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AI Looms Over the City: the Kaiju Approach

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I began tapping, it grew louder, then I thumped the lectern, then again (louder). That woke them up. I always enjoy seeing the mixture of shock, awe, gasps, disbelief, puzzlement and smiles as I speak about the New Fund Order: where Technology meets Finance meets Philosophy, meets Science Fiction.

Armed with my walking stick to accentuate points with dramatic gesturing motions, I would tell them that in a New Fund Order, Artificial Intelligence (AI) looms over all of us like a kaiju*, a giant 200 foot Godzilla. Bluntly put, Finance needs to find new solutions through Technology or become obsoleted by it.

*Kaiju (怪獣 kaijū) is a Japanese word that means “strange creature,” often translated as “monster” or “giant monster”

Today the looming threat of AI seems obvious and very real. Why then did Finance so badly underestimate the threat for 20 years? There was certainly a sense of complacent containment. To some extent the City of London had assumed it had controlled Technology after it moved to electronic trading in the 1986 ‘Big Bang’. The de-regulation and computerisation of the City.

Be it London or New York, Tokyo or Geneva the City, in its broadest catholic sense, had after all funded the automation of blue collar roles over 40 years, without fuss, without remorse, without resistance give or take the odd union strike. Indeed the systematic breakdown of worker unions and labour was key to reducing man hours, reduce workforces, to lower operating costs and allow companies to deploy CapEx into machines. There was a sense then that Finance held sway over the purse strings of Technology, the capitalisation of Silicon Valley and the Internet through the 1990s and 2000s. And that was true, then.

However since the Dotcom crash Technology had kept accelerating. A series of market crashes opened the way for Tech Disrupters. Those crashes combined with a collapse in trust in big finance and disintermediation of balance sheets of large pension schemes and banks. To now curb those ‘bankers’, the industry moved from self regulation back to statutory regulation. Investors began to put faith in technology over Finance. Assets also started to flow through index baskets, the influence of Finance was on the wane.
I have seen that change coming, having worked through 3 major market crashes in 1997, 2001 and 2008. Throughout I have worked for a long time as an educator, writing Finance textbooks and working towards better professionalism therein. My industry work now takes me into broader areas such as technology, costs, transparency, Environmental Social Governance (ESG), industry consolidation, product innovation, Investment Governance and supporting the growth of Fintech in my sector. Using my observations I was motivated in 2015 to write ‘New Fund Order’, discussing “digital death” for my profession and the underlying reasons.

It is somewhat fitting to note that, as a born Scotsman and where I am still based today, Scotland was the birthplace of analogue Finance and therefore apt to consider how might Scotland’s Finance Industry adapt to AI. Ground zero.

Finance 2.0 has been coming heavily from the US. Indeed halo US companies like Google (Alphabet), Microsoft, Facebook were not only changing Finance externally but also becoming the largest capitalised stocks within it. Asia Technology joined the party through Samsung, TenCent and Alibaba. This has created a huge feedback and validation of Technology as both a source of economic growth and social advancement on a global scale.

Consequently investors became more familiar with Technology in their own lives then they were becoming more attune to Technology managing their Finances. The desire for faster information, what we call the latency effect saw a rapid shift to individuals away from intermediaries. Now the public could ask information previously privileged to the few and without an information or technology advantage the role of Finance has become exposed. A simple fact utterly underestimated by the industry.

Over time Finance had been built on rules, laws and regulations, these were considered sacrosanct and unassailable; with a generous sprinkling of subjective bias and judgement. The industry had developed technology previously to be subservient and most importantly out of reach of clients. The number of times I heard about an actuarial consultant ‘turning the wheel’ on a long-standing model, all the time charging clients a quantum for the privilege. Frankly this is absurd industry complacency. It simply couldn’t hold up to inevitable transparent scrutiny. The likes of writer Martin Ford and MIT have noted the unrelenting substitution for white collar roles in other sectors, so too is change coming to Finance professions.

“the hurdle machines have to cross to out-perform humans with college degrees isn’t that high.” Martin Ford, Author ‘The Rise of the Robots’.

“The end game scenarios seem kind of severe. From here on in, it’s really, really, really going to change and it’s going to change faster than we can handle.” Matt Beane, MIT.

Yet the acceleration of technology alone could not not have unseated the rocksteady position of Finance so readily; it also took the latent issues within the establishment to bring about its own demise. Our own Monsters!

Why can’t we rely on the human condition alone to deliver effective Finance? Why does it break down or resist automation? Well, we assume only humans are capable of empathy, trust, making good decisions but consider. Alas the human condition is not itself constructive;

Karl Marx wrote “the more the division of labour and application of machinery extend, the more does competition extend among the worker”

While George Orwell wrote “on the whole humans want to be good but not that good and not all the time”
Lastly JP Morgan, forefather of modern Investment Banking said ‘someone does a thing for two reasons, a good reason and the real reason.”

