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David Clement

Artificial intelligence & integrity: winners & losers

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Explore the importance of ethical AI development. Learn how integrity and ethics can prevent misuse, build trust, and ensure fairness in AI applications. Discover the potential societal impacts of generative AI and the need for regulatory frameworks to safeguard against malicious uses.

The emergence of artificial intelligence (AI) enabled applications have the potential for societal disruption, especially with the advent of generative AI (GAI) tools. There is a growing concern about the potential for sophisticated use of GAI to create deepfake videos, images, audios, and text that manipulates and/or fabricates content with the potential to spread misinformation, … Read more

MindBridge launches new advancements with its latest release in 2022

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Leading next-generation data anomaly and risk detection for financial data. Read more in the Press Release published here. While it’s true that there’s nothing more constant than change, in today’s financial markets, this piece of wisdom could not be more accurate. With the growing volumes of data, increasing complexities of multinationals, and the ongoing march … Read more

AI in finance: Helping professionals shift from hindsight to insight to foresight

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Stopping dominoes with foresight

We are facing an unprecedented time of global uncertainty created by the COVID-19 virus that has unleashed a global healthcare crisis. Humanity is fighting a war against an invisible enemy that is attacking humans around the world and sparing no country. We need not be pessimistic or optimistic but rather realists and learn from the history of humanity. Human ingenuity will prevail, and humanity will survive.

We have entered a new world after COVID-19 with very different assumptions than we had in the old world when the world GDP yielded a record high of $85T. The world GDP has been severely impacted by the lockdown stipulations that were imposed to minimize the spread of the virus within the population. The key pillars of the economy are consumer and companies’ spending. If this slows down, it can lead to a recession and even depression. The lockdown restrictions are being relaxed and governments and central banks around the world are injecting massive amounts of funds into the hands of individuals and companies in an effort to reopen the economy to avoid an economic crisis.

How can artificial intelligence in finance help organizations pull through?

A renewed focus on financial errors

During economic uncertainty, an added vigilance is needed by those responsible to ensure the accuracy and integrity of the financial records that are being relied upon to make decisions about the operations of their organizations. A report by the Association of Certified Fraud Examiners (ACFE) “2020-Report to the Nations”- 2020 Global Study on Occupational Fraud and Abuse estimates that the yearly cost to the world due to fraud and abuse is about $4.5T or 5% of the world GDP. They examined over 2500 cases from 125 countries with combined losses of $3.6B with an average loss by case of $1.5M and a typical case lasting 14 months before being detected.

Whereas corruption was the most common type of fraud, the most costly were financial statements fraud schemes, even though they represented only 10% of the cases.  The breakdown of the detection methods reveals that analytics plays only a small role in the detection of occupational fraud: Human tips; 43%, internal audit; 12%, management reviews; 5%, by accident; 5%, whereas external audit catches only 4%.

A 2019 survey by Blackline provided insights into the concerns by executives with inaccuracies in financial data. With over 1100 C-suite executives and finance professionals from mid- to large-size organizations around the world, the white paper stated that:

“55% are not confident that they can identify financial errors before reporting results, 70% claim that their organizations made a significant business decision based on inaccurate financial data and 26% are concerned over errors that they know must exist but they have no visibility”.

 

The power of AI in finance

Finance professionals that rely on outdated tools and methodologies do not offer the best visibility into finding errors, errors with intent, errors that are considered fraud, and general mismanagement of the financial dataset in their organizations. The world is already witnessing a major trend toward moving to the cloud and becoming digital native and these must be vigorously pursued by organizations that want to be of the forefront of growth post the crisis.

Becoming digital native enables companies to move towards a near real-time view of their financial data and, coupled with AI in finance functions, the ability to fully analyze 100% of transactions. This ensures transparency to key stakeholders such as board members and auditors and aids in the identification of any anomalies in their financial records.

Currently, a company’s financial records are examined by external auditors on a yearly basis and evaluated using a sampling method that leaves the bulk of the dataset untouched. This method of rear view-mirror assessment provides C-suite executives with a hindsight perspective and the fear that decisions are made based on inaccurate and untimely information. Using AI-based tools to review 100% of the financial records in near real-time offers C-level executives with insights into data and, by using the appropriate analytics built into the AI applications, offers foresights into the operations of the company.

The two most important behaviors that companies must have to thrive post COVID-19 are resilience and adaptability. Resilience is defined as the ability to withstand or recover quickly from difficult conditions whereas adaptability is defined as the quality of being able to adjust to new conditions. Companies must build their operations and culture around resilience and adaptability so they can work efficiently during the “new normal” when we emerge out of this dark tunnel will become stronger and better off.

