Bayes’ theorem on AirAsia’s Bribery Allegation

Analysis, Bayesian, Decision Making, Probability / Monday, February 10th, 2020

4 February 2020

UK SFO (Serious Fraud Office) had implicated AirAsia directors/staffs on 31 Jan 2019 for involvement in bribery allegations with Airbus.

Below are the key points. You can also refer to the links below for further readings.

  • SFO enters into €991m DPA (Deferred Prosecution Agreement) with Airbus.
  • Improper payment consists of $50m to AirAsia directors/staff as sponsorship for the Caterham F1 team in exchange for securing Airbus orders.
  • AirAsia, Tony Fernandes, Kamarudin Meranun denies allegation.
  • MAVCOM (Malaysia Aviation Commission) to probe AirAsia if they broke aviation law.
  • MACC (Malaysia Anti-Corruption Commission) launched an investigation against Tony Fernandes and Kamarudin Meranun.


Additional readings

SFO vs Airbus SE JudgmentSerious Fraud Office

AirAsia’s response to Airbus’ Deferred Prosecution AgreementAirAsia

AirAsia directors/staff got RM204 mil bribe from Airbus, court docs sayFocus Malaysia

SFO enters into €991m Deferred Prosecution Agreement with Airbus as part of a €3.6bn global resolutionSerious Fraud Office

MACC investigating Airbus bribe claims against two AirAsia executivesThe EDGE Markets


If you’re an AirAsia long-term shareholder, this bribery scandal is something worth the time to ponder because it can significantly impact the business long-term prospect. Unlike coronavirus that will most likely dampen earnings for the next few quarters, this scandal can significantly impact the business’ medium to long-term cash flow.

How should you deal with this uncertainty so you can make the best investment decision? 

We can use Bayes’ theorem. I have written a bit about Bayes’ theorem here (point 4) so I’m not going to repeat it here. But in short, it is a simple framework to update and quantify your belief every time you receive more information. 

Before we can apply Bayes’ theorem in this case, we need to understand what we are trying to find out. Generally, what everyone wants to know is whether Tony Fernandes and/or Kamarudin Meranun did accept the bribe from Airbus. But that is not very useful. Because bribery is subjective. What is considered as a bribe for UK SFO might not be considered as bribe for MACC, for example. Therefore, whether Tony and/or Kamarudin are found guilty is not as important as what the consequences are if they’re found guilty. If authorities found Tony guilty but only give him a warning, that is unlikely to affect AirAsia’s long-term prospect.

From a long-term shareholder’s point of view, there are only three things that will change AirAsia’s valuation negatively:

  1. Any outcome that would force Tony Fernandes to relinquish his control of AirAsia. Which includes selling all of his stakes, unable to take charge of the direction of AirAsia and so on.
  2. Any outcome that would stop Tony Fernandes from running AirAsia i.e more than 3 years of jail time, more than 3 years ban from managing AirAsia or aviation industry and so on.
  3. Any outcome that would significantly impact AirAsia’s future cash flow i.e significant fines or any restriction imposed by authorities that inhibit their revenue or increase the cost of doing business. 

There is little dispute that most of AirAsia’s success can be attributed to Tony Fernandes. So for a long-term shareholder, the two black swans from the bribery allegation are:

  1. AirAsia receive fines (big ones)
  2. Tony Fernandes (and/or Kamarudin) lose control of AirAsia

These are quite different from forecasting the probability of whether Tony is guilty of bribery. Because as mentioned earlier, the verdict is not important. The consequences of the verdict are.

To make this easier, we will apply Bayes’ theorem separately to both hypotheses. We need 3 things to apply Bayes’ theorem:

  1. Prior probability 
  2. Probability as a condition of hypothesis the being true
  3. Probability as a condition of hypothesis the being false

Hypothesis: AirAsia would receive a fine 

Prior probability – What is the initial estimate of how likely AirAsia will receive a fine for bribery before the bribery allegation appears? 