Thus it is no surprise then that Finance has achieved both amazing things but also much to be criticised for. Our industry is a litany of poor practice, high fees, big bonuses, fraud, bubbles, manipulation, market crashes, Ponzi schemes. Unsurprising when the populous loses trust in the appointed class then it opens the door to monsters. Thus technology was no longer benign for its Finance masters.

Meanwhile you need only take a trip to the airport, open a newspaper or get into a taxi to see the marketing power of Finance. Since the 1980s Finance had become big business, deeply human intensive and highly compensated. That introduced strong economic incentives to resist change. When we talk about behaviours, the human condition is susceptible to many, driven by a desire to succeed, to make profit, fear, greed. Since the Great Financial Crisis, Asset Management had grown as capital moved from bank balance sheets and defined benefit pension schemes through to individual retirement accounts and into mutual funds. We called this disintermediation. It moved the capital at risk from employers to workers. Now workers were paying Finance directly, a form of taxation that helped the division of wealth as companies were released to focus on shareholder returns.

In response to the influx of assets, asset managers have increased their portfolio desks, salaries and bonuses swelled, fuelled by an endless supply of graduates, CFAs, MBAs and economic migrants, from investment banking, hedge funds and sell side research. More people lead to higher operating costs, complexities and inefficiencies to manage investor money. The Old Fund Order can be typified as;

• Human Intensive
• Human-Human
• Complex Value Chains
• High Salaries and Fees
• Information Advantage
• Fraud and Ponzi schemes

Where Finance has then become tested in recent years somewhat ironically is justifying its own economic value. So what is Optimum Economic Value (OEV)? In its purest form, we recognise the Finance value chain is itself an alignment between a customer and a financial outcome.Think of it as a piece of rope. How long and how straight is it, is it loose or is there a clear tension (a directness and transparency) between the two points?

Finance is defined by lots of parties involved in the front, middle and back office, it is people intensive. Think of a traditional portfolio of active managed funds sold by a distributor on the advice of a financial adviser, bundled into a retirement product, regulated, consulted, traded, operated, audited. Lots of pound signs. Having largely operated unfettered for 20 years, what exposed these value chains were two-fold;
The immutable growth of computing power known as Moore’s Law, the doubling of computing power at least every 2 years. Secondly Parkinson’s Law, a 1958 paper that observed that organisations became less efficient the more people you hire. We also call Parkinson’s Law ‘Coefficients of Inefficiency’ and historical examples have included; the Roman Empire, Greater London Council, the Civil Service, The Department of Defence, IBM, British car industry and Banks. Likewise Finance was quickly finding itself both outdated, inefficient and expensive.

Despite this, Finance for over a decade tried to box, restrict and otherwise compartmentalise ROBO, portraying it as a dim-witted ‘Robbie the Robot’ from 1956 Forbidden Planet. Big mistake! CitiGroup believed RoboAdvisors will hit $5 trillion AUM in the next decade. A more recent study by Deloitte estimated that “assets under automated management” (including hybrid offerings) in the U.S. will grow to U.S. $7 trillion by the year 2025 from about U.S.$300 billion today. More alarming (if you are a financial adviser) is that consultants A.T. Kearney predicts that assets under “robo-management” will total $2.2 trillion by 2021.
Another view of Fintech is that of the 1933 classic ‘KING KONG’. A loud chest-beater. All noise, but no bananas? Certainly this was the crux of the audience questions. Firstly there is a lot of noise, mostly from consultants and big business but change is happening and at an accelerating rate. Simply note how the make-up of Finsbury Square is changing and innovation is spilling out of Old Street into Threadneedle, into EC2. The very heart of the City.

No longer just noise then, what is being systematically removed is human intensity to be replaced by deep learning and AI. Until the late 1970s hundreds of clerks updated futures prices on chalkboards and recorded them on Polaroid film. Thousands of traders walked the pits, hundreds of thousands accountants, Actuaries, typing pools, administrators and computers processed, calculated, deliberated and predicted.. all gone! The first major electronic platform was Instinet, that could bypass the trading floor. Superseded in the 80s by Bloomberg and Archipelago, which began to replace floor traders. In 2000 there was over 150,000 involved in securities and commodities contracts in New York alone. In 2016 there were less than 100,000 yet the asset market has grown five fold since 2000.

