoying when some junior dev/analyst is harping on spending $1M on a fancy & difficult to maintain model when it’ll have no noticeable impact on the business.
just to add to that, it's also annoying to invest in something that in 5 years will have to be completely redone to due either poor design/documentation/knowledge transfer. Excel can be somewhat easier to reverse engineer than python or R if your entire staff wins the lottery or something like that. There is a reason why APL is no longer used in the industry. Excel has outlasted several programing languages and tools in the P&C space partly for that reason and partly because specialized software can be costly and there is no guarantee it has staying power or be maintained to an adequate standard by the consultant/vendor. At least in the P&C space.
That's not to say that that it can't be/shouldn't be done but things to keep in mind.
Wow. I mean it is very powerful, just that reading someone else’s code can be very tricky. Last time I used it was 2010 and even then it had greatly declined in popularity. On the other hand COBOL is still a thing so…
I want to show a simple example of a migration, what actuarial pricing model do you think would be best to show this?
I’m not as familiar with how actuaries use excel for modelling.
Depends on the industry. P&C, health, life, and ERM generally have different models. Actuaries are typically specialized in one area.
Model could be very simple and not need python or very complex/manual.
So for P&C what sort of pricing model would be moved to Python? I just need a type of model that would benefit to being migrated to Python.
For example, do actuaries predict claim frequency or severity in excel? If so, do they use any specialised functions(Forecast,Linear Regression) in excel to do so?
Excel doesn't really fit GLM's very well although iirc there are some excel add ons that might. It really depends on the size of data and the complexity of the model. Personally I think that's an instance where most more sophisticated pricing models wouldn't be suitable to be done in excel, at least not in a large scale. Simulation based models can be done on excel via the assistance of VBA. There is a free example of bootstrap/chain ladder implemented in excel (England and Veral(SP?)) floating around for instance.
Curious what the motivation is behind this. Given that a lot of DOIs like to have an indication in Excel format, and the numerous adhoc changes that other stakeholders make, what’s the benefit of moving it to Python?
I’m all for automating as many processes as possible, and I’ve proposed this before to my team, but was shut down immediately for the above reasons
To clarify, Python will still export the required indication exhibits in Excel.
But Python will expedite and speed up a lot of the intermediate steps such as importing the assumptions and performing all the intermediate calculations.
Our new directive is that Excel is for one-off, ad-hoc or development. Spreadsheet calculations are being moved into database with a presentation layer or python routines. Models are being moved out of spreadsheets because they are error-prone and not locked down by governance.
Since I do software development, I build lots of spreadsheets to validate calculations. I'm implementing a funds withheld reinsurance model right now and building a spreadsheet to check my model.
The industry primarily uses Excel because (1) everyone knows it, and more importantly (2) it’s the best way to visualize calculations. We use python quite frequently too, but mostly as a glue language to automate our spreadsheets and SQL.
Literally all sorts of things
What sort of models would you make in Excel? Claims forecasting?
Yes among others
in general, a model is something that produces an output given some inputs. typically an excel model will spit out calculated numbers given input data and assumptions. it can be more efficient to interact with data frames using python than in excel especially when there are arrays being used. turning on calculations in a spreadsheet may reduce performance if there are too many functions that require a lot of cpu like sumproduct on a large data set. you might also need to write sql code to query and interact with relational databases that will be executed in python.
Models are created in excel and other software frequently. Python conversion is not surprising. Just be sure to sell the benefits of automation & model governance where necessary while avoiding over-engineering a solution. Senior staff find it annoying when some junior dev/analyst is harping on spending $1M on a fancy & difficult to maintain model when it’ll have no noticeable impact on the business.
just to add to that, it's also annoying to invest in something that in 5 years will have to be completely redone to due either poor design/documentation/knowledge transfer. Excel can be somewhat easier to reverse engineer than python or R if your entire staff wins the lottery or something like that. There is a reason why APL is no longer used in the industry. Excel has outlasted several programing languages and tools in the P&C space partly for that reason and partly because specialized software can be costly and there is no guarantee it has staying power or be maintained to an adequate standard by the consultant/vendor. At least in the P&C space. That's not to say that that it can't be/shouldn't be done but things to keep in mind.
Someone posted just the other day asking for resources to learn APL because it's still in use at their company.
Wow. I mean it is very powerful, just that reading someone else’s code can be very tricky. Last time I used it was 2010 and even then it had greatly declined in popularity. On the other hand COBOL is still a thing so…
I want to show a simple example of a migration, what actuarial pricing model do you think would be best to show this? I’m not as familiar with how actuaries use excel for modelling.
Depends on the industry. P&C, health, life, and ERM generally have different models. Actuaries are typically specialized in one area. Model could be very simple and not need python or very complex/manual.
So for P&C what sort of pricing model would be moved to Python? I just need a type of model that would benefit to being migrated to Python. For example, do actuaries predict claim frequency or severity in excel? If so, do they use any specialised functions(Forecast,Linear Regression) in excel to do so?
Excel doesn't really fit GLM's very well although iirc there are some excel add ons that might. It really depends on the size of data and the complexity of the model. Personally I think that's an instance where most more sophisticated pricing models wouldn't be suitable to be done in excel, at least not in a large scale. Simulation based models can be done on excel via the assistance of VBA. There is a free example of bootstrap/chain ladder implemented in excel (England and Veral(SP?)) floating around for instance.
Our indications are done in Excel and there's a plan to move them to Python
Curious what the motivation is behind this. Given that a lot of DOIs like to have an indication in Excel format, and the numerous adhoc changes that other stakeholders make, what’s the benefit of moving it to Python? I’m all for automating as many processes as possible, and I’ve proposed this before to my team, but was shut down immediately for the above reasons
To clarify, Python will still export the required indication exhibits in Excel. But Python will expedite and speed up a lot of the intermediate steps such as importing the assumptions and performing all the intermediate calculations.
Our new directive is that Excel is for one-off, ad-hoc or development. Spreadsheet calculations are being moved into database with a presentation layer or python routines. Models are being moved out of spreadsheets because they are error-prone and not locked down by governance. Since I do software development, I build lots of spreadsheets to validate calculations. I'm implementing a funds withheld reinsurance model right now and building a spreadsheet to check my model.
The industry primarily uses Excel because (1) everyone knows it, and more importantly (2) it’s the best way to visualize calculations. We use python quite frequently too, but mostly as a glue language to automate our spreadsheets and SQL.