It is not that uncommon for AirAsia to receive fines; $2 mil fines from MAVCOM in 2019; $200K by Australian Court in 2012 etc. But bribery fines? I would consider that as rare if not impossible considering AirAsia’s reputation. Or we can use base rate to think about how often do we see bribery fines? Airbus $4 bil fines, Ericsson’s $1 bil fines bribing government etc but they’re far in between. I’ll put it at 13%. Keep in mind, this is subjective. There’s no right or wrong, and it is what Bayes’ theorem is all about. The starting point is not important as long as you continue to move towards the direction of less wrong. 

Condition of the hypothesis being true – What is the probability of bribery allegation if AirAsia receives a fine for bribery? 

Next, we need to estimate the probability that if AirAsia receives a fine for bribery, how likely will we see this allegation? The probability will be 100% because bribery investigation always precedes punishment. 

Condition of the hypothesis being false – What is the probability of bribery allegation if AirAsia doesn’t receive a fine for bribery? 

Then we consider the opposite: How likely is it for us to see a bribery allegation if AirAsia doesn’t receive a fine at all? There are many reasons for not receiving a fine despite the allegation. AirAsia could get acquitted by MACC and MAVCOM, or receive a warning without any fines and so on. I’ll put it at 50%.

As shown above, my belief of whether AirAsia will be fined has increased from 13% to 23% following the bribery allegation. This 23% will become the prior probability when we receive more new evidence. Which will be the continuation of this post. 

Next, we are going to update our belief on whether Tony will lose control of AirAsia.

Hypothesis: Tony Fernandes to lose control of AirAsia 

Prior probability – What is the initial estimate of how likely for Tony to lose control of AirAsia before the bribery allegation appears? 

There are several ways for someone to ‘lose control’ on a company they own. Voluntary selling down their stakes, like what Steve Jobs did when he got ousted by Apple’s board in 1985. Forced selling, although rare in corporate history, but not entirely impossible. Forced selling can come from government authorities banning a person from the aviation industry for several years. Most often being pressured to do so. The last one would be a resignation, effectively relinquishing power to have any say in the future direction of AirAsia. 

How often do we see any of the above scenarios happen to a majority shareholder? I’ll put it at 3% prior probability given Tony is the largest shareholder of AirAsia and in the corporate history of Malaysia, there have been little cases where a majority shareholder is forced to relinquish control of his company. 

Condition of the hypothesis being true – What is the probability of bribery allegation if Tony loses control of AirAsia? 

If Tony loses control of Airasia, how likely would we see the evidence of bribery allegation? In one way or many, Tony can lose control of AirAsia for many reasons other than bribery allegations, but definitely bribery is a big reason. I’ll give this 60%.

Condition of the hypothesis being false – What is the probability of bribery allegation if Tony does not lose control of AirAsia? 

If Tony doesn’t lose control of AirAsia, how likely is it to see the evidence of bribery allegation? Again, Tony can get acquitted by MACC, or get fined, which I believe is a more likely scenario. So a bribery allegation doesn’t always mean Tony will lose control of AirAsia. Far from it. So I’ll put 50% on this being false

My initial belief that there’s a 3% probability of Tony losing control of AirAsia has gone up to 4%, a very small change. 

Of course, these are my subjective guesses, and an initial estimate as to whether these two hypotheses are going to happen. As more new evidence shows up, I’ll revise my belief as necessary. Your belief will not be the same as mine, could be higher or lower. Someone might think the possibility of getting fined is 90% rather than 23%, and that’s fine. The beauty of Bayes’s theorem is that over time as more evidence shows up, it will allow our belief to become less wrong and ‘converge’ towards a smaller range of possibility. 


The advantage of using Bayes’ theorem is by quantifying your belief, you have a better idea of what your belief entails. And as you practice Bayesian thinking, you get better at making decisions because it forces you to consider alternative outcomes, which allow you to have a more balanced judgment. In the scenarios above, we consider the possibility of something else that could happen as much as what is likely to happen. What can explain the allegation aside from the obvious? What do we expect to see if this evidence shows up? You start thinking in decision-tree. You become more comfortable dealing with uncertainty and cultivate an open-minded attitude rather than succumb to confirmation bias.

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