Entries by Alan Patrick

Diagram - ongoing down and upspikes in perception of the topic of Fraud for various banks

No sooner do we do some research on the problems of retail banks managing perception (using our own DataSwarm analytics suite, natch) and find that the Challenger Banks/Fintechs will not be immune, than the proof comes down thick and fast on one Challenger's head (Revolut).

In a few short days they have suffered bad publicity over parts of their system's issues re money laundering tracking, their uber-culture, and their App not working.

Our research had shown that overall, the "Challenger banks" like Revolut had better customer perception overall, for a number of reasons (seehere for the relevant blog post). At that point it was only the Traditional retail banks that were being hit by negative stories, due to ongoing complex operations and old bad news continually resurfacing - a factor we call "perception drag". You can see this on the diagram at the top of the page, its those big downspikes. However, we also wrote (to quote the report):

These types of issues do not yet impact the Challenger banks, but we would suggest that this is currently because they haven’t been around long enough. It is yet to be proven that they will not fall into these sorts of policy errors and PR gaffes over time.

No sooner had we finished the report and turned the trackers off than Revolut fell into one mess a few weeks ago, and now all this.

Fortunately, not all is bad news. If you look back up the diagram, there is an interesting effect if a bank chooses to try and adress these issues - again from the report, look at two of the spikes:

• October 27th – Lloyds faces probe alongside KPMG over HBOS possible fraud a decade ago

• November 14th - Lloyds shortlisted for an award for anti fraud detection system

Being seen to try and fix he things that go wrong is a good antidote to the downspikes. What we also know' from our work experience with cients is that not only the devil, but salvation, is in the details - the report again:

Digging down to the next levels [in the perception data] reveals which specific factors are causing positive and negative impressions, when, and why, for each issue. At that point it becomes possible to attempt to optimise perception from an informed base.

Our experience is that the impact of perception on sales and service can change market structure over time, but is not well understood, especially the effect of service on future sales. It is an opportunity for most companies to improve on significantly.

And one last crumb of comfort - in matters of publicity, to quote Wilde, "the only thing worse than being talked about is not being talked about...."

The DataSwarm report "Retail Banking in the Fintech Age" is available here

An ode to the digital comminications technologies of our times, and the wannabe influencers who infest them:

Go with rage into the noise and haste, and remember there is no value in silence. As far as possible never surrender the mike, get your opinions out to all other persons.

Speak your truth loud and often, to all the others, they are mostly dull and ignorant and they need to know they are wrong.

Override quiet and thoughtful people, they are impediments. Compare yourself with others frequently, ignore those lesser than you as they are a waste of effort, and suck up to those still better than yourself.

Trumpet your achievements, and broadcast your plans. Focus on your own career, don’t be humble, this is your way to make your fortune.

Exercise duplicity in all your business affairs, there is one born every minute. Do not be blind to virtue, idealism or quiet heroism, there is always a way to use those suckers.

Be You, but feign whatever you must to get what you want. Especially feign affection, the losers always fall for it as they are continually seeking love, and anyway they are used to falling on their arse.

Resist the counsel of the years and try and look as young as you can. Nurture a feeling of self-worth to get you over misfortunes, and ignore any dark imaginings, fear is for wimps.

Have a good workout discipline, and always forgive yourself anything. You are a master of the universe, much more than the trees and the stars, it needs you to be here.

Labour and aspire to create noise and bustle in life, peace is for the meek souls. It’s a beautiful world despite all the drudges and dreamers, so don’t worry about anyone else, be happy.

Note to self. Take 2 frog pills, eschew social media, and see if someone will sell you one of those devices that splats mobile phones on trains....

(With grovelling apologies to Desiderata)

To say that blockchain is overhyped is to put it mildly - not a day goes by without some pundit proclaiming that industry X will be transformed by it. Most of this is speculation, based - as far as I can see - on little understanding of the technology or the viable alternatives. For most proposals, somewhere between the hype and heuristics is a major disconnect.

