Was it a pure coincidence that my new book Artificial Intelligence for Audit came on the same day when PwC’s chairman committed to change things?

Was it a pure coincidence that yesterday (August 25th, 2020) when my new book Artificial Intelligence for Audit, Forensic Accounting, and Valuation: A Strategic Perspective (Wiley) hit the market, PwC’s Bob Moritz committed to aggressively review and improve the quality of audits?*

The answer to the question is “yes” and I acknowledge it was a cheap tactic to get you to open the link – but if you have even slightest interest in revolutionizing and reinventing audit, just keep reading.

Why a new book on Artificial Intelligence in Audit?

For more than two decades now, I have been on both ends of audit – and neither gave me a sense of assurance and confidence. As an auditor, I felt pressured to complete things faster and with less resources. When being audited, I felt I was just a “check-the-box” item.

There was no objective reality There was no solid framework. There was no business value-added. If the universe of business problems is like a polluted ocean, legacy audit would be like a lonely castaway survivor dipping net in the water to catch fish – while simultaneously hoping to solve the pollution problem. In other words, too little, too lame, too limited, and too lost.

I felt that when it came to audit, we were simply playing roulette. The difference was that the casino and the gamblers, all seemed to be in a strange state of cohesion – a state often observed in softball, overly friendly audits.

Something needed to change even before the current crisis emerged (and this crisis is not Covid19).

The sudden and powerful rise of artificial intelligence became a gamechanger for business – a change for which audit is least prepared:

  1. It introduces new types of emergent risks for which audit firms are not ready.
  2. It forces audit firms to rethink their service delivery systems.
  3. It requires audit firms to stay a step ahead of the emergent risk vs. always playing the catchup

So, don’t be surprised to discover that Wirecard, the firm whose demise made Bob Moritz issue the call for change, offered a comprehensive service model for fraud detection. The ironies of our time!

If the existing pace of innovation in audit is a gauge for what lies ahead, we are in trouble. A new approach and a new book were necessary. As an Applied AI technologist, I took the lead to envision an audit automation model that can deliver constant, intelligent, and automated audit.

It is all about the emergent risk

AI has infused new risk in all aspects of business. Automation is the right answer to most business problems – and businesses have recognized that non-intelligent information technology can no longer provide a sustainable competitive advantage. Call it whatever – cognitive, AI, Fourth Industrial revolution, or intelligent automation – AI has now become a central part of our business.

The emergent risk comes from many sources – including from a company’s operations – where rapid automation is enabling a dynamic universe of interactive agents working in an adaptive and evolutionary configuration.

Decision-making by intelligent agents (human-machine or machine alone) requires constant for risk and bias.

New Audit Business Models with AI

I explain in my book that the audit will experience a surge in new business models, including:

Technology Provider: Audit firm will have the opportunity to develop, design, and install AI agents to help clients achieve better internal control and improve greater visibility to increase the understanding of inherent risks. These systems will be done for the internal use of the client firm and not for the external use. PwC’s cash.ai project is an example of that.

Audit as a Service: Audit firms can now deploy AI agents that perform continuous audit and track new developments in client’s business. These agents will be visible to the audit firm.

Audit the Auditor: Audit firms will acquire much better understanding of their audit teams and match the right people with clients.

Audit the Agents: As agents become more pervasive, governance of audit agents will become an important issue. Audit firms will need the ability to audit the agents used by the clients.

Assess the maturity: Audit firms will be able to help clients determine their maturity in terms of being able to audit their intelligent and non-intelligent digital workforce.

Niche Players: We will observe a sharp rise of audit-tech firms focused on niche areas of audit and these firms will sell their services to other audit firms.

Innovation is Key to Making Progress in Audit

We find ourselves in an extremely complicated position. Audit was not ready to face the world in the pre-AI-rise period – and now it seems even more incapacitated.

If audit needs to be relevant, this perpetual catchup of audit must end. Audit must grow up and face the reality. In my book I describe that as:

Audit has always been in a reactive mode and not a proactive mode. Scandal after scandal, and failure after failure, audit has functioned as a follower and never a leader of best practices. Being proactive means that audit must stay ahead of the emergent risk.  

With great concern, I highlight something far more important: It is not enough to keep the pace, the pace of innovation in audit must exceed than that of the entities being audited.

The model presented is comprehensive

In my book I offer a model to show how the entire audit process should be reviewed and automated – with adaptive design elements in mind. It will require a blend of various technologies including machine learning (basic), deep learning, RPA, process mining, and other technologies.

It will also require focusing on the entire process. Hence, I try to synthesize various areas of innovation in my book to assemble a comprehensive model of intelligent automation (Figure 1).

