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IFoA CA2 Paper 2 -tips and strategies

I'm quite demoralized by paper 1 right now.. but I'll still write a post for paper 2 for completeness! 

For paper 2, we have a (hopefully) functioning excel sheet which we need to chng a bit for some additional requests your boss is asking from you. I like to start with the summary first because there's a lot you can do even before touching the model.

The main format of paper 2 is:
Overview - this section is supposed to give the reader a summary of what the project is about. Use the audit trail and rephrase it such that it's more "report like". For the past papers, I don't see much value add in this section besides the paraphrasing so I think this should be sufficient. 

Data - this section holds information on the data used and where it was obtained. Similarly, use the exam script and paraphrase. Noting to add additional information when necessary. When the data is unclean, I'll include a "data validation" section right here (since the question will more often than not ask for a plot of the raw data) and start explaining the clean up process (why more than how). Make sure you include checks to ensure that there isn't any obvious errors! I like to plot my data to see the smoothness of the curve but I don't think they give marks for that.

Assumptions- Lock stock and barrel from the audit trail. It's important to value-add in this section. Think of the model from these following angles:

Data integrity e.g. is the data correct and appropriate for use?
Statistical distribution Question will usually dictate the use of certain distributions; we need to question the validity (of the data), and suitability (of the parameters).
Time consistency For CA2, very simple assumptions are used. The decrement rate is usually fixed at a certain % or value. We need to add this assumption into the audit trail.
Independence/Correlation
e.g. drop out rates are independent of each other. Since it might be the case that when your best friend drops out of a course, you're likely to follow.
Delays This is applicable to almost every paper I've tried. The idea is that there is no lags between whatever you're modelling- could be payment and receiving of premiums, transporting woollen felts, improvement of dog's longevity etc. You get the idea.
Business mix If external data is used to model internal projects, we have to assume that the business mix will remain the same, or at least that the fluctuations are insignificant.
Shocks Assume that there will not be any shocks throughout the 5/10/20/25 years modelling period. Way off assumption, I know, but unless you wanna spend time adding a random element into your model (which you will not be given marks for), this is a necessary assumption.
Other factors: Allowance for interest, inflation, capital gains, better mortality? All these will affect the modelling results and should be stated before you do the modelling- to ensure you don't miss these effects out.
Brainstorm: what other real world factors can affect our model? Be the devil's advocate! A lot of these insights will be whispering at you when you're doing the modelling on excel so don't ignore these voices! Grab a piece of paper and write them down. There will always be some factors that are missing out; if not actuaries will be able to predict the future ^^

Methodology- to document the method and approach used in our model. 

Create sub-sections to document the excel sheet, from top to bottom, left to right. Just type in everything that you do and justify the methods. Try to use as few formulae as possible since this is not the audit trail. For this section, I tend not to refer to the audit trail since most of the time, it'll contain too much information, and the tones of the reports are not the same. I don't think any marks will be given for signposting but do it if it will make your report more readable! 

Remember to document the parameters used for each sheet!

Some checks to include at the end of the model, after the results are shown:
Summary statistics. The simplest form of checks. Ensure that the data count is correct and the mean makes sense.
Reconciliation of models. If you have a model that was based on another model, enter the first model's parameters into the second model and see if the results are the same. This ensures that the logic in both models are similar and hence comparable.
Shape of graph- talk about the shape of the graph and why it looks reasonable.
Impact analysis- talk about why the impact of different models and how your results can be expected. e.g. passing rate has increased, hence the number of passes is higher. (I know this sounds like a tautology and I used to hate it and it made me so angry! But I guess I got over it.. take some time and meditate and you'll feel better about the whole situation..^^)

Differences - highlight any significant differences in results and try to make sense of it.

(did you see what I just did? I copied and pasted the entire section from my paper 1 post and did a bit of edits here and there to fit it into paper 2- and I feel no shame ^^v) 

Results- to summarize the overall conclusion and explain result 
I believe this is where the bulk of marks (50-60) are for this paper. After you're done with the methodology section, take some time to make sense of the result. I have several sub headings for this section, namely:

Results (the title will be the result that is presented here. E.g. number of dogs, hours on the tv, IRR.)
Present the graphs as requested by the question and talk about it. The main commentary can be split into: what is reasonable and what is not. There should be 2 or 3 subsections presenting the graphs and result, depending on the questions asked.

Conclusions I'll write 2-3 paragraphs about the model. firstly, summarise the results. E.g. decrease in mortality of 5% will lead to a short life span of 2 years. Then, challenge the assumptions used for the model: think of the parameters used e.g. growth rate which is given by the client, initial fund value, which is deemed to be fixed, expenses that does not change with time. Lastly, state how the model will differ if these assumptions do not hold. Also add a few lines about sensitivity. e.g. changing the decrease of mortality by 1% will affect our result by 10%. I like to add this line: the sensitivity of our model to its parameters is well noted. This leads us to think of further steps to validate the data used and improve our model. 

Next step: challenge every single assumption. I just use the assumptions part above and paraphase it. for example,

Data is available only for male dogs, assume similar results for bitches.

would be edited into: 

Obtain mortality rates for female dogs and the expected gender mix of the business. Model the difference in life expectancy for female and male dogs and weigh it by the gender mix to have a clearer picture of the effects of dog food. 

I usually have around 10-20 assumptions and at least 10 of them will be relevant in this section. List of other common next steps:

Validate the data provided from other sources

Repeat the model with other parameters e.g. industrial statistics instead of internal data. 

Perform sensitivity testing on key parameters

Challenge the fixed parameters e.g. different age same risk premium? it may be more realistic to have a higher risk premiums for older customers. 

Test and update the model with emerging experience.

Model the parameters stochastically

and my favorite: 0btain a peer review of the work performed. (always a free mark :D)

Next Steps provides 25 marks and it's huge so if there's one section you should complete, I suggest that you jump straight to the conclusion and type in the next steps. Don't worry if the excel sheet is not up to mark. There are only 15-25 marks for that portion. 


As with P1, please do not spend too much time on the excel model. I would say that 1 h is wayyyy more than enough since this paper is not meant to test your excel skills. Especially for working students who have a lot of experience and expertise on excel, please make sure you don't go ham in creating a over complicated model. Focus a lot more on the report and you should be fine!


Time management. This is the strategy that I tell myself to follow but sometimes I get carried away by modelling (cause we all love excel so much). But it's a good guide as to where you should be at each time period.
first 15 min - read and plan the structure of the report. It should take less than 15 minutes. I suggest that you make notes (I open my exam paper in words and highlight things that I will be copying later) as to which segment of the exam paper and audit trail are reusable in the report. Understand the input and output of the model and what the examiner requires in the report.
next 30 min - Start on the report. without touching excel, but understanding what is required from the model, you'll be able to draft a decent Overview, Data, Assumption without the data validation. That would be around 500-1000 words.
next 45 min - excel. model model model. Remember to include checks!
next 1 hour - Complete the report. Start by writing what is essential into methodology and you don't really need this part to be perfect. Be sure to spend at least 1 h on the results. 
Last 45 min - (I have an alarm set up for this) take 1 minute to meditate and clear your mind and relook at the report from a third person's point of view. This is the time when you look at your work and ask yourself: "what the hell was I thinking then?". Also, make sure to correct the voice of your report.

I realised that IFoA gives 15 minutes to upload. So as long as you don't edit the report after the cut off time, you have another 15 min to fight the other students on the sever to get your work uploaded! 


TLDR: Read, Report, use the Information from the exam paper, Model, Document, Check, Submit, all the best ^^

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