This post is not going to go into the ins and outs of blockchains, but will just point out the basics of what defines whether it will transform Industry X (this post summarises a more detailed article I wrote last year - go there for the more tech stuff - in our preparation for the blockchain work last year for helping to run the London part of Global Legal Hackathon.

In short, its all about ROI on the transaction costs..... in essence blockchain technology (as designed today) is great if you want transactions that are:

secure (ish - the overall flowchain of a blockhain system has shown itself to be prone to all the standard hacking problems),
resilient,
no trusted 3rd party (TTP).

But, owing to the high calculation load to be secure/resilient/not require a TTP, for blockchain to work it also needs transactions to be:

relatively low volume (c 6 a second for Bitcoin for eg)
not particularly time sensitive (hours, or even days to complete the agreement between nodes)
relatively high transaction value - as these calculation loads massively increase the blockchain transaction cost.

This is captured in the 2 x 2 matrix above - there is only one "sweet spot" square for blockchain, that where the value and rate of transaction flow makes blockchain a useful solution. So for applications that are:

High volume, Low value - blockchain is a lousy solution in every single way unless the need for the secure/resilient/TTP overrides everything.

High Volume, High Value - the value means blockchain transactions cost in, but the high volume and resulting time lags will probably break the system or render it useless for most cases

Low volume, Low value - can cope with the volume, but unless the need for the secure/resilient/TTP overrides every consideration, or one is prepared to fund the transaction in another way (VC stump money, subsidies, indirect gains) there are far better approaches around today.

Low Volume, High Value - this is the sweet spot - transaction values make it economic to use, volumes don't break it.

Worryingly, for a lot of mooted applications there is not a hope in hell that Blockchains (in their current forms), will be fast enough or cheap enough to work. To be fair, there are now more and more blockchain approaches entering the market, but there is always a trade-off of speed and/or secure processing. Here is what is more likely to happen in most cases:

1. For most non "sweet spot" blockchain applications there are existing, cheaper, faster “Good Enoughs” today. There will nearly always be an alternative case to employ more scalable/lower cost/faster performance blockchain system designs with:

Less distributed architecture to scale it for speed and cost
Less complex security in the blockchain to reduce the processing load time and cost

2. This will probably be done by implementing them as “private and much less distributed” systems behind IT datacentre security walls, ie “Walled Garden” blockchains will very probably put themselves in the position of the TTP supplier. (This is what is in fact happening today, I first saw this predicted in 2015 by Dinis Garda looking at IoT outcomes).

Which of course negates a lot of the theoretical benefit of the Blockchain idea itself.....

For the last day or so Revolut Bank (or more accurately, non-bank) has been in trouble with data privacy advocates, singletons, vegans, users and now possible even the regulators

For the new "Challenger banks" this is a very bad idea.

We have been researching perceptions of Challenger Banks vs Traditional Banks from October 2018 to January 2019, but in a nutshell, perception matters for capturing and retaining customers, especially in an age when 90%+ of people go online for recommendations.It also matters more for the Challenger banks, who don't have large service portfolios - so they need to be seen to be really, really excellent at what they do. Implying they spy on their users to the extent the regulator is interested (never mind the rest of the fallout) is a bad idea.

Revolut have not done as well in the perception stakes as their main competotor, Monzo - another Challenger bank - as the chart above shows (the faster the rise in the line, the better), their perception is lagging and is not much better than the Traditional banks, with all their history dogging their steps. This latest episode is not going to help (and may well point to deeper issues)....

More analysis on the DataSwarm site over here

This is a summary of a more detailed post over here on our DataSwarm company site:

We have been tracking Brexit (the British Exit of the EU) for about a year now using our DataSwarm systems, as a side project to test our system’s predictive analytical capabilities (we predicted the Brexit referendum correctly all those years ago, as well as quite a few other “surprise result” elections since – see here for more details).

One of the more interesting features of Brexit has been the fate over the last 9 months or so of the one of the most high profile and flamboyant politicians in the Pro Brexit camp (the "Brexiteers"), one Alexander Boris de Pfeffel Johnson, better known as Boris Johnson, or just “Boris”.