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Figure 1

This model has five areas for audit, including:

  • Automated preaudit
  • Automated risk assessment
  • Automated Audit procedures
  • Automated Audit reporting
  • Automated post-audit management

AIAI’s model

This above model is developed and used by the American Institute of Artificial Intelligence (AIAI). AIAI is not approaching governance and audit problems as separate problems. And both problems have two dimensions to them:

  • Using AI to perform audits
  • Auditing AI system

The coexistence of these two dimensions leads us to keep our focus on the integration of the following areas:

  • Audit standards for AI
  • Audit automation
  • ESG (Sustainability – Environment, Social, and Governance)

If we want to avoid a repeat of the Great Recession, or a total meltdown of the human civilization (which is quite possible as we have seen with Covid19), these three areas must develop simultaneously. Yes, it is about audit efficiency and quality – but more importantly it is about being responsible. Bob Moritz is right: a change is necessary – and AI will make it happen.

You can order my book on Amazon or Google. Also read my other article on ESG.

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* See Financial Times article PwC pledges to review fraud detection after Wirecard scandal shakes industry by Tabby Kinder on August 25th, 2020

How to put “S” in ESG without being pretentious? A guide for investment managers: Artificial Intelligence does it for you

The ESG movement has a strong emphasis on environment. ESG stands for Environment, Social, and (corporate) Governance.

This whole area is so new that when CNBC interviewed Jonathan Bailey, Head of ESG Investment at Neuberger Berman, the CNBC analyst commented that Bailey’s position would not have existed a year ago. (interview aired on 08/24/2020)

Clearly, getting the E part is easier. It has 40 years of advocacy by people like Al Gore, well-defined global standards, and tradable products such as Carbon Credits.

But what about S in ESG? In an era marked with Black Lives Matter protests, rising nationalism across the world, increasing global tensions, and higher awareness about these issues – how to bring S into ESG without feeling the guilt of insincerity?

“Guilt of insincerity” … what’s that?

Claiming to start an ESG focused fund is easy. Defining the standards in accordance with which the fund will operate is much harder. What makes it extremely difficult is that you must deliver reasonable returns to the shareholders.

To posit that great returns will only come from companies with the highest ESG performance is still unproven. To claim that firms with higher standards deliver great value, is also empirically unverified.

But what is provable is that if a strong movement exists in the investment world – a movement that can help transition investment from assets that exhibit low ethical standards to those that show strong ESG traits, the needle will move. The obvious question is: what are the standards for “S” – social concerns?

Without having such standards – fund managers are trapped in a debate that precedes Al Gore’s save the planet campaign by a few thousand years. The question of what is good for society is as old as the human civilization.

If ESG fund managers pay lip service to social concerns, they are being insincere to the cause. If they don’t do anything – then it is not much of ESG. If they create goals that are too tight, it may reduce the breathing room for delivering returns. This is the dilemma and a source of guilt.

Without more elaborate frameworks, asset managers tend to lean upon some obvious areas such as diversity, human rights, consumer protection, and animal welfare.

The United Nations provides further guidance on the sustainability development agenda by establishing 17 goals. But these are cookie cutter approaches. A fund needs a competitive advantage.

While these standards are clear, the following three problems become a source of concern for investment managers:

The problem of variable inclusion: What constitutes as social concern? Which social value driver variables to include and which to exclude? For example, would running clinical tests on animals be considered as a concern about animal welfare? Would running clinical trials in Africa – where populations may not really understand what they are getting into – constitute as human rights violations? Would capturing personal information of users be considered a human rights issue?

The problem of measurement: Once variables are defined – questions arise about how to measure the social impact? Would the measurement be against an absolute standard or would there be some flexibility?

The problem of definition: This is close to the first problem – but it captures a different perspective. While the first addresses which variables to include, this problem addresses that once a decision is made to include a variable, how do you define that variable. For example, if you have selected weapons manufacturing as a (negative) social value driver variable, does all weapons manufacturing violate human rights or only selling weapons to countries that violate human rights constitutes as bad? It is questions as that which help us define the variables.

The above three considerations impact the real problem faced by fund managers: delivering returns above the expected cost of capital.

This means that answers to the above should somehow be linked with returns – and that means answering various strategy and goals of fund questions. For example: Is the fund operating with the goal of behavior modification for a target firm – or is the goal to reprimand offenders? Does the firm establish internal goals or abide by external goals? And most fundamentally: how does the ESG-enabled strategy translate into a competitive advantage?

The answer to the above question lies with artificial intelligence.

How can AI help?

Artificial Intelligence provides the most comprehensive way to implement “S” and “G”. In this article I will only address S – as G will be discussed in a separate article.

I believe that every firm needs its own internal standards and the ability to analyze target investments. This gives maximum strategic flexibility and can help firms establish standards that can be unique.

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Figure 1

A system for achieving that is based upon 5 Social Value Discovery drivers – which are followed by a CRISP-DM type model development and deployment process (Figure 1).

The first four steps are what helps define the overall social value creation model.

The fifth step – Return Linking – establishes a hot link between social value drivers and returns to enable dynamic fund management. The word dynamic refers to having the ability to evaluate the link between social value creation and returns. This strategy can work for both passive and active investment styles.

The outcome of the first five steps establishes the data requirements and scopes out the preprocessing requirements – keeping in mind that your data and algorithms can themselves be a source of bias. 