Alternatively take Actuaries: In 2009, 110 students qualified to become Associates of the Faculty or the Institute of Actuaries, 335 qualifying as Fellows of the Faculty or the Institute of Actuaries. In 2016 the Institute and Faculty of Actuaries reported 29,000 members (December 2016), 52% of whom were students, 73% of members were 40 years old or under. With older actuaries retiring; what is the future for the next generation given traditional actuary roles are in demise? Martina King writing for the Actuarial Post.
“In the Insurance sector, reducing the number of highly skilled, highly paid actuaries by replacing them with technology is attractive. It’s a potentially scary prospect for actuarial careers.. there are few open positions for individuals with predictive analytical skills. In other sectors too, organisations are slotting into job ads the request for experience in machine learning. It’s worth the investment in gaining these skills to get ahead.”
We are seeing record numbers of CFAs, MBAs but a reduction in number of roles to fill, as incumbents work longer into life. Any role that is based mostly on rules rather than creative critical thinking are obviously at risk but ultimately all roles are in danger. Regulation key help preserve for a time driven by our desire for human accountability but this will ebb. Roles that will survive longer-term will adapt to work with AI. My own profession is not immune to this threat. The Fund Selection community is quickly waking up to the threat and something we are actively discussing at the Association of Professional Investors (APFI). We are now seeing a new wave of fund analysis, selection tools and digital fund warehousing, which is making fund selection more accessible and transparent. The information advantage is closing. AI will change how mutual funds are analysed and selected in future.

With the closing information gap, the challenges for Fund analysts can then be summarised as growing transparency, the lack of performance persistency from active fund managers, which has a knock-on effect onto Fund Selectors. Also the decomposition of activeness itself, into factors but also luck and risk-taking. Consider the following studies;

• Past Performance Persistency: Mark M. Carhart. Journal of Finance 1997, Blake and Timmermann 2003, SJ Brown 2006, Luckoff 2011, Barclays Capital 2012
• The effect of Marketing and Commission on Broker-sold funds: Del Guercio and Jonathan Reuter, University of Oregon, 2012, 2015
• Herustics and Behavioural Finance: Khaneman and Tversky, 1974, 2007, 2015,
• Active Share: Cremmer, Patijesto, 2010, 2013,

After all if Fund managers cannot add value then what value do the people who select them offer? Other issues include inducements, marketing, survivorship bias and behavioural biases. Obvious symptoms if this shift include the large shift towards index investing and Exchange Traded Funds (ETFs). What we are now moving towards is greater mass objectivity away from individual subjectivity. Performance and quant based analysis is rapidly becoming codified and automated. Meanwhile qualitative Fund analysis, which is itself judgemental based, too is undergoing change. Originating from the Harvard Marketing Mix and later management consulting like Booms & Bitner and Russell. Profile + Process + Portfolio + People + Price… + Pi? Such approaches are coming under scrutiny, as investors get better direct access to fund information. The very value of such analysis is now in doubt. What is now changing is the disruption from crowd research platforms that reduces the information advantage and thus premium of traditional analysts.

So if the value of human Intelligence is left in question, what then for Artificial Intelligence? AI has entered the daily narrative, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions such as “learning” and “problem solving”. Today is hard to fathom just what the limitations are for AI. It certainly offers advantages both in the rapid assimilation of Big data, learning from changes in data and predictive modelling. This takes us back to our friend Robbie the Robot. Consider first that AI algorithms fall into many classes and have already permeated various corners of society;

• Search and optimisation: program synthesis
• Logic and information consciousness
• Probabilistic methods for uncertain reasoning
• Classifiers and statistical learning methods
• Neural: feed forward, recurrent, differentiable
• Control theory
• Languages and translation
• Evaluating progress and self-learning
• Complex Adaptive Systems

For example a simple model was already proposed by Ludwig and Pivioso in their 2005 machine-learning paper. They also considered what sort of Algo should a Fund Selection Robo adopt from 3 choices: Decision-Tree, Neural Network or a Naive Bayes approach. Ludwig and Pivioso concluded that all three approaches outperformed simple scoring models typically employed by human Fund Selectors. What is frightening was that this was achieved with only a simple array of data inputs. Let’s remind ourselves what these approaches do, Ludwig and Pivioso described them as;

“Decision-tree algorithms construct a flowchart-like structure where each node of the tree specifies a test of an attribute, each branch corresponds to an outcome of the test, and each leaf node represents a classification prediction.

Neural networks are represented by set of interconnected units, each unit has multiple inputs and produces a single output. The signals are weighted, transforming the incoming signals, weighted and passed to the output units.