To recap for our non-UK readers, Boris was one of the main personalities in the Leave Europe campaign leading up to the Brexit referendum vote, but - after David Cameron (the Prime Minister at the time) resigned the morning after the unexpected result to Leave came in. Boris exited the ensuing tussle for Prime Minister, and Theresa May won the internal Tory Party election and then scraped back in as Prime Minister after (just) winning a General Election.

At any rate, post these elections Boris again threw himself into the task of championing the Leave cause, and from the get-go captured a very large amount of the UK mindshare around the Brexit arena. Chart 1 shows his prominence in March this year.

Chart 1 – Zeitgeist Chart for Brexit, March 2018



But a week is a long time in politics, and 9 months is an eternity, and when we look at the same chart of the memetic heavens now, his blob is not nearly as prominent.

Chart 2 – Zeitgeist Chart for Brexit, December 2018



So what has happened? An examination of the opinion and idea sub-streams underlying Brexit overall over the intervening months points to 3 main causes:

The actual process of the negotiation has risen in the overall mindshare as March 29th 2019 approaches
Others have actively started to make more running as That Day draws near
The “Boris Camp” has been changing tack over recent months, and going for Gravitas. The shift has been to adopt a more serious, thoughtful, dare one say Leader-like approach

So, watch this space. Or better still, come back and watch us watching this space.

A short note on what the DataSwarm system does

The DataSwarm Analytic Engine tracks opinions and ideas from social and other media. It registers their rise and fall in the mindshare of groups of people (this change over at any time what we call the Zeitgeist), whether of large groups like the British population, or smaller subgroups (for example cosmetics users, whisky drinkers, online TV consumers, bitcoin buyers and various other client projects). Just to make it more interesting (i.e. complicated), large groups comprise of smaller groups each with varyingly different idea and opinions sets. We also track how these varying idea sets combine, split, recombine and generate new Zeitgeists in different subgroups. It’s fairly complex analytics, but very powerful – hence our ability to predict elections that surprise all the pollsters, pundits and politicians. See here for more:

Prediction is always difficult, said Yogi Berra, especially about the future. However, it's something we do quite a bit of (have you seen our work on election prediction - Brexit, Trump, UK 2017, Germany etc - we got them all right) so I went along to the NESTA Futurescoping 101 event on this last night see what they had that I could steal to offer.

There were 4 talks on various ways of predicting the future, or more accurately 4 ways of making scenarios (reading entrails of dead animals is still outre, but with the return to the Age of Unreason we are seeing I wouln't be surprised it it makes a comeback, albeit maybe with dead plants). Anyway, in summary they covered these 4 approaches:

Speculative Design - Cat Drew

Speculative Design is primarily the use of physical things to provoke debate, rather than using documents/words/numbers etc. For example Cat did one session on blockchain visualisation using a blood transfusion drone made of felt and other blue petery stuff to stimulate thoughts of what (electronic vampire?) blood donation services may be like.

Whether you agree with the approach or not (In my experience with Tech you do often do have to build a Future Thing for people to be able to think concretely about, and this looks useful for that - but it's risky to abstract the results too far without doing the hard sums....) she also noted some things that are important to scenario building no matter what your approach, viz:

- multidisciplinary teams
- can't be fantasy, don't make it too remote
- base it on evidence of research
- Suspend belief but no trickery

Games and Simulation - Florence Engasser

Simulation and game design are more my comfort zone area, so to an extent I knew more abot this approach than the others - anyway, it was all sensible stuff and Florence noted the main problems one hits with respect to modelling complex and unpredictable things (aka most future problems), and the benefits. As she notes, simulation helps in a number of ways:

- Model the trade-offs and outcomes (and some aspects often become clear in the dynamics of the model)
- Visualisation makes it easy to see things
- Collaboration - seeing things the same way (modelling often is a good way to bring people up to speed with a situation)
- Experimentation - safe place to experiment with changing factors and assumptions