You must avoid these mistakes

To secure solid returns and keep your “social” a strong contributor of value – you must avoid the following five mistakes:

Do not go with a cookie cutter approach. Your investors will be able to smell the insincerity associated with the cookie cutter approach. Make social value creation a competitive advantage for your firm. This means you must have a framework.

Do not leave the 5th Step as a loose end. Make sure to link returns with your social value drivers – and do so at a lower level in fundamentals-based value creation. Do not use indefensible models of social value creation. Make sure to have clear and defensible framework which clearly defines what value creation means.

Do not proceed without properly understanding the data and algorithms: Your data and algorithms can be a source of bias. You must have a clear strategy on how to deal with bias.

Make sure to test your strategies: Make sure that you have a way for testing your strategies. And this opens a new can of worms. Your model must work in active and live settings – and making it work is not easy. Good investment strategies often come to die on the altars of overfitting.

Do not ignore that your target investments can use AI for good and bad: One of your critical evaluations need to be whether your target investments (firms) are using AI to create or destroy social value. These days AI is the top agenda item for most firms – and they can use it for good or for bad – and your knowledge of that can make all the difference.

Next Steps

Based upon the above, here are the five steps to put a solid S in your ESG:

1.   Understand your strategy; identify your social value drivers, measurements, and definitions.

2.   Establish a hot link with returns. Study what that means. Test, test, and test. Do not use a cookie cutter approach.

3.   Data is expensive. Do not just get all the data. Get data that is meaningful for your framework. Establish best practices for data management and preprocessing.

4.   Establish models – and you would need multiple – to work together to identify and manage value creation for you. This means to get a synchronized value-creation framework implemented.

5.   Things are never constant. Know and proactively manage when change happens. It can happen when the underlying distributions have changed – or the set of features you used to define the social context have been altered.

You mean well. But this is investment business and having goal excellence is one thing – making it work another. Social value creation is important and while we cannot change everything – as Dylan Thomas said, we must “rage against the dying of the light”.

Professor Al Naqvi

Worse than Covid19 – the Real Tragedy of our Time

After at least four decades of unrelenting emphasis on preparing leaders – we now have the results: a big “F” for a colossal failure. From seminars to conferences, and leadership books to graduate programs, all that we have accomplished, it seems, is to install gutless, morally deficient, and fake leaders at the top.

When the greatest test of a coordinated global leadership arrived, perhaps the only one in the past eighty or so years, the so-called leadership of the entire world collapsed and crumbled. And unlike the World War 2 or the Great Depression, this challenge did not require any complex maneuvering. All it needed were few coordinated steps, and it would have been fine. But, alas!

Remember all the case studies and stories fed to us about leadership? The formula to become great, to lead change, to manage crisis, to learn rules of leadership, to become highly effective, to be a servant leader, blah, blah, blah. Remember the claims of exceptionalism, of protecting human rights, of safeguarding human dignity, of saving the planet, of integrity? As we discovered from our recent crisis – that is all they were: case studies, myths, and stories. When it came down to the real thing, we saw a total meltdown of leadership at all higher levels.

This is not a criticism of any one leader, or one country, or a business, or a political party, or a parliament, or a congress. This is a factual assessment of the upper echelons of the entire humanity.

The tragedy of our time is not that we suffered from this pandemic – but just how ugly, how incompetent, how cruel, how selfish, our conduct has been as a civilization.

Just as the first signs of Covid19 appeared, we observe a complete state of denial. Except for perhaps a couple of countries, the rest of the world could not put two simple facts together: it is transmitting human-to-human and it has no cure.

One can agonize over just how reckless we all must be to miss that – but it is not that we missed it. The real issue, in my opinion, was that we lacked empathy to see “their” problem as “our” problem. Look at the news stories that came out during the early Covid19 period (and I have analyzed them). We saw Covid19 through the social and political lens. What should have been a story of science and biology and humanity somehow got blended with factors such as the Chinese Communist Party, President Xi, the Chinese culture, the Chinese people and so on. Remember our fascination with the Chinese constructing a 1000-bed hospital in 10 days? While we marveled their engineering, agility, and capabilities – we could not see the underlying reason why it was being done.

We felt safe and detached, as if Wuhan was situated on some distant planet. We saw it melting away, but we were unable to channel our thought process through the lens of humanity. Our prejudices – I believe – held us back to see what it really was: an emerging crisis for the entire world. Somehow, it was “their” and not “our” problem.

Perhaps our collective consciousness has become so trapped in memes, tweets, and 2-minute videos that we have lost our ability to make simple observations. Perhaps we have become such a prisoner of our digitized, social media governed world, dominated by behavior manipulation business models, that we have lost our ability to think clearly and respond to facts. With 30-second clips designed to extract reaction, sensationalized content, screaming influencers, and “likes-and-retweets” mindset – we seem to be more concerned about our content becoming viral vs. a real virus going viral.

