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45% lower Economic Capital requirement on a typical corporate loan portfolio ? YES, it is possible with ALGOSAVE. And not just during the Tour de France.

45% lower economic capital requirements on a typical corporate loan portfolio ? yes it is possible with ALGOSAVE proprietary financial technology. And not just during the Tour de France.

45% lower economic capital requirements on a typical corporate loan portfolio ? yes it is possible with ALGOSAVE Financial Technology combination of
– borrower & seniority specific Loss Given Default (LGD) term-structure, and
– deep financial-analysis driven Default Probability (PD) and Joint-PD correlation term-structure.

Background :

The main drivers of Economic Capital estimates are :
– Loss Given Default (LGD),
– default probability (PD),
– and at a credit portfolio level, Joint PD correlation.

Let’s review each of those critical component with ALGOSAVE proprietary Financial Technology and see why and how ALGOSAVE helps banks save substantial capital.

1 – Seniority specific and economic-cycle sensitive Point in Time Loss Given Default (LGD) term structure:

ALGOSAVE addresses the 2 main drawbacks of traditional LGD “Beta” distribution model :
– uni-modality : where empirical evidence show that recoveries are – at least – bi-modal, with typical peaks at 20% and 80%
– difficulty to cope with “point masses” at 0% and 100%, where empirical evidence also show that LGD are often located in those areas.

Simple solution : ALGOSAVE financial technology GOT RID of LGD Beta model.

ALGOSAVE proprietary Financial Technology (FinTech) projects corporate financial statements – 100 data points – using Montecarlo simulations. This allows ALGOSAVE FinTech to reflect each corporate specific asset and leverage structure in each of its thousands of Montecarlo simulations paths at 3 different level of seniority : secured, unsecured and subordinated.

For instance, let’s assume that on a given path of its Montecarlo projections, McDonald (MCD) net debt is greater than its Enterprise Value in year 1. This comes – for instance – from higher capital expenditure than average. Assuming that, at the same time, MCD must draw on its Revolver Credit Facility – for instance to fund a greater than optimal sales growth rate – ALGOSAVE Financial Technology “pushes” MCD in default on that specific path.

ALGOSAVE then computes recovery using MCD specific debt and MCD specific asset liquidation value upon default . The latter takes into account 2 possible Machine Learning based outcomes : (1) a gone concern liquidation value, where MCD is taken over (at a multiple of sales or EBITDA) and keeps operating – or (2) a fire sale of MCD assets which price depends on the then Machine Learning Based economic cycle.

Following this methodology, this graph depicts the unsecured 1-year LGD distribution of a typical portfolio of 100 corporates selected from ALGOSAVE issuer database. And not Singapore skyline. The x-axis measures the LGD between 0 and 1.0 while the y-axis measures the number of observations of each bar in the histogram

First observation ? Both the multi-tail and “point mass” nature of ALGOSAVE proprietary LGD distribution clearly shine.

The blue LGD histogram depicts the LGD distribution for OECD consensus growth scenario with 59% average LGD – close to Basel Foundation 55% LGD and CDS 60% standard ISDA LGD. Examining the red histogram – which depicts the LGD distribution for ALGOSAVE stressed macro economic scenario – it is interesting to notice the marked shift of the LGD distribution from left (15% “blue” LGD) to right (100% “red” LGD). The average ALGOSAVE stressed-scenario LGD stands at 72%.

2 – Point in Time Probability of Default term structure :

ALGOSAVE Probability of Default (PD) term structure is calibrated on capital market PD term structure which comes from CDS, bonds and/or secondary loan trading.
It can also be calibrated on ALGOSAVE clients’ internal Point-in-Time default probability term structure.

This calibration gives ALGOSAVE PD term structure its unique “live” flavor which – by the way – is required by new accounting regulations.

3 – Joint PROBABILITY OF DEFAULT correlation :

ALGOSAVE proprietary joint PD correlation technology is a lot lower than its ill-fated CDS and equiy price correlation proxies. Those are full of “market noise” such as iTraxx CDS sector and ETF trading and hide real default correlation.

But, CLO traders and loan/bond portfolio managers are well aware of this phenomenon whereby actual default correlation – to the exception of those in the highly regulated financial sector – is a lot lower than its capital market proxies.

