bi benefits

Using historical data to justify BI investments – Part III

Peter James Thomas business analytics, business intelligence, data warehousing, Statistics , , , ,

The earliest recorded surd

This article completes the three-part series which started with Using historical data to justify BI investments – Part I and continued (somewhat inevitably) with Using historical data to justify BI investments – Part II. Having presented a worked example, which focused on using historical data both to develop a profit-enhancing rule and then to test its efficacy, this final section considers the implications for justifying Business Intelligence / Data Warehouse programmes and touches on some more general issues.
 
 
The Business Intelligence angle

In my experience when talking to people about the example I have just shared, there can be an initial “so what?” reaction. It can maybe seem that we have simply adopted the all-too-frequently-employed business ruse of accentuating the good and down-playing the bad. Who has not heard colleagues say “this was a great month excluding the impact of X, Y and Z”? Of course the implication is that when you include X, Y and Z, it would probably be a much less great month; but this is not what we have done.

One goal of business intelligence is to help in estimating what is likely to happen in the future and guiding users in taking decisions today that will influence this. What we have really done in the above example is as follows:

Look out Morlocks, here I come... [alumni of Imperial College London are so creative aren't they?]

  1. shift “now” back two years in time
  2. pretend we know nothing about what has happened in these most recent two years
  3. develop a predictive rule based solely on the three years preceding our back-shifted “now”
  4. then use the most recent two years (the ones we have metaphorically been covering with our hand) to see whether our proposed rule would have been efficacious

For the avoidance of doubt, in the previously attached example, the losses incurred in 2009 – 2010 have absolutely no influence on the rule we adopt, this is based solely on 2006 – 2008 losses. All the 2009 – 2010 losses are used for is to validate our rule.

We have therefore achieved two things:

  1. Established that better decisions could have been taken historically at the juncture of 2008 and 2009
  2. Devised a rule that would have been more effective and displayed at least some indication that this could work going forward in 2011 and beyond

From a Business Intelligence / Data Warehousing perspective, the general pitch is then something like:

Eight out of ten cats said that their owners got rid of stubborn stains no other technology could shift with BI - now with added BA

  1. if we can mechanically take such decisions, based on a very non-sophisticated analysis of data, then if we make even simple information available to the humans taking decisions (i.e. basic BI), then surely the quality of their decision-making will improve
  2. If we go beyond this to provide more sophisticated analyses (e.g. including industry segmentation, analysis of insured attributes, specific products sold etc., i.e. regular BI) then we can – by extrapolation from the example – better shape the evolution of the performance of whole books of business
  3. We can also monitor the decisions taken to determine the relative effectiveness of individuals and teams and compare these to their peers – ideally these comparisons would also be made available to the individuals and teams themselves, allowing them to assess their relative performance (again regular BI)
  4. Finally, we can also use more sophisticated approaches, such as statistical modelling to tease out trends and artefacts that would not be easily apparent when using a standard numeric or graphical approach (i.e. sophisticated BI, though others might use the terms “data mining”, “pattern recognition” or the now ubiquitous marketing term “analytics”)

The example also says something else – although we may already have reporting tools, analysis capabilities and even people dabbling in statistical modelling, it appears that there is room for improvement in our approach. The 2009 – 2010 loss ratio was 54% and it could have been closer to 40%. Thus what we are doing now is demonstrably not as good as it could be and the monetary value of making a stepped change in information capabilities can be estimated.

The generation of which should be the object of any BI/DW project worth its salt - thinking of which, maybe a mound of salt would also have worked as an illustration

In the example, we are talking about £1m of biannual premium and £88k of increased profit. What would be the impact of better information on an annual book of £1bn premium? Assuming a linear relationship and using some advanced Mathematics, we might suggest £44m. What is more, these gains would not be one-off, but repeatable every year. Even if we moderate our projected payback to a more conservative figure, our exercise implies that we would be not out of line to suggest say an ongoing annual payback of £10m. These are numbers and concepts which are likely to resonate with Executive decision-makers.

To put it even more directly an increase of £10m a year in profits would quickly swamp the cost of a BI/DW programme in very substantial benefits. These are payback ratios that most IT managers can only dream of.