As the disease spread from China to other countries and the human tragedy unfolded in Iran, the stories became about the Iranian politics and government, sanctions, and the nuclear treaty. When the disease entered Italy and Spain – while a bit more relatable – it still seemed like a distant reality. The narrative of a ruthless virus going after the older Europeans, who smoked a lot, and do not have an advanced healthcare system kept the problem afar – our prejudices rationalized things for us.

As the pandemic spread in the United States, we observed a tsunami of political rivalry and ugliness. CEOs of prominent tech companies promised apps to help fight Covid19 – as we were told that over 700 people were working on those apps. Well, so much for those apps as nothing came out and if it did, it did not work. In a pow wow of CEOs and the US administration, we saw handshakes and elbow bumps, and were told that within days the parking lots of large department stores and pharmacies will have Covid19 testing facilities. But even after months, it was still not there.

Our supply chains crumbled. With first signs of trouble, we were out of toilet paper, cleaning supplies, and masks. Our healthcare workers were asked to reuse masks, even bandannas. We were out of hospital beds and ventilators, medicines and masks, cleaning supplies and food. Dead bodies piled up in trailers outside hospitals as death toll turned from hundreds to thousands and then tens of thousands.

Our character was on display as we saw people snatching supplies from each other, and countries fighting over masks and ventilators. We observed utter disregard for others as people hoarded and filled their basements with months, even years, of supplies – and companies allowed that to happen. Shocked and dumbfounded, few (if any) companies were prepared for such an event. Inventories dried out. Machines stopped churning. Warehouses and distribution centers emptied.

In times when the entire humanity should have come together – populations within many countries stood more divided than ever. We saw hate filled outrage and street fights, looting and mass discontent.

But prejudices did not just stop at an international level. The disease quickly took a racial and political tone in America. With complete disregard for others, many organized Covid19 parties. The valor and sacrifice of the previous generations (for example during the WW2 and Vietnam) was a thing of the past. This time around young people found one summer without partying as too-much-to-give-up and unbearable, and were willing to risk their own lives and the lives of their parents and grandparents for an hour of entertainment. Our politicians turned the crises into red and blue virus. Our media outlets quickly took sides. As death toll mounted – we became even more entrenched, more selfish, and frankly more stupid.

Our agencies told us that wearing masks is not important. Then they told us to wear masks. Even wearing of masks turned into political symbolism. Instead of viewing it as a common courtesy – even if we thought it was ineffective – it became a matter of personal honor, freedom, and political expression.

The economic stimulus became another battleground of its own. How much? For whom? How to allocate? How to get it to the people who need it most? Answering these questions should not have been as complicated as it became. As political rivalries intensified, foreign adversaries and state actors, as well as domestic special interests, set the narratives for even more conflict. The most vulnerable times for mankind became the hunting grounds for exploiting peace, starting social conflicts, making people hate each other, and turning countries into ideological battlegrounds.

When it came to developing a vaccine, the matters became even worse. Instead of sharing data and information, algorithms, science, and technology, we quickly divided the world into a clash of vaccines. The Oxford vaccine vs the Russian vaccine. The Chinese vaccine vs. the American vaccine. Within the pharmaceutical industry – pharma giants rushed to develop their own versions – instead of sharing information to solve the common problem. Smelling mega profits, billionaires launched their own little side projects to develop vaccines. Accusations of hacking into each other’s data were thrown around. Countries blamed each other for starting and spreading the virus. Countries taunted each other’s vaccines, instead of feeling relieved that a vaccine exists. And WHO lost its funding from a major donor, which arguably depleted its ability to function constructively.

What we observed in the last seven months is a world without any leadership. What we saw is the degeneration of leadership in human society and that must serve as an example of who we are or what we have become.

While we can relate to our experience, the reality was not that different in dozens of other countries. The same saga unfolded all over the world. Also, this was not the failure of one man or woman, or even one administration. It was a failure of the entire human society to learn how to solve our greatest challenges together. We should all be extremely concerned about what lies ahead – as we face the new collective challenges of climate change, artificial intelligence, and new geopolitical rivalries.

THE MAIN LESSONS

As Covid19 provided us a mirror to see our true face and uncovered our collective reality – certain fallacies and myths were exposed. We discovered that we overestimated in certain things – and underestimated in others. Here is a breakdown:

Overestimate our leadership abilities: Covid19 is a humbling reminder that our leadership trainings, business education, and books did not prepare us for true leadership. True leadership demands putting people first – not “our people” and “their people”, but people as humans – and we failed to do that. In a world that seems so bent on deglobalization, this is probably the last chance we would get to come together in a meaningful way to address and confront our common problems. As history from the previous century teaches us, when we all go our own ways, sooner or later we all run into each other – but it happens with bombs and militaries and not with pens and emissaries. There is an urgent need to deescalate global tensions, reduce fear, restart global cooperation, and focus on our common problems.