You may be interested by one of our latest posts which dealt with this issue in greater detail. Read it all here.

Conclusion – ALGOSAVE proprietary Financial Technology helps banks save 45% in economic capital

Indeed, putting all things together – LGD, PD and Joint PD correlation – where traditional Expected Loss model points toward a 3.30% Economic Capital requirement (on the sample corporate portfolio), ALGOSAVE proprietary Financial Technology only requires 1.80% Economic Capital at 99.9% confidence level.

A 45% saving on Economic Capital !

Now, that’s something worth celebrating. And, not just among our Welsh Tour de France cycling fans 🙂

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The missing link : PD-LGD correlation – almost – holds true throughout the term structure of forward looking Point-in-Time PDs and LGDs

There is empirical evidence of high Default – Loss Given Default correlation. This historical phenomenon is also known as high PD-LGD correlation.
Indeed, historically and during periods where there is a relatively high number of corporate defaults, the average Loss Given Default – LGD – is also relatively high. And, the opposite is also true. During periods where there are relatively few default occurrence, LGDs are relatively low on those occurrences.

Does this empirical observation also hold true throughout the term structure of forward-looking and Point-in-Time PDs and LGDs ?

ALGOSAVE new valuation paradigm confirms that PD-LGD correlation – almost – holds true throughout the term structure of forward-looking & Point-in-Time PDs and LGDs.

Contradicting this intuitive correlation, there are also cases whereby benign macro-economic LGDs are as high or even greater than stressed LGDs. This phenomenon mostly happens towards the end of the term structure.

We will illustrate this finding with a few examples that all come from ALGOSAVE CORPORATE ISSUER DATABASE.

For instance here are ALGOSAVE unsecured 3-year,5-year and 10-year LGD distributions for 3 Integrated Oil and Gas companies : BP PLC., Occidental Petroleum and Royal Dutch Shell.

The blue bars in the histogram illustrate the LGD distribution in benign – consensus – macro economic scenario, while the red bars depict the LGD distribution in ALGOSAVE stressed macro-economic scenario. The first, second and third histograms – from top to bottom – respectively depict the distribution of the 3-year, 5-year and 10-year unsecured LGD.

For instance, in the case of BP PLC – the first histogram at the top – whereas the consensus 3-year unsecured LGD (in blue) are distributed between 40% and 63%, the stressed LGDs (in red) are distributed between 73% and 98%. As shown in the other 2 histograms, this marked difference between ALGOSAVE 2 LGD regimes (benign and stressed) also holds true both for the other 2 corporates at the 3-year point. Although there is already a little overlap for Occidental Petroleum.

This PD-LGD correlation almost holds true throughout the scenario-sensitive LGD term structure. Indeed, from 3-year to 10-year, the LGD of ALGOSAVE benign economic scenario – associated to lower PDs – are generally lower than those of ALGOSAVE stressed scenarios – associated with higher PDs.

However, those 2 LGDs regime sometime overlap. This is the case of BP PLC and Royal Dutch Shell 10-year LGD distributions, where the blue bars (benign scenario) overlap with the red bars (stressed scenario)

Validating empirical findings, ALGOSAVE confirms that PD and LGD are correlated throughout the term structure albeit with some overlap.

Those LGD term structure are delivered by ALGOSAVE ISSUER DATABASE and are directly related to the the here-under ALGOSAVE scenario-sensitive PD term structure and EV distribution :

  • Forward looking, scenario-sensitive Default Probability Term Structure  : consensus (in blue), very good macro economic scenario (in green), stressed macro economic scenario (in red) for BP PLC., Occidental Petroleum and Royal Dutch Shell
  • Consensus (in blue) and stressed (in red) Asset Value (EV) distributions for BP PLC., Occidental Petroleum and Royal Dutch Shell.



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Good century-old DuPont-analysis helps Banks SAVE CAPITAL : what an explosive surprise !


Good century-old DuPont-analysis helps Banks SAVE CAPITAL : what an explosive surprise !

ALGOSAVE confirms DuPont analysis profound wisdom : LGD CORRELATION is ROCK-BOTTOM LOW and helps financial institutions SAVE CAPITAL.

And, especially for high grade borrowers, ALGOSAVE has another BIG surprise in store on PD-LGD correlations.