As an aside, it may have occurred to readers that the mechanistic rule is actually rather good and – if so – why exactly do we need the underwriters? Taking to one side examples of solely rule-based decision-making going somewhat awry (LTCM anyone?) the human angle is often necessary in messy things like business acquisition and maintaining relationships. Maybe because of this, very few insurance organisations are relying on rules to take all decisions. However it is increasingly common for rules to play some role in their overall approach. This is likely to take the form of triage of some sort. For example:

  1. A rule – maybe not much more sophisticated than the one I describe above – is established and run over policies before renewal.
  2. This is used to score polices as maybe having green, amber or red lights associated with them.
  3. Green policies may be automatically renewed with no intervention from human staff
  4. Amber polices may be looked at by junior staff, who may either OK the renewal if they satisfy themselves that the issues picked up are minor, or refer it to more senior and experienced colleagues if they remain concerned
  5. Red policies go straight to the most experienced staff for their close attention

In this way process efficiencies are gained. Staff time is only applied where it is necessary and the most expensive resources are applied to those cases that most merit their abilities.

 
Correlation

From the webcomic of the inimitable Randall Munroe - his mouse-over text is a lot better than mine BTW
© xkcd.com

Let’s pause for a moment and consider the Insurance example a little more closely. What has actually happened? Well we seem to have established that performance of policies in 2006 – 2008 is at least a reasonable predictor of performance of the same policies in 2009 – 2010. Taking the mutual fund vendors’ constant reminder that past performance does not indicate future performance to one side, what does this actually mean?

What we have done is to establish a loose correlation between 2006 – 2008 and 2009 – 2010 loss ratios. But I also mentioned a while back that I had fabricated the figures, so how does that work? In the same section, I also said that the figures contained an intentional bias. I didn’t adjust my figures to make the year-on-year comparison work out. However, at the policy level, I was guilty of making the numbers look like the type of results that I have seen with real policies (albeit of a specific type). Hopefully I was reasonably realistic about this. If every policy that was bad in 2006 – 2008 continued in exactly the same vein in 2009 – 2010 (and vice versa) then my good segment would have dropped from an overall loss ratio of 54% to considerably less than 40%. The actual distribution of losses is representative of real Insurance portfolios that I have analysed. It is worth noting that only a small bias towards policies that start bad continuing to be bad is enough for our rule to work and profits to be improved. Close scrutiny of the list of policies will reveal that I intentionally introduced several counter-examples to our rule; good business going bad and vice versa. This is just as it would be in a real book of business.

Not strongly correlated

Rather than continuing to justify my methodology, I’ll make two statements:

  1. I have carried out the above sort of analysis on multiple books of Insurance business and come up with comparable results; sometimes the implied benefit is greater, sometimes it is less, but it has been there without exception (of course statistics being what it is, if I did the analysis frequently enough I would find just such an exception!).
  2. More mathematically speaking, the actual figure for the correlation between the two sets of years is a less than stellar 0.44. Of course a figure of 1 (or indeed -1) would imply total correlation, and one of 0 would imply a complete lack of correlation, so I am not working with doctored figures. Even a very mild correlation in data sets (one much less than the threshold for establishing statistical dependence) can still yield a significant impact on profit.

 
Closing thoughts

Ground floor: Perfumery, Stationery and leather goods, Wigs and haberdashery, Kitchenware and food…. Going up!

Having gone into a lot of detail over the course of these three articles, I wanted to step back and assess what we have covered. Although the worked-example was drawn from my experience in Insurance, there are some generic learnings to be made.

Broadly I hope that I have shown that – at least in Insurance, but I would argue with wider applicability – it is possible to use the past to infer what actions we should take in the future. By a slight tweak of timeframes, we can even take some steps to validate approaches suggested by our information. It is important that we remember that the type of basic analysis I have carried out is not guaranteed to work. The same can be said of the most advanced statistical models; both will give you some indication of what may happen and how likely this is to occur, but neither of them is foolproof. However, either of these approaches has more chance of being valuable than, for example, solely applying instinct, or making decisions at random.

In Patterns, patterns everywhere, I wrote about the dangers associated with making predictions about events are essentially unpredictable. This is another caveat to be born in mind. However, to balance this it is worth reiterating that even partial correlation can lead to establishing rules (or more sophisticated models) that can have a very positive impact.

While any approach based on analysis or statistics will have challenges and need careful treatment, I hope that my example shows that the option of doing nothing, of continuing to do things how they have been done before, is often fraught with even more problems. In the case of Insurance at least – and I suspect in many other industries – the risks associated with using historical data to make predictions about the future are, in my opinion, outweighed by the risks of not doing this; on average of course!