Overestimate our risk management abilities: Covid19 is also a stark reminder that how bad we are at risk identification and mitigation. We failed to see it coming and were least prepared when it came. As Covid19 showed us, anytime something out of the ordinary happens, regardless of how trivial it is to handle, we show complete incoherence. We tend to experience a total meltdown. Like a trapped animal, we become anxious and go in a state of panic. With all this technology and billions spent on risk management infrastructures, we should have done better.

Overestimation of “empathy”: Despite our processing power and data and algorithms, we failed to understand a simple relationship “human-to-human contact + no vaccine = deadly virus”. The virus does not know national borders or race or ethnicities or religions – but our biased minds do. We overestimated how much we know about each other. Knowing the names of foreign foods and visiting their landmarks is not empathy. A human bond must exist between various civilizations.

Underestimate the impact of the behavior modification technology: In an influencer dominated social media culture, I will go out on a limb to share that sometimes I feel like we are in some real life Black Mirror (Netflix) show. With little attention spans and overactive fingers sliding on phone screens going from one video to another, we have lost the ability to think deeply, to truly understand each other, to develop human empathy. We believe that our technologies have brought us together, but they have also turned us into vicious animals desperately hunting for “likes”. If there is so much of “me” in the equation, how can there be others in the same space. In other words, I believe we have given birth to a new type of digital insensitivity and narcissism.

What does this mean for the ESG industry? This means ESG should consider this failure of leadership as a validation that ESG matters. This means that authentic and true ESG is necessary to drive change. This means ESG is our best hope. We have received yet another proof that a world without governance and ethics, without social consciousness and responsibility, without addressing our common problems in a responsible way, is a world doomed to fail.

THE REAL HEROES AND TRUE LEADERS

In the middle of this crisis – a crisis worsened by the lack of leadership – there lies a remarkable story of millions of people demonstrating true leadership. These are the stories of courage and sacrifice, but all have the same common theme: for others. These are the stories of doctors and nurses, teachers and frontline workers, police and firemen/firewomen, garbage collectors and taxi drivers, meat plant workers and delivery people, pharmacists and store clerks. They demonstrated leadership when their leaders with C-suite titles failed. They gave hope when presidents and prime ministers, congresses and parliaments, cabinets and ministers failed to do the most basic job of inspiring us. The common person prevailed and rose to the occasion.

The post-Covid19 era must belong to these workers. They deserve more than a shout-out or a spot on the cover of Time magazine. They deserve better leaders.

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This is the first in a series of articles authored by Professor Naqvi on ESG and Artificial Intelligence.

Professor Naqvi to give Keynote at CFO and COO Conference

We are pleased to welcome Prof. Al Naqvi, CEO & Researcher, American Institute of Artificial Intelligence, on board as a keynote speaker at the rescheduled CFOs & COOs Europe Forum on 12-13 October.

Discover how Al has become the core around which modern companies are built, and how it’s transforming the fund management industry. Professor Naqvi’s keynote presentation will be delivered live, immersing you in the latest developments from the frontline of artificial intelligence.

Join the virtual experience to meet Prof. Naqvi, ask any questions which you may have and discuss the topic further in our interactive ‘meet the speaker format’.

Download the agenda:
https://lnkd.in/dPF_btK

#PDI #CFO #COO #Virtualevents #AI #Finance #Operations #Tax #Technology

How to improve audit with AI?

As scandal after scandal have destroyed audit credibility, the audit profession needs a total restructuring. One type of restructuring happened when PCAOB (regulator, Public Company Accounting Oversight Board) took control away from accounting firms’ self-regulation. The ongoing problems with audit are now forcing the exploration of new ideas. Some believe that the large accounting firms should be fragmented into smaller firms and must require the inclusion of smaller firms as partners. Others are suggesting having government take over the entire audit business (like IRS for taxes).

Audit suffers from both effectiveness and efficiency. In fact, the problem with audit is that effectiveness and efficiency goals tend to work against each other. If you seek efficiency, you may have to compromise on effectiveness and vice versa. Machine Learning can greatly improve audit outcomes. The application of machine learning happens in all stages of audits. Machine learning can also help discover new business models for audit firms.

Audit automation can be viewed as automation of audit planning, audit evaluation, internal controls risk assessment, reporting, fraud detection, valuation, and other such audit process tasks. AIAI offers a report on machine learning in audit.

Machine Learning for Bond Pricing

ML and Bond Pricing

Bond origination and OTC trading continues to be manual, risky, and problematic. The problem comes from figuring out how to price the bond. It is not easy to measure credit risk. There are multiple liquidity buckets and different issuers. Credit risk assessment is a major problem and it impacts price discovery.

Measuring credit pricing risk and valuation requires using many different datasets. Dynamic monitoring of credit risk implies using dynamic data that includes factors such fundamentals, alternative data, transactional data, and behavioral data to price risk.

Many models are being developed and deployed. Some are based upon older machine learning techniques including support vector machines, regression, and k-nearest neighbor. Deep learning and neural networks are now being used to improve the outcomes.

AIAI offers a comprehensive report on these developments.

How Covid19 has Accelerated and Changed Data Needs?