DuPont Analysis comes from the DuPont Corporation that started using this formula in the 1920s.
DuPont explosives salesman Donaldson Brown invented this formula in an internal efficiency report in 1912.

More than a century after that, ALGOSAVE CORPORATE DATABASE shows that DuPont analysis also hides another even more profound wisdom : the CORRELATION of Loss Given Default (LGD) between corporates in a given industry is ROCK BOTTOM LOW to negative. This means IMMEDIATE CAPITAL SAVING for corporate lenders.

In The Beginning, long time ago.

DuPont analysis tells us that the Return On Equity – ROE – can be decomposed in 3 items

Net Income / Sales = Profitability
Sales / Total Assets = Asset Efficiency
Total Assets / Average Shareholder Equity = Financial Leverage

  1. DuPont analysis also tells us that profitability is mostly matter of technology.
    In other word, when a company is part of a given industry, it “inherits” the industry profitability which is technology-dependent.
    For instance, if you distribute food in the US, your expected Net Income / Sales cannot be too-far away from your peers. Unless you have a completely different service or…technology.
  2. DuPont analysis also tells us that Asset Efficiency is mostly a matter of the corporate competitive landscape.
    Indeed, in order to increase this ratio, the company will have to gain market share <=> increase marketing expenses, lower price, increase inventories to prepare for increased sales, and give longer credit to its clients <=> lower net Income and higher Total Assets.
    Profitability and Asset Efficiency are interdependent.
  3. So, in order to increase its ROE and make a difference, the company’s management is mostly left with the latest ROE key driver : ITS degree of Financial Leverage. Let’s also keep in mind that an increase in financial leverage also means an increase in the Equity BETA, with its consequence on WACC and ultimately on the Expected ROE.

From DuPont analysis … to rock-bottom LOW LGD CORRELATION

If DuPont analysis holds true – and indeed corporates in the same industry mostly drive their ROE thru financial leverage – then when they go bankrupt together, we should be expecting little correlation between their respective Loss Given Default : they all finance their assets in a different way to make a difference in their respective ROE.

The challenge : historical high-grade corporates LGDs are scarce <=> Default of large and solid corporates are a rare event.
Concomitant defaults of such corporates are even rarer. So that measuring historical LGD correlation is a challenge.
Estimating forward looking and Point in Time LGD correlation is a double-challenge.

Algosave technology raises to the challenge and delivers its clients exactly that : forward looking and Point in Time stress-tested LGD correlations.
ALGOSAVE delivers those for all the 5000 borrowers in ALGOSAVE CORPORATE DATABASE.

Let’s take an example on our favorite Integrated Oil and Gas basket : 11 high-grade global corporates

ALGOSAVE CORPORATE DATABASE delivers the following data : average 5-year unsecured stressedLGD, current and stressed cumulative 5 year PD

  1. Before the bigger surprise, let’s have a look at 5-year average stressed unsecured LGDs for each of those 11 corporates.Although the average LGD of those asset-heavy corporates is close to 49% – which is close to CDS standard LGD  – there is a marked difference between BP P.L.C. 81% 5-year LGD and Husky Energy 22% 5-year LGD.
    This already confirms DuPont analysis differing financial structure, albeit in a static and average way.
  2. Let’s also compare current market 5-year PDs and ALGOSAVE stressed 5-year PDs.
    Although current 5-year cumulated PDs are multiplied by a factor of 5 when stressed (increase from 4% to 20%), BP and Statoil are multiplied by a factor of more than 10, whereas Husky Energy is only multiplied by a factor of 2.5.
    This also confirms DuPont analysis about differing financial structure, albeit in a static and average way.
  3. Finally, last but not least, ALGOSAVE CORPORATE DATABASE unique and CAPITAL SAVING deliverable : Forward-looking, Point in Time and stressed 5 year unsecured LGD correlations

    On average stressed LGD correlation is a ROCK BOTTOM -0.01
    The highest LGD correlation is a small 0.11, between Royal Dutch Shell and EXXON.
    The lowest LGD correlation is also a small -0.13, between TOTAL SA and Suncor Energy.
    Thinking “DuPont”, this should not be surprising. Indeed, DuPont analysis implicitly states that in order to drive its ROE, company management is mostly left with carefully choosing its Degree of Financial Leverage. Hence is case of concomitant default of two corporates, lenders should not expect to loose the same amount of money on their debt.As a conclusion, ALGOSAVE confirms DuPont analysis profound wisdom : LGD CORRELATION is a ROCK-BOTTOM LOW and CAPITAL SAVING critical metric.
  4. Last but not least ALGOSAVE ALSO offers a surprise on PD-LGD correlation in stressed scenario for high-grade borrowers.
    This will be the object our our next post. Please stay tuned.If you like this post, do not hesitate to ask for you free subscription :

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From Stress-Testing to Relaxing : what if banks could really SAVE CAPITAL ?