But then 1=2 for very large values of 1
 

Using historical data to justify BI investments – Part II

Peter James Thomas business, business analytics, business intelligence, data warehousing , , ,

The earliest recorded surd

This article is the second in what has now expanded from a two-part series to a three-part one. This started with Using historical data to justify BI investments – Part I and finishes with Using historical data to justify BI investments – Part III (once again exhibiting my talent for selecting buzzy blog post titles).
 
 
Introduction and some belated acknowledgements

The intent of these three pieces is to present a fairly simple technique by which existing, historical data can be used to provide one element of the justification for a Business Intelligence / Data Warehousing programme. Although the specific example I will cover applies to Insurance (and indeed I spent much of the previous, introductory segment discussing some Insurance-specific concepts which are referred to below), my hope is that readers from other sectors (or whose work crosses multiple sectors) will be able to gain something from what I write. My learnings from this period of my career have certainly informed my subsequent work and I will touch on more general issues in the third and final section.

This second piece will focus on the actual insurance example. The third will relate the example to justifying BI/DW programmes and, as mentioned above, also consider the area more generally.

Before starting on this second instalment in earnest, I wanted to pause and mention a couple of things. At the beginning of the last article, I referenced one reason for me choosing to put fingertip to keyboard now, namely me briefly referring to my work in this area in my interview with Microsoft’s Bruno Aziza (@brunoaziza). There were a couple of other drivers, which I feel rather remiss to have not mentioned earlier.

First, James Taylor (@jamet123) recently published his own series of articles about the use of BI in Insurance. I have browsed these and fully intend to go back and read them more carefully in the near future. I respect James and his thoughts brought some of my own Insurance experiences to the fore of my mind.

Second, I recently posted some reflections on my presentation at the IRM MDM / Data Governance seminar. These focussed on one issue that was highlighted in the post-presentation discussion. The approach to justifying BI/DW investments that I will outline shortly also came up during these conversations and this fact provided additional impetus for me to share my ideas more widely.
 
 
Winners and losers

Before him all the nations will be gathered, and he will separate them one from another, as a shepherd separates the sheep from the goats

The main concept that I will look to explain is based on dividing sheep from goats. The idea is to look at a set of policies that make up a book of insurance business and determine whether there is some simple factor that can be used to predict their performance and split them into good and bad segments.

In order to do this, it is necessary to select policies that have the following characteristics:

  1. Having been continuously renewed so that they at least cover a contiguous five-year period (policies that have been “in force” for five years in Insurance parlance).

    The reason for this is that we are going to divide this five-year term into two pieces (the first three and the final two years) and treat these differently.

  2. Ideally with the above mentioned five-year period terminating in the most recent complete year – at the time of writing 2010.

    This is so that the associated loss ratios better reflect current market conditions.

  3. Being short-tail policies.

    I explained this concept last time round. Short-tail policies (or lines or business) are ones in which any claims are highly likely to be reported as soon as they occur (for example property or accident insurance).

    These policies tend to have a low contribution from IBNR (again see the previous piece for a definition). In practice this means that we can use the simplest of the Insurance ratios, paid loss-ratio (i.e. simply Claims divided by Premium), with some confidence that it will capture most of the losses that will be attached to the policy, even if we are talking about say 2010.

    Another way of looking at this is that (borrowing an idea discussed last time round) for this type of policy the Underwriting Year and Calendar Year treatments are closer than in areas where claims may be reported many years after the policy was in force.

Before proceeding further, it perhaps helps to make things more concrete. To achieve this, you can download a spreadsheet containing a sample set of Insurance policies, together with their premiums and losses over a five-year period from 2006 to 2010 by clicking here (this is in Office 97-2003 format – if you would prefer, there is also a PDF version available here). Hopefully you will be able to follow my logic from the text alone, but the figures may help.

A few comments about the spreadsheet. First these are entirely fabricated policies and are not even loosely based on any data set that I have worked with before. Second I have also adopted a number of simplifications:

  1. There are only 50 policies, normally many thousand would be examined.
  2. Each policy has the same annual premium – £10,000 (I am British!) – and this premium does not change over the five years being considered. In reality these would vary immensely according to changes in cover and the insurer’s pricing strategy.
  3. I have entirely omitted dates. In practice not every policy will fit neatly into a year and account will normally need to be taken of this fact.
  4. Given that this is a fabricated dataset, the claims activity has not been generated randomly. Instead I have simply selected values (though I did perform a retrospective sense check as to their distribution). While this example is not meant to 100% reflect reality, there is an intentional bias in the figures; one that I will come back to later.

