Wednesday, 25 July 2018

Prudential Singapore (Insurance), Agents and Digitisation, some thoughts

A recent article (1) highlights Prudential’s  agents’ unhappiness with ‘digitisation’ of their business. The 2 main questions are whether this unhappiness is founded or not and what impact this has for the future of the insurance industry, especially in line with the emergence on Insurtech.

The complaints centre on the fact that Prudential is making available to customers via online channels the same products that agents offer to their prospects, including the basic/workhorse/most popular ones. Prudential management has been quick to state that the agents returns will not be affected.

So what is the most likely scenario?

Just think of it this way, if, without premiums going up, Pru is able to finance a digital platform, maintain it, and still pay agents full commission on sales they have nothing to do with, then it must mean that the premiums Pru customers are paying today are too high since they can pay for the extra costs of the platform...

Assuming that is not the case, so why would Pru be losing money? It is possible that Pru believes that agents do a terrible job of cross-selling products, and therefore the platform would pay for itself based in sales of products they wouldn’t otherwise have sold.

I worked in the insurance industry for a while, and of course there are customers who are more or less abandoned by agents and left to their own devices. Agents most often focus their efforts on a few customers whom they believe are more likely to buy or people they are closer to. But this doesn’t mean that simply having a platform where these less served customers could reach out to pru and make purchases would generate enough business (minus agents’ commissions) to fund the platform.

I would think that what agents are really worried about is the ‘customer ownership’. Again from my experience in the industry, agents believe it is them who own the customer relationship, not the insurance company. When an agent leaves, there is a process to deal with these ‘orphan’ accounts; they are basically reassigned to another agent, usually from the same agency.

It is possible that this may change, with the insurance company choosing to serve these customers via the platform rather than reassigning to agents, effectively having the insurance company now own the customer.

Furthermore, the company could enrol customers directly via the platform, removing agents from the picture for simple products, and using a pool of trained agents for products that require it.

In fact agents could fear that their role in the insurance industry will change drastically, and there may be a need for fewer of them, to act more as product specialists than engaging in the whole range of activities they cover today.

Does that mean that digitisation is not a good thing for agents?

The answer is no.

Digitisation is important, but it must be part of a transformation of the organisation towards being data driven. I believe that, in order for an organisation to become truly data driven, all its component parts have to be more or less  equally data driven – like a chain whose weakest link determined its strength, the least analytically mature component of an organisation determines its overall maturity and how far it is from being data driven.

I am an outsider, and I have no clue how analytically mature Pru is. However, as a customer of Pru, I get to see some of it. For example, I have received calls from Pru agents/agencies I have had no prior contact with, asking me about my policies with them; this should not happen. Usually only my agent or his agency should have access to what I purchased, not other agents/agencies. To me it means their databases are not that secure. The best part is that when I told my agent to complain to HQ, he basically said it happened all the time... So I am a bit skeptical about how data driven the organisation is.

Anyway, that being said, I think it would have made more sense for Pru to work together with their agents, rather than take the approach they have, which leads to some legitimate fears among their agency force.

Pru itself has said that Singaporeans are under insured (2), so if despite the personal attention that agents can give to their customers, these are still under-insured, what makes Pru think that the customer would by himself/herself buy from the platform?

Singapore is a relatively mature economy, insurers have been here for ages, and even then people are still under-insured. People do not decide to under-insure themselves, it’s most likely a matter of education, and that’s where the agent, if properly trained, can bridge the gap. People usually learn better in small settings; just like another Singaporean institution, the private tutor helps students in 1 on 1 or small class settings rather than in giant classrooms (or even worse on youtube videos...)

Basically, to me, agencies like Pru, still need their agency force to be on-board, they have a crucial role to play. (And my friendly agent would hit me with his hockey stick if I said otherwise). Education, and really doing proper financial planning for their customers is key. Sure there are stuff that can be bought ‘over the counter’ like travel insurance and are hugely successful (4). But not every insurance product is in that category.

Does that mean then that the platform is useless?

Of course not. I have even written how the whole insurance process could potentially be run on the cloud (3), so I get the importance of digitisation. To me the platform and analytics should support the agent and enable him/her to serve his/her customers better.

Properly built analytical models should give an idea of not only the potential need of a customer, but also the right timing. The simple way of working with agents to serve customers would be to:
  1. Inform the agents of whom, among their customers, is a good target for a specific offer now
  2. Allow the agent to personally contact some of the customers he/she wants to, get a commitment accordingly and a simple easy to use feedback loop.
  3. Contact the rest making reference to the agent if customers want to take action.