In stress-test scenarios, the assumption is : high correlation for everything.

We, bankers – and I used to be one – have been caught with our hand in the low correlation cookie jar. We decided to take action so that it won’t happen again.

To add to our comfort – or rather, discomfort really – markets are confirming our fears and react exactly this way . Looking at CDS prices one could even ask : why bother having one CDS per issuer ? Let’s just have one CDS per industry. One size fits all !

For example, the first CDS 5-year graph is that of Royal Dutch Shell (RDSA), the second one is that of Total SA, and the third one is that of Statoil (renamed Equinor).

Thanks to for those graphs

Guessed the 85% correlation ?

Immediate consequence : in stress-test scenarios, banks must take into account this thru-the-roof – 85% – CDS implied Probability of Default correlation and put aside a lot of capital.

Why ? since defaults of rock-solid corporates – such as the one we just looked at – are very rate events, there are very little statistics of actual default contagion in high grade credit portfolios.

So, in order to measure default contagion, lenders are left using whatever proxy they can find. But, although CDS – or share, or bond – price correlation is the most obvious one, at 85% it is also a killer !

What if one could statistically measure this domino effect ? That is precisely what ALGOSAVE FinTech does. And it is definitively worth our while and that of our clients.

Indeed, ALGOSAVE shows that, even in very bad economic scenarios, default correlation is actually far lower than CDS thru-the-roof correlation suggests.

Here is an example on our favorite Integrated Oil and Gas portfolio.

For instance, whereas Total/RDSA 5-year CDS correlation is close to 85%, ALGOSAVE shows that it is actually closer to 25% even a very bad economic scenario. Arguably, it is still more that 3 times Total/RDSA conditional default probability in consensus OECD macro-economic scenario. But it is a far cry from the 85% CDS correlation mark.

Well done, from 85% down to 25% default correlation that’s tangible capital saving. It is well worth the time you just invested into reading this article till the end. And, should you want to see these default correlation for another industry, please leave us a comment here. We will graciously send this to you.

ALGOSAVE proprietary credit database is full of those precious nuggets … and some other too such as equity analyst favorite WACC and Beta. But, let’s leave these for our next article.

All the Best

ALGOSAVE team. 13th of June 2018.



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Putting all things together : how is the Loss Given Default – LGD – of my credit portfolio affected by the domino effect ?

In the Eurovision LGD Contest post Algosave shows that BP plc forward-looking Loss Given Default (LGDs) are the highest among 10 western major integrated oil and gas producers.

In the Credit Portfolio domino effect post, Algosave Fintech also delivered powerful insights into issues of credit dependencies, for instance by showing which of those 10 corporates was the least “toxic” in the portfolio.

Putting all things together, and putting Algosave FinTech to the test : when another corporate of the portfolio defaults, (1) what happens to BP relatively high LGDs, and (2) is BP – then – in a high, average or low LGD regime ?

For instance, let’s focus on what happens to BP LGDs if Chevron was to default. Why Chevron ? because it is the “toxiest” corporate for BP plc. Indeed, If Chevron was to default, Algosave Fintech shows that BP 5year PD would jumps from its current 3.5% to 11.8% = more than 3.7 times. Whereas, BP 5year PD only jumps 2.2 times on average with the other 8 corporates of the portfolio.

First, let’s remind ourselves how BP 5year unsecured LGD distribution looks like (this distribution can be found in the EUROVISION LGD Contest article).

Now the first good piece of news and the answer to the first challenge : what happens to BP LGD distribution when Chevron defaults ?

  • Algosave FinTech shows that BP median 5-year unsecured conditional LGD (if Chevron was to default) is 8% lower that BP “standalone” LGDs.