The sheet also calculates the policy paid loss ratio for each year and figures for the whole portfolio appear at the bottom. While the in-year performance of any particular policy can gyrate considerably, it may be seen from the aggregate figures that overall performance of this rather small book of business is relatively consistent:

Year Paid Loss Ratio
2006 53%
2007 59%
2008 54%
2009 53%
2010 54%
Total 54%

Above I mentioned looking at the five years in two parts. At least metaphorically we are going to use our right hand to cover the results from years 2009 and 2010 and focus on the first three years on the left. Later – after we have established a hypothesis based on 2006 to 2008 results – we can lift our hand and check how we did against the “real” figures.

For the purposes of this illustration, I want to choose a rather mechanistic way to differentiate business that has performed well and badly. In doing this I have to remember that a policy may have a single major loss one year and then run free of losses for the next 20. If I was simply to say any policy with a large loss is bad, I am potentially drastically and unnecessarily culling my book (and also closing the stable door after the horse has bolted). Instead we need to develop a rule that takes this into account.

In thinking about overall profitability, while we have greatly reduced the impact of both reported but unpaid claims and IBNR by virtue of picking a short-tail business, it might be prudent to make say a 5% allowance for these. If we also assume an expense ratio of 35%, then we have a total of non-underwriting-related outgoings of 40%. This means that we can afford to have a paid loss ratio of up to 60% (100% – 40%) and still turn a profit.

Using this insight, my simple rule is as follows:

A policy will be tagged as “bad” if two things occur:

  1. The overall three-year loss ratio is in excess of 60%

    i.e. is has been unprofitable over this period; and

  2. The loss ratio is in excess of 30% in at least two of the three years

    i.e. there is a sustained element to the poor performance and not just the one-off bad luck that can hit the best underwritten of policies

This rule roughly splits the book 75 / 25; with 74% of policies being good. Other choices of parameters may result in other splits and it would be advisable spending a little time optimising things. Perhaps 26% of policies being flagged as bad is too aggressive for example (though this rather depends on what you do about them – see below). However in the simpler world of this example, I’ll press on to the next stage with my first pick.

The ultimate sense of perspective

Well all we have done so far is to tag policies that have performed badly – in the parlance of Analytics zealots we are being backward-looking. Now it is time to lift our hand on 2009 to 2010 and try to be forward-looking. While these figures are obviously also backward looking (the day that someone comes up with future data I will eat my hat), from the frame of reference of our experimental perspective (sitting at the close of 2008), they can be thought of as “the future back then”. We will use the actual performance of the policies in 2009 – 2010 to validate our choice of good and bad that was based on 2006 – 2008 results.

Overall the 50 policies had a loss ratio of 54% in 2009 – 2010. However those flagged as bad in our above exercise had a subsequent loss ratio of 92%. Those flagged as good had a subsequent loss ratio of 40%. The latter is a 14 point improvement on the overall performance of the book.

So we can say with some certainly that our rule, though simplistic, has produced some interesting results. The third part of this series will focus more closely on why this has worked. For now, let’s consider what actions the split we have established could drive.
 
 
What to do with the bad?

You shall be taken to the place from whence you came...

We were running a 54% paid ratio in 2009-2010. Using the same assumptions as above, this might have equated to a 94% combined ratio. Our book of business had an annual premium of £0.5m so we received £1m over the two years. The 94% combined would have implied making a £60k profit if we had done nothing different. So what might have happened if we had done something?

There are a number of options. The most radical of these would have been to not renew any of the bad policies; to have carried out a cull. Let us consider what would have been the impact of such an approach. Well our book of business would have shrunk to £740k over the two years at a combined of 40% (the ratio of the good book) + 40% (other outgoing) = 80%, which implies a profit of £148k, up £88k. However there are reasons why we might not have wanted to so drastically shrink our business. A smaller pot of money for investment purposes might have been one. Also we might have had customers with policies in both the good and bad segments and it might have been tricky to cancel the bad while retaining the good. And so on…

Another option would have been to have refined our rule to catch fewer policies. Inevitably, however, this would have reduced the positive impact on profits.