As a Pru customer, I have received SMSes that make me offers, sometimes they include my agent’s contact details in case I want to follow up. Well, none of their offers interested me; and the best part is that my agent knows that. We meet up once a year of so, and go over the policies, life… and he knows I do not need more coverage for now.

Is that all there is to digitisation or becoming data driven?

No. Far from it.

Insurance companies are made up of many parts, and the selling is only a small part of it. For example, once the customer decides to buy a policy (whether it is via a platform or an agent) how quickly is the policy sent back to the customer for take up?

Ideally this should all be straight through processing, especially for existing customers whose KYC (Know Your Customer) is still valid, all that’s needed is to get the customer to confirm the validity or make necessary updates. Then the insurer should have the validated data of teh customer and can proceed with the application proper.

Electronic forms are the best; while the platform should have this feature by default, it takes little to provide agents with the equipment necessary, a tablet for example (issues like online or offline are dependent on the specific market) with in-built checks to ensure all information necessary for an application is available and in correct form.

Then with correct use of technology, standard cases can be approved almost instantly; I have reproduced a diagram from my blog (3) below:



In such a case, underwriters need only work on exceptions. Using technology and analytics in this way makes the policy issuance process much more efficient, customers get their coverage faster and agents can focus on educating and selling rather than to have re-works, and the organisation gets policies in faster and or good enough quality. A win-win-win situation.

Similarly the claims process can be automated, again I would refer you to my earlier blog (3).

So the question is, has Pru done all this?

Well it has tried (5), but while this is a good beginning, this is far from customer centricity. Basically Pru gives discounts on premium if no claims are made. The interesting thing is that customers might choose the game the system, pay for small ailments rather than claim, enjoy lower premiums, and hit the insurer on the big ticket if any. I wonder whether behavioural changes have been taken into account in pricing: no claims doesn’t necessarily mean healthy, and incentivising gaming of the system is not usually a good idea. People are not stupid. Instead of piecemeal attempts, customers should be engaged, the organisation become customer centric rather than organisation or product centric as seems the case above.

And I didn’t even get into data driven customer centric  product design...

Sorry if this sounds like Pru bashing, it wasn;t my intention. But I see this episode as a case of an attempt at digitisation without looking at the big picture of being data driven and customer centric. I am sure Pru is not the only insurer in this situation. 

As long as there is no effort by insurers to become truly data driven and customer centric (and this is a process, not a single big bang), they will be vulnerable to more nimble technology driven players, picking off specific profitable segments, and that would be a double loss to the traditional insurers.

Friday, 13 July 2018

The world cup predictions prove: you need to use the right algo with the right data to have a chance at the right outcome.



I had written a simple blog about the English Premier League, using simple analytics to uncover some patterns and was planning to publish it. Then I thought I would see if the same patterns applied in the world cup. But have you seen so many worldcup predictions, and how spectacularly wrong they were? Hey, I can’t say anything about whether the octopus or the guinea pig can predict the winner, but I can certainly see when the tools/method/data used for prediction are unlikely to suit.

Let’s start with UBS(1)...

The technique used was simulation (Neymar’d be happy, so I guess Brazil would have an edge...). The first weird thing is that they decided to include Italy, a team that didn;t even qualify... Italy, again, a team that did not qualify, is ranked 12th...

This is where they got interesting,what did they simulate? Obviously not the tournament, since they have a phantom team. They input various valirables including the ELO ranking (2) of teams (claimed to be an objective measure of how good they are) into a statistical model and ran a series of monte carlo simulations.

The fun thing is that even while ELO ratings are ranking the performance of a team in a game, weighing the result by importance of the game (of course a win in a tournament is more important than in a friendly) among others. But interestingly, according to Wikipedia, “there is no single nor any official Elo ranking for football teams

It becomes hazier when these are claimed to be econometric methods (3).

Oh and by the way Italy was added to honour the nation...

Well if my aim was to come up with an accurate prediction I would try to make the best prediction possible, not use numbers that are not objective (although I may say they are), and include teams to honour them.


So, I would classify this prediction and a PR smoke and mirror exercise with some smoke coloured in teams colours to make you believe you are at a stadium. Don;t worry, there was very little violence at the World Cup, kudos to the Russian security.

Next, let’s go to Goldman Sachs...(4)

Now, no such old fashioned stuff like econometrics and monte carlo, too old and old school. Goldman Sachs went for everyone’s favourite: AI!

They used 200,000 models and simulate 1 million variations. Impressive. They used player and team attributes to forecast specific match scores, and then simulate the whole tournament. Now that makes more sense than having a phantom team...