Hence, also the answer to the second question : when Chevron defaults, is BP is a low, average or high LGD regime ?

  • Algosave FinTech shows that upon Chevron default, if BP was to also to default, it would – on average – be in a relatively lower LGD regime.
  • the BP LGD “standalone” and “conditional” 5-year unsecured LGD distribution shows exactly that.
  • In red (and on the left hand y-axis) BP “standalone” LGDs, more centered around its mean. In blue (and with the right hand y-axis) BP “conditional” LGDs (if Chevron defaults), with a larger left-hand tail, illustrating the lower LGD regime if BP was to go bankrupt as well.

Algosave can address these complex challenges thanks to the high granularity of its big data universe. Also, current credit model are either calibrated on Equity Capitalization (Option-type models) or on Bond/CDS (reduced form models). Algosave model takes it all : it is both calibrated on Equity as well as on bond spread data (when available). Departing from “thru the cycle” ratings, this gives Algosave FinTech its refreshing “live” and Point in Time flavor.

All the best. Algosave Team – 01/06/2018

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Portfolio management critical question : how severe is the “domino effect” in my credit portfolio ?

Remember Algosave last LinkedIn Article : “Eurovision LGD Contest” ?

We then examined Point-in-Time (PiT) and forward looking LGDs of a sample portfolio of 7 Western oil majors : BP Plc., Exxon Mobil, Royal Dutch Shell, Total SA, Husky Energy, Chevron and ENI Spa.

Algosave unique modelling technology, – surprisingly – revealed that Husky Energy – the lowest credit rating – offered the lowest LGDs. On the other hand, BP plc. was also – and surprisingly – “disappointing”, with the highest and the most scenario-sensitive senior unsecured LGD.

This Algosave exclusive BP dashboard summarizes those findings (right click “Open image in new tab” for a better view) :

By the way, the same Algosave dashboard is available on demand for each of those corporates. Do not hesitate to ask.

Let’s go one step further into portfolio management and look into some core issues of credit dependencies : Domino Effect <=> from single borrower management (single PD) to portfolio management (conditional PD)

Let’s define a “Domino Effect” as being the sensitivity of a given borrower PD to the default of another borrower.

The same data granularity and “real-life” rich universe of possibilities which help ALGOSAVE deliver unique insights into Loss Given Default, are also instrumental in Algosave FinTech delivering powerful insights into issues of credit dependencies for portfolio and risk managers alike.

First, and to make things crispier, let’s enlarge the portfolio to also include Statoil, Repsol and Suncor Energy and ask how severe is the domino effect – a.k.a Conditional Default Probability – in this sample portfolio of 10 oil majors ?

Portfolio = BP Plc., Exxon Mobil, Royal Dutch Shell, Total SA, Husky Energy, Chevron, ENI Spa, Statoil, Repsol and Suncor Energy.

Here is Algosave full insight into issues of credit dependencies produced from its database, with Algosave unique conditional default probabilities and toxicity score (right click “Open image in new tab” for a better view).

Let’s read thru those Algosave insights into portfolio domino effect with a few examples :

For instance, let’s start simple. If Total was to default, how would Statoil 5-year cumulative PD be affected ?

  • Algosave answer : it will jumps from its current 1.3% to 2.3% if FP (Total SA) defaulted.

For instance, knowing that Total SA current 5-year cumulative PD is 2.2%, will this PD jump – on average – to 4%, 6% or 8% if one the other 9 corporates defaults over the next 5 years ?

  • ALGOSAVE answer : on average, Total SA 5-year PD will jump from 2.2% to 5.6% if one of the other 9 oil majors defaults on its debt. And, this average hides a broad range of possibilities, whereby some corporate defaults are more friendly while some are far more “toxic” to Total SA.

If so, which of the other 9 corporates is the “friendliest” and which is the most “toxic” for Total ? and does it have to do with credit rating ?

  • ALGOSAVE answer : the friendliest is ENI. Indeed, if ENI was to default, Total SA 5-year PD will hardly move from its current 2.2% to 2.3%.
  • ALGOSAVE answer : the most toxic is Chevron. Indeed, If Chevron was to default, Total SA 5-year PD will jump from its current 2.2% to 9% (more than 4 times !)
  • And yes, this is somehow related to Credit Rating, but in a “reversed” way. Indeed, since Chevron enjoys one of the highest credit rating of the portfolio, it is logical that if it were to default, it would very likely do so on the grounds of systemic/industry risk. Hence a higher degree of toxicity. Inversely, if ENI – one of the lowest rating of the sample – was to default, it would probably have to do more with idiosyncratic risk. Hence a lower level of toxicity.