At the other extreme, we might have chosen to take less drastic action relating to the bad policies. This could have included increasing the premium we charged (which of course could also have resulted in us losing the business but via the insured’s choice), raising the deductible payable on any losses, or looking to work with insureds to put in place better risk management processes. Let’s be conservative and say that if the bad book was running at 92% and the overall book at 54% then perhaps it would have been feasible to improve the bad book’s performance to a neutral figure of say 60% (implying a break-even combined of 100%). This would have enabled the insurance organisation to maintain its investment base, to have not lost good business as a result of culling related bad and to have preserved the profit increase generated by the cull.

In practice of course it is likely that some sort of mixed approach would have been taken. The general point is that we have been able to come up with a simple strategy to separate good and bad business and then been able to validate how accurate our choices were. If, in the future, we possessed similar information, then there is ample scope for better decisions to be taken, with potentially positive impact on profits.
 
 
Next time…

In the final part of what is now a trilogy, I will look more deeply at what we have learnt from the above example, tie these learnings into how to pitch a BI/DW programme in Insurance and make some more general observations.
 

Using historical data to justify BI investments – Part I

Peter James Thomas business analytics, business intelligence, data warehousing , , , ,

The earliest recorded surd

This is the first of what was originally a two part piece that has now expanded into three. In the initial chapter, I provide some background on Insurance industry concepts and practices. These are built on in the second chapter (Using historical data to justify BI investments – Part II), in which I offer an Insurance-based worked example. In the final piece, which is cunningly named Part III, I will explain how such an approach to analysing historical data can be used to justify BI investments.

Readers who are already au fait with insurance may choose to wait for the next instalment.
 
 
Introduction

Quite some time ago, when I wrote Measuring the Benefits of Business Intelligence, I mentioned that, in some circumstances, I had been able to leverage historical data (is there any other kind?) to justify Business Intelligence investments. I briefly touched on this area in my recent interview with Microsoft’s Bruno Aziza (@brunoaziza) and thought that it was well past time me writing more fully on the topic.

My general approach applies where there are periodic decisions to be made about a business relationship and where how that relationship has performed in the past informs these decisions. These criteria particularly pertain to the industry in which I ran my first BI / DW project; commercial property and casualty insurance. While I hope that users from other sectors may be able to extrapolate my example to apply to them, it is to insurance that I will turn to explain what I did.
 
 
An insurance primer

I have always wanted to launch a '[...] for Pacifiers' series in the US

My previous article, The Specific Benefits of Business Intelligence in Insurance, starts with a widely used and pig-related (no typo) explanation of how insurance works, both for the insurer and the insured. I won’t repeat this here, but if you are unfamiliar with the area I recommend you taking a look first.

Although of course there are exceptions (event related insurance for example), many commercial insurance policies – just like those that most of us purchase in our personal lives to cover cars and property – have an annual term after which either party can decide whether or not to renew the cover. At renewal, as in the pig example, the insurer will first of all want to assess whether or not they have received more money than they have paid out over the past year. However, the entire point of insurance is that sometimes an event occurs which requires the insurer to give the insured a sum in excess of the premium that they have paid in a given year (or indeed over many years). The insurer is therefore less interested in whether a particular year has been bad – from their perspective – than whether the overall relationship has been, or will become, bad. Perhaps I am over simplifying, but if in most years the insurer pays out less in settling claims than they receive in premium (or ideally there are no claims at all) and if one bad year’s claims are unlikely to negate the benefits accrued in the normal years, then this is good business for the insurer.
 
 
Some rational comments

The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honors the servant and has forgotten the gift

I have bandied about a number of rather woolly concepts in the previous section which include: how much money the insured has paid out and how much they have taken in. Of course these things tend to be more complicated. On the simpler side of the equation, broadly speaking, money coming in is from the insurance premiums paid by customers (but see also the box appearing below).

Investment income

Some insurers are actually relatively relaxed about paying out more in claims that they receive in premium over the life of a policy. This is because of timing differences. So long as the claims are settled some time after premium is received and so long as there are relatively lucrative investment opportunities (remember that?), it may be that the investment income that the insurer can generate while it has use of the insured’s premium will more than compensate for what might be termed an operating loss on the policy. Equally some insurers will have the business goal of – at least in aggregate – always having premiums exceeding claims and thus making a profit on their core underwriting activities. In this case any investment income is added to the underwriting-related profits, rather than compensating for underwriting-related losses. I won’t complicate this article any further by including investment income, but it is a factor in the profitability of insurance companies.