Bayes – The classifier learns the conditional probability of each attribute value from the training data given the classification of each instance. To classify an unknown instance, Bayes’ theorem is applied to compute the probability of a particular class value given the attributes of the new instance.” Now consider the technology advancement and complexity of data available 13 years on since that paper. AI can now begin to replicate judgemental nudges and biases based on common material changes like price, attribution data, manager experience, tenure, benchmark, fund changes, moving firm, news flow and so on. I began to imagine if fund selection can be derived from AI: to screen thousands of funds and make judgements, shortlist recommendations, assess suitability and compatibility against a mandate or investor needs and monitor the outcome of those decisions. This is especially so when we consider key advances in Differentiable Neural Computing (DNC). What DNC does is create digital memory, DNC can literally read and rewrite memory, it becomes iterative. It was used to enable AlphaGo to beat the best Go player in the world across 250 to the power 150 possible moves. Secondly add program synthesis, a program like DeepCoder can literally data-mine and piggy-back other algorithms to solve any problem in seconds and there is around 30,000 GB of new data on the Internet every second for these programs to access.

This takes us the toughest question. Can the human condition still infect AI? It is the question that wrangles the industry today. Can ROBO act in a Fiduciary way, to put the interests of the client ahead of others? This is clearly a concern for regulators not only in terms of the original coding but also subsequent changes as a consequence of self- learning. Such protections could become hardwired, needs monitored and programmers regulated. Firstly AI can follow the Three Laws of Robotics by the science fiction author Isaac Asimov.

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. A robot must obey the orders given to it by human beings except where such orders would conflict with the First Law.
3. A robot must protect its own existence so long as such protection does not conflict with the First or Second Laws.

One of the key problems today in big firms is that Finance staff do not sufficiently understand code and coders do not understand Finance. Regulators often understand neither. This will require tomorrow’s coders to have both technical knowledge of Finance and programming expertise to satisfy a Fiduciary Test. This is where small Fintech start- ups can exploit this void, in the detail between Technology and Finance. Consider that if AI can be programmed to remove human error then what role is left for regulators? It will change. In response the Financial Conduct Authority proposed a new Certified regime (CP17-25) for anyone with responsibility for:

• approving the deployment of a trading algorithm or a material part of one
• approving the deployment of a material amendment to a trading algorithm or a material part of one, or the combination of trading algorithms
• monitoring or deciding whether or not the use or deployment of a trading algorithm is or remains compliant with the firm’s obligations

Ultimately the fiduciary duty falls initially to the programmer of the algorithm that instructs the programme to make decisions. Ultimately a regulated person has to be accountable for the programmer, the program and the outcomes. Taking these laws it is not unfathomable that computers can be programmed to put the interest of the client first and foremost to;

1. Uphold a fiduciary standard and all conflicts of interest must be disclosed. A computer has no conflicts unless they are first programmed. Like a driverless car its’ function is to serve the purpose without question.
2. A fiduciary has a “duty to care” and must continually monitor not only a client’s investments, but also their changing financial situation. A computer can monitor 24/7 continuously and is not restricted by fatigue or the adviser/fund selector’s diary. A sequence can be included if the client does not supply an update within x days or could be linked to the client’s accounts, email, diary and so on.
3. Understand changes to a client’s risk tolerance, perhaps after a painful bear market. Perhaps there was a family change. Under the suitability standard, the financial planning process could begin and end in a single meeting. For fiduciaries, that first client meeting marks only the beginning of the legal obligation. We have seen the term ‘orphan clients’, and humans have a great track record of dropping less profitable clients (value pools).
4. Monitor, adapt, assess fund changes. The reality is that many fund investors do not monitor their decisions often enough or with objectivity. They are susceptible to heuristic biases. Yet a computer can continuously monitor cost, turnover, risk, changes and performance. It can monitor twitter feeds, performance, fund manager commentary, portfolio positions, information supplied by the client, instructions, deal flow, thousands if not millions of data points analysed through neural networks.

Adding in Asimov rules into the AI subroutines become the safety net to ensure the program operates efficiently and investor aims are managed. AI can even offer the Robo Fund Selector a framework to set ESG criteria and identify better solutions to improve ethical and sustainable investing. It can employ new metrics to help investors understand their impact on Green House Gas emission, the economy and environment.
According to the New Fund Order, Finance structures will continue to change over the next two decades and beyond, an unrelenting digitalisation of the value chain. As mutual funds in turn become managed by AI then so too will humans become more challenged to understand, select and manage them. They need AI to solve the equation. How then to survive the kaiju, how to survive digital death? Be prepared, my lecture on AI Robo Fund Selection is available at FintechCircle Institute.

I finished by saying ‘if this sounds all too monstrous then you’re probably right. Godzilla approaches.’ Cue nervous clap, more stunned disbelief and many interesting questions. A day later I was contacted by one of the students who had attended to say she would change the focus of her dissertation. Hope then.

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