An article published by the Boston Consulting Group titled “The Rise of the AI-Powered Company in the Postcrisis World” highlights the tremendous opportunity for companies that are going to digital native, moving to the cloud, and adopting AI in finance applications to supercharge their operations. Arvind Krishna, in his inaugural speech as IBM’s new CEO, said, “I am predicting today that every company will become an AI company – not because they can, but because they must. Digital transformation means putting artificial intelligence at the center of workflows, and using the insights generated from that process to constantly improve products and services.”

 

The auditor’s fallacy: The law of small numbers

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big data analytics in auditing

Humans have used simple statistical sampling for millennia to make generalized sense of the world around us. Living in a resource-constrained world, statisticians gave emperors, surveyors, and accountants a simple workaround to the prohibitively intensive process of counting, checking, and validating everything. Sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of a much larger population.

Random sampling is an old idea, mentioned several times in the Bible with the word “census,” derived from the Latin word censere – “to estimate”. One of the world’s earliest preserved censuses was held in China in 2 AD during the Han Dynasty and appeared later in Ancient Egypt and Greece as a means of tallying or estimating population characteristics and demographics. Historically, the immense benefits of sampling’s simplicity outweighed any cost to accuracy. “Close enough” was good enough.

Fast forward to 2019 and we’re living in a tremendously different world with exploding data volumes and complexity. One domain where this is particularly problematic is the world of audit and assurance, where achieving a passable level of reasonable assurance is increasingly challenging.

For MindBridge Ai, the most obvious place to apply our advanced analytics and breakthroughs in machine learning is the audit world. To help everyone move toward a more wholesome and comprehensive risk analysis, enabling more informed decisions.

Simply, MindBridge Ai Auditor can be thought of as an advanced transaction analysis platform and decision-making tool that amplifies our ability to make sense of the complex and data-saturated world around us. Within our digital world, it’s now possible to pivot from reliance on sampling to algorithmically analyzing everything in a population.

Why is this evolution a good idea?

Why audit sampling doesn’t work

In Daniel Kahneman’s seminal work, “Thinking, Fast and Slow”, the author deals with problems related to “the law of small numbers,” the set of assumptions underlying prevailing statistical sampling techniques.

People have erroneous intuitions about the laws of chance. In particular, they regard a sample randomly drawn from a population as highly representative, that is, similar to the population in all essential characteristics. The prevalence of this belief and its unfortunate consequences for the audit and assurance business are the countless high-profile audit failures. The mounting issues related to outdated standards and problems related to transparency and independence have prompted regulators to go as far as tabling legislation for the break-up of the dominant Big Four firms.

Kahneman makes the point that we’ve known for a long time: The results of large samples deserve more trust than smaller samples. Even people with limited statistical knowledge are intuitively familiar with this law of large numbers but due to human bias, judgmental heuristics and various cognitive filters, we jump to problematic conclusions/interpretations:

  • Humans are not good intuitive statisticians. For an audit professional, sampling variation is not a curiosity, but rather it’s a nuisance and a costly obstacle that turns the undertaking of every audit engagement into a risky gamble.
  • There’s a strong natural bias towards believing that small samples closely resemble the population from which they are drawn. As humans, we are prone to exaggerate the consistency and coherence of what we see. The exaggerated faith of auditors in what can be learned from a few observations is closely related to the halo effect. The sense we often get is that we understand a problem or person or situation when we actually know very little.

This is relevant for auditors because our predisposition for causal thinking exposes us to serious mistakes in evaluating the randomness of a truly random event. This human instinct and associative cognitive machinery seeks simple cause and effect relationships. The widespread misunderstanding of randomness sometimes has significant consequences.

The difficulty we have with statistical irregularities is that they call for a different approach. Instead of focusing on how the event came to be, the statistical view relates to what could have happened instead. Nothing, in particular, caused it to be what it is – chance selected from among its alternatives.

An example shared by Kahneman illustrates the ease with which people see patterns where none exist. During the intensive rocket bombing of London in World War II, it was generally believed that the bombing could not be random because a map of hits revealed conspicuous gaps. Some suspected that German spies were located in the unharmed areas. Careful statistical analysis revealed that the distribution of hits was typical of a random process and typical as well in evoking a strong impression that it was not random. “To the untrained eye,” the author remarks, “randomness appears as regularity or tendency to cluster.” The human psyche is rife with bias and errors in calculation, that have meaningful consequences in our work and lives. Algorithmic and computational tools like MindBridge Ai Auditor stand to improve the human ability to make better and less biased decisions.