Equally broadly speaking, money going out is normally in six categories:

  1. settlement of claims – often referred to as case payments
  2. claims adjusters’ estimates for the settlement of specific claims that have been notified to the insurer, but not as yet paid – often referred to as case reserves
  3. actuarial estimates of insurance events that have occurred, but which have not yet been reported to the insurer – generally known as incurred but not reported losses, or IBNR (more on this later)
  4. fees paid to insurance intermediaries for placing their clients’ business with the carrier – commission
  5. premiums paid to other organisations to transfer some of the risk associated with specific policies, or baskets of types of policies – facultative or treaty reinsurance
  6. the general expense of being in business (staff, premises, consumables, equipment, IT, advertising, uncollectable premiums etc.)

In the cause of clarity, I will lump commission, reinsurance and the general expense of being in business into Other Expenses for what follows. However please bear in mind that, as is often the case in life, things are not as simple as I will make them out to be.

Rather than dealing in monetary units, insurance companies like percentages; though they then insist on referring to these as ratios. Taking the above categories of money flowing in and out of an insurance company, the main ratios that they consider are then:

 
Insurance Ratios
 
 
Incurred but not reported

Not sure whether the Nixon administration set up any Watergate-related reserves

This concept requires a short diversion as later on I will exclude it from our discussions and will need to explain why. There are some interesting time lags in insurance. Take the sad case of asbestosis (also mentioned in my previous article). Here those unfortunately exposed developed symptoms of the disease in some cases many years later. However if their exposure was in say 1972, they would be covered by whatever Employers Liability policy their organisation held or whatever personal policy they held in the case of the self-employed. An asbestosis sufferer may have changed insurance company ten times since their exposure, but it is the insurance company who provided cover at the time who is liable for any claims.

Rather than waiting for such claims to emerge, insurance companies follow the best practise of recognising liabilities at the earliest point. Because of this, they set up estimated reserves for claims that they may receive in future years (or decades) and apply these to the year in which the policy was in force. Of course in some lines of business, say Property cover, most claims are reported as soon as they occur and so IBNR reserves are low. However in others, say Directors and Officers Liability, or the Employers Liability mentioned above, claims may arise many years hence and IBNR can be a big factor in results.

It should be stressed that IBNR is seldom calculated for a single policy (though it is conceivable that this would happen on a very large risk). Instead it is estimated for classes of policies, often grouped into lines of business, and the same “rate” of IBNR is applied across the board. Of course IBNR is calculated based on experience of losses in the same baskets of policies in previous years, adjusted to take account of current differences (e.g. more or less favourable economic conditions for Directors and Officers Liability, or maybe rising or falling property indeces for Property).

For reasons that are probably obvious, lines of business where most claims are promptly reported (i.e. low IBNR) are called short-tail lines. Those where claims may emerge some time after the period covered by the policy (i.e. high IBNR) are called long-tail lines. Later on I will be focussing just on short-tail business.

[Incidentally, improving this process of estimation is one of the specific benefits of Business Intelligence in insurance that I highlighted in my previous article.]
 
 
Underwriting Year

Fundamental particles of the Underwriting Year

Something else may have occurred to readers when considering the time lags that I reference in the previous section, namely that while a policy may last from say 1st January 2006 to 31st December 2006, claims against this may occur either during this period, or after it. The financial statements of an insurance company will place claims in the period that they are notified or settled. So in the above example, a claim paid on 23rd April 2008 (assuming the financial and calendar years coincide) will be reflected in the 2008 report and accounts.

However it is often useful for analysis purposes to lump together all of the claims relating to a policy and associate these with the year in which it was written. Again in our example this would mean our 23rd April 2008 claim would be recorded in the Underwriting Year of 2006. So an Underwriting Year report comparing 2006 and 2007 say would have the premium for all policies written in 2006 and all the claims against these policies – regardless of when they occur – compared to the premium for 2007 and all the claims against these policies, whenever they occur.

Because of this, Underwriting Year reports provide a good measure of the performance of policies (or books of business) over time, regardless of how associated losses are dispersed. By contrast Calendar Year (i.e. financial) reports will often have premium from policies written in say 2010 combined with losses from policies written in say 2000 – 2010.
 
 
Tune in next time…

BBC ANNOUNCER: Tune in to the next exciting instalment of... CAST: Dick Barton, Special Agent!

Having laid some foundations, in the next article, I will draw on the various concepts that I have introduced above to offer a worked example. In the closing chapter, I will explain how I such an example to justify a major, multi-year Business Intelligence / Data Warehousing programme within the insurance industry.