Covid-19 has changed the world. The unthinkable has happened. The world has just experienced the strangest and most unexpected times. This is placed our businesses, industries, institutions, and countries on different trajectories. In some areas, growth has accelerated. In others, it has stagnated. The structural changes in the economy are large enough that asset managers, investors, firms, analysts, and strategists need to reanalyze their assumptions.

Using pre-Covid assumptions for the post Covid-19 world is an extremely risky undertaking.

The data acquired and produced during these times will be critical for many types of analysis and machine learning solutions. Let us look at some examples:

  • Marketing Data: Consumer purchasing data gives tremendous insights into how stress, pandemics, physical localization, and change impacts our buying habits and purchasing behaviors.
  • Retail Data: Many US retailers filed for bankruptcy. Others saw an increase in sales. Chinese and Europe retail sectors are improving but the pre-Covid19 business is different than post-Covid19.
  • Supply Chain Data: The emerging geopolitical situations, rise of nationalism and new rivalries, and now Covid19 – all are pointing that 2020’s will the years of realignment and restructuring of supply chains. Supply chain data will be critical to assess and predict the new reality.
  • Trading Data: Search for alpha signal is one area of exploration. The other is behavioral information. The information about how investors and machines behaved during the Covid19 turmoil is embedded in the data.
  • Geopolitical Data: Geopolitical risk is increasing. With treaties (including nuclear treaties between Russia and US) falling apart, increasing conflicts in the Middle East, and a new West-China rivalry taking shape, the geopolitical risk assumptions need to be revisited.
  • Healthcare Data: Covid19 has given the healthcare industry significant data to analyze and develop new therapeutic and diagnostic solutions.

The bottomline is that it is time for data suppliers to create awareness that Covid-19 has changed the underlying assumptions related to markets, behaviors, competitive structures, and industry and company performance. New analysis backed by new data will help understand the shift.

Why strategies developed in labs fail to perform in real life situations?

Hundreds of papers are published in finance that claim to discover successful new investment strategies. However, when the strategies are deployed in real market situations, they often fail to perform as they did in the lab. Some of the major reasons for their failure are as follows:

  1. The real-life market data tends to be very different than the data used in the labs. Lab data contains full and corrected data whereas in actual situations data is delayed for several minutes or hours after markets close.
  2. Data is sometimes corrected in periods (days, weeks, or months) after the periods. For instance, data might contain corrected (restatements) earnings.
  3. The sampling of data may not be random as the model developer has ex-post knowledge of events and hence it is likely that data taken is from normal times and not when markets experienced abnormal periods.
  4. Other than the tick data, other data – especially fundamentals data – is extremely limited.
  5. While a pattern may be discovered but without proper economic foundations, it becomes too risky to deploy in production.
  6. Model works well with the data used by the modelers but fails to generalize (overfitting).

For those reasons, it is important to have standards developed for machine learning in finance. AIAI is working to establish those standards.

AIAI-Newsletter July 13 2020

7 Opportunities in 7 Minutes on Day 7

Your Sunday Briefing starts here:

Opportunity 1: Don’t forget the investment banks

What is on the radar screen?

Do you know there are several powerful opportunities to transform investment banks with AI? Investment banks are looking for creative solutions to their problems and AI/ML can directly help. After a seven-year M&A boom, Covid19 has greatly reduced the M&A activity. With large revenues gone, smaller and medium sized investment banks are feeling the pain. Large shops have diversified income streams from trading and capital markets, so they are better positioned to handle the storm.

Smaller boutique firms hold significant market share of M&A deals in the industry. Covid19 has shrunk the deal volume. Boutique firms also offer many other types of services – such as capital raise, debt, and risk management.

Why it matters?

Post Covid19, the old model of investment banking needs to be jumpstarted. Many mid-sized and smaller firms have the intellectual bandwidth and relationships to close the deals, but they need to transition to the digital age. This means to have intelligent agents that can perform initial investment banking work, attract potential client interest, outline opportunities, and be able to demonstrate value in a virtual environment.

What’s the scoop?

The scoop is that many leaders are confused and seeking clear guidance. There are budgets to transform the firms.

How to do it?

Help transition mid-sized banks and smaller investment banks into world-class digital deal shops. Strategic transformation requires building the following capabilities:

  • Identify opportunities based upon publicly available data
  • Proposal building
  • Due diligence of the business
  • Automated valuation
  • Evaluate and analyze the deal
  • M&A document preparation
  • Develop insights about potential acquirers
  • Perform detailed due diligence
  • Valuation readjustment
  • Negotiation strategy development
  • Document review and management
  • Transaction management

Opportunity 2: There’s lots of green for AI in the Green bonds

Is your firm well positioned to target a $1 Trillion industry? If your sales teams are not familiar with ESG, get them ready fast. ESG is one of the greatest opportunity for AI. Here is the scoop.

What is on the radar screen?