Her summary of how to make it work was good, I liked the checklist of what a simulation or game needs, not so much the first 3 which are almost a given, but the last 3 which are often neglected by techie types, and are part of the "soft factors" that are so essential:

- objectives (getting these right is non trivial in my experience)
- mechanics (wish she'd said more on what they do here)
- balance between complex and simple (again critical for model acceptance)
- narrative for the scenarios
- choices people can make
- a journey, putting it all together

What I didn't know is there are a bunch of Nesta games in existence or production:
- Innovate
- Superbug
- Consortium


Inclusive Place based futures - Harry Armstrong

This was about how to have inclusive conversations with people about changes to their cities, buildings etc. Typically top doesn solution go awry, it's better to get the locals involved for a variety of reasons and avert failure:

- Appeal to democratic/ethical notions to get buy in
- To drive collective action needs a shared vision
- Driving nclusion of a large number can show wider benefits to make the project more attractive, and show flaws and avert failure that a small group won't see

Examples used were the Newcastle 2065 future city strategy and the Island of Aruba's 2000+ strategy for itself. Looking at the latter:

- Aruba in the early noughties took at collective approach for its future strategy and engaged 60% of the island's c 100 000 population,
- They used the Appreciative Inquiry approach (Discover, Dream,Design, Deliver )
- Then went on with scenario planning approach, across 10 workstreams

What interested me about this example is that Aruba had a change of Govt during the process and teh new people wanted to change or cancel some of the streams, but concerted citizen action forced their continuation - now that is buy in

Lessons about this approach are salutary:

- works if it's authentic,
- don't do it if you won't act, expectation raising can be risky
- needs buy in from major players
- need neutral spaces for trust
- need to manage "the usual suspects" being involved

DiY Scenarios - David Finnigan

The approach here is to acting out a scenario as a short play or event. His view is that a scenario is an Imagined vision - doesn't have to be correct, but you need a range including a best and worst. So far same old same old, but his approach is to use the play as a vehicle to slip in the assumptions and outcomes as just part of teh narrative.

This evening he acted out making a nature documentary in the future, but members of the audience had to answer assumptions on the role of nature reserves in the future, impact of tourism, and the various other assumptions about the future, using the play-acting as a vehicle to sideways slide in the assumptions of the real scenario, ie the setup conditions as part of the building the story, and the edenouemaent is anarrative end of teh reults of the various 2x2 essumption matrices.

Overall impression - the evening seemed a bit heavy on the qualitative and light on the quantitative, my experience is at some point someone with the power of decision making says "show me the numbers behind the grand vision" (which is where simulation type modelling has an advantage), so while I could see how some of the more narrative approaches could really help the envisioning.

I think - as Cat noted in the first talk - always base your scenarios on evidence of research.

So, Twitter has expanded the 140 character limit to 280 for all users. I've used it for a week or so like this, and overall what I've found is:

- It's useful as you don't have to artificially concatenate twts, and you can be clearer in what you mean
- If you stick to around 140 characters and stick to the point, it flows as well as the original.

But...
- Some users (too many) put no new, useful information in the extra 50%, the signal-to-noise ratio is effectively halved
- A pageful of 280 character twts is half as much information as one of 140 character twts, and given too many are full of woffle it can be even worse.
- A 280 character tweet needs some formatting functions to be easy to read.

Now no doubt Twitter could put in basic editing functions, but this seems to be adding complexity to a system where the major attraction is speed and simplicity.

IMO Twitter is for fast-to-read bullet point information, not micro-essays, and especially not micro-essays by the verbose.

Lest we never forget, not all unknown soldiers are dead

(Rogers cartoon, Pittsburgh Post-Gazette)

Covered in boingboing and LabSix- what happens when people start to spoof image processing algorithms..Can you make a cat look like guacamole?

Three researchers from Kyushu University have published a paper describing a means of reliably fooling AI-based image classifiers with a single well-placed pixel.

It's part of a wider field of "adversarial perturbation" to disrupt machine-learning models; it's a field that started with some modest achievements, but has been gaining ground ever since.