Where is gets interesting is that they say “Brazil is expected to win its sixth World Cup title, defeating Germany in the final by an unrounded score of 1.70 to 1.41” mm... do they watch football? 1.71 to 1.41? is that 1-1 and goes to extra time or 2-1 and Brazil wins? Do you round up or down? Do they actually know there is extra time and penalty kicks?

Hmm... Looks like there is some domain knowledge lacking...




Fret not, Goldman Sachs was at it again (5). As the tournament progressed, they revised their predictions. After England beat Panama, they revised the prediction, now the final would be Brazil 1.59 England 1.17. hmmm still these pesky decimals... (also I guess it meant their initial model predicted England would not beat Panama...)

Still lacks understanding of the tournament, looks like all games are assumed to start with fresh squad... (at least equally fresh: no extra time)


But Goldman Sachs were not done yet! (6) After Belgium beat Brazil, they updated their prediction and made Belgium the favourite (32.6% chance of winning the world cup). Note that these predictions were made at semi-final stage; Goldman Sachs predicted the final would be Belgium England. Well they got it 100% wrong...






I would say Goldman has the right idea, simulate the whole tournament, use player and team stats... but not understanding football or at least the tournament game is not a good thing...

Any other method?

Well someone tried using graph theory (7).

I recommend this article, it is fun, easy to read and pretty.

But it eventually boils down to: the country whose players are playing in leagues where most players also play is most likely to win.  That’s what eigen vector centrality boils down to in this case. (ooops, sorry I try to avoid technical terms, but sometimes they come out)



This is a nice idea. If players at the world cup are the better ones, then the teams where many of them play would be of higher quality, Therefore, the teams with the more players in high quality teams are likely to have higher quality and therefore likely winners.


Makes sense, right? Especially if you take into account the fact that world cup teams are balanced (if you have 10 brilliant goal keepers in the toughest league, it won’t matter much here as every team can only have 3 goalkeepers). However it does ignore that football is a team sport, and although your squad matters very much in a tournament, you need some balance. If you have amazing talent in midfield but no strikers, who will score for you?

I would say I like this approach, can be made better with some football thinking; afterall football is a team sport; a team has to be more than the sum of individuals, the tactics matter, so does the manager who seems to have been ignored in all this.

So you  may ask, while I spit in everyone’s soup, is there a soup I would drink? The answer is yes! It may have sounded like a joke, but, to me, the most realistic prediction of the world cup that I have seen is a simulation of the whole tournament, with data on players, managers, tactics, from football manager (8)

Why?

It’s very simple, the data inside the game is of very high quality, the attributes of the players, their preferences, they style they play, how often they get injured... is all measured and included in the game; same for managers.

Furthermore, the whole tournament is simulated; has been 1000 times. And the results compiled.
And the likely winner is France.

To sum up, choosing the correct data to suit your problem (player and manager statistics, team statistics), the correct algorithms (simulating the whole tournament by the rules of the tournament) gives us a good chance to get the wanted outcome.

Hence France is likely to win the world cup, or so says the best analytical model I have seen.

But personally, I am likely to be rooting for Croatia, else my friend Genti may not forgive me ;)

And in case you are wondering, if you have seen my linkedin comments about the tournament (9), Tonton Zola Moukoko is a player where FM got it wrong; not everything is about data and sometimes life takes a turn (10)

  1. UBS predictions for the world cup https://www.businessinsider.sg/who-will-win-the-world-cup-2018-2018-5/?r=US&IR=T
  2. https://en.wikipedia.org/wiki/World_Football_Elo_Ratings
  3. https://www.cnbc.com/2018/05/17/world-cup-winner-predicted-by-ubs.html
  4. Goldman Sachs predictions for the world cup (https://www.businessinsider.sg/world-cup-predictions-pick-to-win-it-all-goldman-sachs-ai-model-2018-6/?r=UK&IR=T)
  5. Goldman Sachs predictions for the world cup  again https://www.ft.com/content/804a21be-7915-11e8-bc55-50daf11b720d
  6. Goldman Sachs predictions for the world cup  again and again https://www.businessinsider.sg/world-cup-predictions-goldman-sachs-ai-model-belgium-england-final/?r=US&IR=T
  7. https://cambridge-intelligence.com/graph-theory-world-cup-winner-prediction/
  8. https://www.youtube.com/watch?v=OxX_tdzFpgk
  9. https://www.linkedin.com/feed/update/urn:li:activity:6422819043379646464/
  10. https://offsiderulepodcast.com/2017/07/21/championship-manager-tonton-zola-moukoko/