ESG stands for Environment, Social, and (Corporate) Governance. ESG has become a major phenomenon in the finance sector. Companies issue ESG performance reports. Asset Management firms look for companies that are responsible in terms of ESG. Investors seek portfolios composed of ESG firms. Mutual funds, ETF, and other investment vehicles are designed for responsible investment. And there is an entire category of green (sustainable) bonds.

Green bonds market has seen significant expansion while simultaneously being criticized for lacking oversight, audit accountability, and greenwashing. Greenwashing implies that issuers are not really serious about benefiting the environment.

Why it matters?

There is over $1 Trillion of sustainable (green) debt. While everyone is rushing into ESG, the foundational elements of ESG are not well developed. For example, clear standards may not exist for what classifies as good performance in ESG. Identifying good ESG opportunities, developing standards, managing risk for ESG, and valuing ESG securities are powerful problems that only AI can solve.

What’s the scoop?

The inside scoop is that anxiety is growing and a lot is at stake. Wrong valuations mean bad investments.

How to do it?

Target investors, underwriters, and issuers and inform and educate them on how they can leverage ML/RPA to solve the following problems:

  • Identify green opportunities
  • Use ML/RPA for materiality assessments
  • Deploy valuation frameworks for assessing credibility scores
  • Help clients pick the right securities with ML
  • Identify what the right standards should be
  • Understand and manage the risk for green bonds

Opportunity 3: Sharpening the knives, preparing turnaround firms

Hidden within the highly lucrative layers of the finance industry is a powerful subsector of turnaround and restructuring. Partly legal, partly business and strategy – restructuring and turnaround firms (and departments) are getting ready for what lies ahead. They need ML to create their competitive advantage.

What is on the radar screen?

High risk corporate debt was a major problem even before the Covid19. Covid19 has made the problem worse. Companies had to seek incremental financing and many investment banks (Goldman and Morgan) saw an increase in Q2 2020 revenues from that activity. However, part of the Fed intervention to save the economy comes in the form of buying high risk (junk) bonds. This is done to save the corporate bond market from collapsing. The process began in May of 2020 and it has shrunk distressed debt from $750 billion to about $400 billion. But this cannot go on forever. This means significant restructuring is coming and companies need AI tools to prepare for what lies ahead.

Why it matters?

U.S. high-yield bond market size is nearly $1.2 Trillion. Investing in this market is one area where ML will be the only effective solution. However, the real area of high opportunity is the upcoming distressed debt investment and bankruptcies.

What’s the scoop?

The inside scoop is that restructuring and turnaround firms competed based upon their size and knowledge base. But now things could be different. The ability to analyze debt, valuation, exploring strategic options, bankruptcy case preparation, and management during bankruptcy are all automatable areas. Firms that invest in these areas will become the new industry leaders.

How to do it?

Target issuers, investors, and restructuring and turnaround firms and inform them about the power of ML/RPA to transform the following processes:

  • Identify clients (opportunities for restructuring)
  • Create options for clients
  • Help with Valuation of distressed debt
  • Analyze bond for comparative structures
  • Analyze bonds covenants
  • Perform Bankruptcy analysis and options discovery
  • Perform Bankruptcy case preparation
  • Discover Distressed debt buying opportunities
  • Create pathways and maps for renewal

Opportunity 4: Cat Bonds and Insurers

Do you know that ML/RPA can play a huge role in driving the future of the insurance industry? No, this area is not about cats that meow and purr and have nine lives. It is about premiums and payouts (often) related to one life. Here is the scoop.

What is on the radar screen?

Even before the Covid19 catastrophe, the insurance business was having bad years due to floods, fires, and storms. Then comes Covid. Insurance Linked Securities (ILS) suffered. Cat Bonds (catastrophe bonds) are high-yield debt instruments that are used to raise money for companies in the insurance industry in the events of natural disasters.

Why it matters?

With $36 billion of cat bonds outstanding, and crisis after crisis, what’s there not to worry about! The market has come to a halt and only innovation can jumpstart the market. 

What’s the scoop?

Imagine the problems with these securities. How to value them? How to price the risk? Reinsurers who package and sell these products are finding new ways to attract the market. But investors are cautious. ML can help all the players in this subsector.

How to do it?

Target insurers, reinsurance shops, and investors and show them how ML/RPA can revolutionize the industry:

  • Mortality and catastrophe forecasting
  • ILS pricing
  • Payout management
  • Terms and conditions – bond covenants analysis
  • New structures (for structured products) analysis
  • Risk assessment and valuation

Opportunity 5: China, China, and China

This is an area that requires immediate ML/RPA help. No, it is not about opening an office in China – it is about helping your non-China based financial services clients evaluate and estimate the risk related to the Hong Kong problems.

What is on the radar screen?

Against the backdrop of protests and growing US-China rivalry, China passed the Hong Kong security law. Many countries condemned the move. Caught in the middle are the firms who have significant exposure to China. First, do your clients know which firms have higher exposure to China? Second, how do you measure the exposure?

Why it matters?