Which of those 10 oil majors are the 2 least and the 2 most exposed to domino effect (significant jump in their respective PD upon default of others) over the next 5 Years ?

  • ALGOSAVE answer : the two least exposed to domino effect are ENI and Statoil. On average, and upon default of one of the other 9 corporates, ENI 5-year cumulative PD will move up 1.5 times, while Statoil PD will move up 1.6 times.
  • ALGOSAVE answer : the two most exposed to domino effect are Exxon Mobil and Chevron. On average, and upon default of one of the other 9 corporates, Exxon 5-year cumulative PD will move up 2.4 times, and Chevron will shoot up 2.9 times !

Finally, and assuming I want to increase my exposure to this sample portfolio with a 5-year unsecured Revolver Credit Facility – which is the best and which is the worst candidate from a portfolio management perspective ?

  • In order to answer this question, Algosave assigns to each borrower a toxicity score which corresponds to the borrower level of toxicity for each of its peer.Score = 1, being the least toxic (i.e. causing the least increase in PD) andScore = 9 being the most toxic (i.e. causing the greatest increase in PD)
  • ALGOSAVE answer : the best candidate for an increased exposure is ENI with an average toxicity score of 1.9. The second best is Statoil with an average toxicity score of 3.
  • ALGOSAVE answer : the worst candidate is Chevron with an average toxicity score of 8.3 ! The second worse being Exxon with a average toxicity score of 7.

Now, your turn to put Algosave FinTech to the test with your own challenging questions.

  • Example of challenge for Algosave : when this domino effect happens, what is the probability of being in a low, average or high LGD regime ?
  • Another challenge for Algosave : from a domino effect point-of-view, which is the most and which is the least “toxic” industry for this oil basket ?

All the Best

Algosave Team, 30-May-2018

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From IAS39 historical statistic data to Point in Time Expected Credit Losses : when the world shifted from Google Maps to Waze.

My dear friend Gregory is an experienced and talented Geo-location IT engineer as well as an amazing story-teller. I would like to share with you what he told me about IFRS 9  and CECL regulations because he gave those accounting tsunamis a palatable touch-and-feel description.

First, a bit of history. Some of us recall using Google Maps to find a place ?  It was very efficient. Especially in Tokyo with its complicated house numbers. It almost replaced the policeman stationed a the corner of Omotesando Dori.

But when it came to real-time traffic, no comment.

Indeed, until Google purchased Waze, its traffic algorithm was based on statistics whereby historical records of what happened on Finchley road last year on Early May Bank Holiday will appear on your screen and dictate your journey to Covent Garden. That is, if you leave in NW…something. And since we – human – have a bizarre tendency of doing things differently – which proves that we are not as predictable as monkeys – you and I inevitably hit traffic. All together. And spent our beautiful morning on the roads with the kids getting wild in the backseats.

Until Waze brought free and live traffic data to our smartphones. And the rest is history.

Well; that is exactly what the accounting regulators have in mind with IFRS 9  and ALLL CECL regulations. When provisioning for potential credit losses in their loan and bond portfolio, Gregory told me that the regulators want Financial Institutions to give out a Waze and no longer a old Google Map to its many stakeholders.

And Yes. Those Wazes – not pronouncable – will change direction according to what is happening on the roads – in the economy – real-time – Point In Time. It will make our trips – investments – sometime unexpected – more volatile – especially when its takes us thru remote roads – uncharted territories.

But boy, have you ever noticed how accurate Waze is in timing arrivals. We exactly know how long it will take to go Covent Garden next week the second we board our car. And if something happens on the road we will all be updated – real time. And with driver-less cars and other new disruptive technologies coming on board – literally – Waze will become more efficient and indispensable.

Statistics and historical data are useful but Point In Time and accurate financial information will become more and more relevant and indispensable in rapidly changing, AI / IoT hungry economies. So, kudos to the accounting regulators who – this time – are far ahead of everyone else in many “Waze”.