But the Kyushu paper goes further than any of the research I've seen so far. The researchers use 1, 3 or 5 well-placed pixels to fool a majority of machine-classification of images, without having any access to the training data used to produce the model (a "black box" attack).

Must admit I'm intrigued and a bit heartened, there may be a way out of the continual CCTV snooping world that is emerging after all. Research paper is over here .

Latest news on the Russians on Facebook brouhaha from Recode

At Facebook, roughly 126 million [or about half the voting population] users in the United States may have seen posts, stories or other content created by Russian government-backed trolls around Election Day, according to a source familiar with the company’s forthcoming testimony to Congress....

Google, which previously had not commented on its internal investigation, will break its silence: In a forthcoming blog post, the search giant confirmed that it discovered about $4,700 worth of search-and-display ads with dubious Russian ties. It also reported 18 YouTube channels associated with the Kremlin’s disinformation efforts, as well as a number of Gmail addresses that “were used to open accounts on other platforms.”

And Twitter will tell Congress that it found more than 27,000 accounts tied to a known Russian-sponsored organization called the Internet Research Agency:

There is a bit more from NBC:

Facebook says in the testimony that while some 29 million Americans directly received material from 80,000 posts by 120 fake Russian-backed pages in their own news feeds, those posts were “shared, liked and followed by people on Facebook, and, as a result, three times more people may have been exposed to a story that originated from the Russian operation

.
Beforehand, Facebook had said they had found about $100,000 of Russian spend on the platform over the election.

To put these numbers into context, the Clinton campaign spent about $1.4 billion on media during this period, and that is without the free publicity from nearly every US media organ being mainly on the Democrat side. The Russian media pieces would have been swamped by this spend and volume, never mind all the rest of the pieces of media on the average social media timeline. The Trump campaign spent c $1bn itself, not on Russians.

A BBC article says Facebook estimates that:

"just one in 23,000 or so messages shared on the network were from the Russians".

That's a ratio of 23,000/1 (about 0.004%), and if you compare the ratio between the Russian spend of c $100k vs the c $2.5 bn spent by Trump & Clinton (25,000/1) then they are no more effective in getting their message transmitted than any other piece of media on average. If the Russian spend is underestimated then they are in fact quite poor at writing attractive content, unless of course the volume is also underestimated in which case it approaches this average again. .

The election was won on c 55,000 voters voting for Trump in marginal wards, of a total of c 130m voters. about 0.04% of all voters. So for the Russians to have "stolen the election" with 0.004% of all Ads, their Ads must be 10x as persuasive as all others just to be have an equal chance. Or they have to be extremely targeted. But from the above, it seems the Russians reached c 50% of the potential voting population or about 130m people , so doing the paper-napkin maths at the most optimistic (The 130m messages seen went only to the "right 50% of the 130m who actually voted) so 23,000/1 is now 11,500/1, about 0.02%, so they are 2x more targeted and thus have to be only 5x as persuasive.

So in order to believe the Russians "stole the election" you either have to believe that either:

(i) They are extraordinarily persuasive copywriters and great targeters to boot, and can do wonders on a shoestring, beating the best US Ad agencies that Democrat money could buy (they bought the best) or

(ii) Facebook (and the others) are massively underestimating both their spend and % of all messaging, but it has to be at least 10x more just to get parity on reaching those 0.04% of marginal voters who turned it for Trump, never mind persuading them

Or alternatively, you have to believe they were a pretty marginal force in the election (albeit with intent).

Incidentally, we used our data analytics technology to predict Trump's win (and a few other recent elections too) correctly, and we did it by analysing social media support for Trump vs Clinton. It was highly predictive, forecasting a neck and neck race, and we edged it to Trump based on small signals. We're pretty confident our system is fairly good at negating the obvious bots (which are the ones that operate in volume) but there's no doubt we saw some Russian Fake News in the results. However our main impression is by and large people knew which candidate they wanted (or quite frequently, which one they most certainly did not want) and shared media that agreed with them in their own filter bubbles.