With a $14 Trillion nominal GDP, China is the second largest economy in the world. While things were fine a few years ago, more recently China and US seem to be in a lock horn position. Geopolitical issues do have consequences. The question for your clients is how will they get impacted?

What’s the scoop?

Financial services providers are concerned about how to analyze risks related to China. With tradewars and policy quarrels, the risk related to doing business with China has changed. ML can help. 

How to do it?

Target your financial services clients and help them make sense of the Chinese situation with machine learning with the following solutions:

  • How to develop insights into the Chinese situation
  • Risk estimation
  • Business risk assessment
  • Investment risk assessment
  • Alternatives risk
  • Pricing geopolitical risk in securities
  • Discover who is exposed
  • Discover how they are exposed

Opportunity 6: Covid-19 Earnings and Portfolios

Covid-19 earnings reporting season is beginning. The traditional measures of risks have changed. Companies, sectors, and industries are experiencing structural changes. Only ML can help develop the forecasts necessary for portfolio realignments. Is you sales team ready to have a real conversation about earnings and portfolio realignment? The discussion below can help.

What is on the radar screen?

As companies announce earnings, the announcements will contain signals about whose business has been impacted in what ways. Analysts will try to determine which impacts are permanent and which are temporary. They will need to assess how the fundamental structures of various industries and economies have changed. Money will follow the growth opportunities and risk will be repriced.

Why it matters?

Covid-19 has ushered the global economy to a new reality. It has produced new winners and losers. However, not every loser is gone forever and not every winner will stay as winner. Too much change is in the air and ML can show the way out. 

What’s the scoop?

Asset Managers want to understand the dynamics of the new reality. Many lack appropriate tools.

How to do it?

Target asset management firms to explain how ML can help in the transformation. Build the following capabilities for clients:

  • Fundamentals forecasting based upon Covid-19 factors
  • Behavioral modeling
  • Structural changes in the economy
  • NLP based tracking of risks
  • Supply chain impacts
  • Business model transformations

Opportunity 7: Social Injustice and Diversity matters for your clients

The major protests in the United States against racial injustices have influenced financial services firms to rethink their internal policies, procedures, and HR frameworks. ML/RPA can help create fast value. If your sales teams are too timid to knock on the doors of the HR departments of financial services firms, they are at a disadvantage.

What is on the radar screen?

The nationwide protests and racial justice and equality movement has left many financial services firms to reconsider their ways of doing business. But this reflexivity goes beyond just HR as many are concerned about their policies related to issuance of debt, product designs, governance, and compliance.

Why it matters?

The racial justice movement is not going away. The movement has spread beyond the United States and across the world. United Nations has recently issued a statement. For many people the root of injustice often lies in access to financial services. There are hundreds of issues that would need to be resolved. ML can accelerate the journey. 

What’s the scoop?

For the past one month, this has been the most talked about topic by HR specialists (even more than Covid). Companies who will build a beachhead in this category will not only have great business opportunity, they will also pursue responsible business practices.

How to do it?

Start with HR and then access the operations teams. Target HR departments of large financial services firms and show them how ML can help increase diversity, enhance social justice, improve race relations, and create a healthier environment. Build the following capabilities for clients:

  • Identify issues from policies
  • Identify issues from language
  • Identify issues from organizational analysis
  • Discover the image of the firm in terms of racial justice
  • Analyze complaints
  • Analyze lawsuits
  • Analyze management and board discussions (from 10k etc)
  • Measure diversity

Commerzbank: Opportunity for AI centric transformation

Under pressure from private equity group Cerberus, Germany’s second largest lender began a restructuring process last year that targeted a meager 4% RoE by 2023 – significantly less than the investor expectations (cost of capital) of 10% return. The Covid19 crises led to a €295 million loss in the first quarter of 2020. As American celebrated the Independence Day holiday, the CEO and the chairman of Commerzbank have left. Their leaving is widely seen as a move by Cerberus, the second largest shareholder has been able to influence the exit of both the CEO and the chairman.
Based upon reinventing the firm, there is significant momentum and appetitive for total transformation. With that type of board and management support, this is the best time for Commerzbank to rethink its future and build it upon an AI centric transformation.
As the bank recognizes the importance of AI as a new paradigm for transformation, the mindset needs to evolve from viewing AI as a technology to support the strategy to seeing AI as the central strategy. This is a great opportunity to transform the bank with full scale automation. The restructuring plan was developed by a consulting firm; however, an AI centric transformation plan is different

Unlike typical deterministic planning that goes in typical strategy models, AI centric transformation is performed with a flexible and creative mindset. Creative planning happens when firms view AI as a simultaneous transformation of business model, business strategy, operational plans, and process automation. In that type of planning, firms develop competitive advantage through a systematic exploration of data.

In the past, several IT initiatives were launched by Commerzbank. Even though some of those initiatives were successful, they were not able to change the trajectory of losses or to create a meaningful competitive advantage for the firm. The use of AI remained at a tactical level. The current change offers an incredible opportunity to rethink and plan the future of Commerzbank.