Minimizing risk exposure

In Kahneman’s article “Belief in the Law of Small Numbers,” it was explained that intuitions about random sampling appeared to satisfy the law of small numbers, which asserts that the law of large numbers applies to small numbers as well. It also included a strongly-worded recommendation “that professionals regard their statistical intuitions with proper suspicion and replace impression formation by computation wherever possible”. As an example, Kahneman points out that professionals commonly choose samples so small that they expose themselves to a 50% risk of failing to confirm their true hypothesis. A coin toss.

A plausible explanation is that decisions about sample size reflect prevalent intuitive misconceptions of the extent of sampling variation. Technology such as machine learning and pattern recognition are removing this bias to the enormous benefit of practitioners currently at the mercy of mere sampling luck to find what is important.

Thanks to recent advances in cognitive psychology, we can now see that the law of small numbers is part of two larger stories about the workings of the human mind:

  • Exaggerated faith in small numbers is only one example of a more general illusion – we pay more attention to the content of messages than to information about their reliability. As a result, we end up with a view of the world around us that is simpler and more coherent than the data justifies. Jumping to conclusions is a safer sport in the world of our imaginations than it is in reality.
  • Statistics produce many observations that appear to beg for a causal explanation but do not lend themselves to such an explanation. Many facts of the world are due to chance including accidents of sampling. Causal explanations of chance events are inevitably wrong.

We are at an important crossroads where we must reconsider traditional approaches like audit sampling in the context of the incredible technology that is now available. For companies that are struggling to interact with huge volumes of digital transactions, detect risk, and extract meaningful insights, MindBridge Ai Auditor is an elegant and powerful solution.

 

Ethical AI goes beyond legal AI

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internal audit sampling

The recent case of the Statistics Canada project to use personal financial data from banks to study the spending habits of Canadians provides a very clear lesson in the ethics of AI. In this case, Statistics Canada has clear legal authority to request and use this data and it’s very likely that the proposed project conforms with ethical standards for AI and analytics. There is also an excellent case that this project will provide significant public benefit. However, it’s also clear that the project failed to gain a moral license from Canadians and by failing in this regard, they have put the project and perhaps their freedom to operate at risk.

Shining a light on the project

At this point, the details about the project are difficult to come by and I have not seen evidence of any public consultation or public notice of the project. This project came to light through a news story published by Global News on Oct 26, 2018. Based on the news reports and a bias towards the general good intentions of government bureaucracy, we can infer that Statistics Canada finds its current survey-based approach to collecting data on Canadian spending habits deeply inadequate. I also expect that the bureaucrats involved saw the opportunity to provide a more accurate picture of Canadian spending habits, more efficiently, and with less burden on the members of the Canadian public. After consulting with Justice, they also determined that they have the legal authority to do so and they honestly believe that Canadians by and large trust Statistics Canada with their personal data. So they made the decision to use the legislation governing Statistics Canada and request data from the banks. I also expect that bureaucrats knew that this request could be misunderstood by the public so they decided to act out of the public eye, trusting that the banks would comply without fuss. Of course, this project will benefit the banks greatly.

What possibly could go wrong?

Application to analytics and AI

I want to stress that there was no malice in the bureaucratic intentions behind this project. To the contrary, I see the motivations as things we want to encourage: innovation, efficiency, improved quality, and Canadian competitiveness. Where things may have went wrong is a long-standing bureaucratic culture of secrecy. The causes and solutions to this problem with bureaucratic culture is a topic for another day.

No doubt there will be calls for changes to the Statistics Act but I think cries for wholesale changes are misguided. Overall, the Act provides a good example of a legal framework for analytics. I’m not saying that events such as this should be ignored, rather the justice department should be tasked with reviewing the act and regulations with the goal of  improving the legislation — perhaps by making public consultation mandatory when Statistics Canada wants to collect personal data indirectly.

Legislative frameworks for analytics and AI must do a few things well:

  • They must protect privacy
  • They must ensure that the collection and use of personal data contributes to the general social welfare broadly defined
  • They must protect the ability to innovate

On this last point, legislative frameworks must be flexible and protect against egregious misuse while relying on social and market mechanisms to align activity with public expectations. Authority granted by legislation must protect against the right to innovate being blocked by a radical few. By these tests, the Statistics Act stands up well.

Having legal authority to do something is not the same as acting morally or ethically. In general, ethical use of personal data requires that the data subjects explicitly consent to the collection and use of their data. One can assume that the data subjects have given a license to the analytics organization to use their data for the intended purposes but, in practice, this is complicated and there are exceptions to this approach. One such exception is that the use serves the public good. From what I understand of the proposed use of data by Statistics Canada, this test is clearly met.

How we can do better

So what went wrong? The personal data in the possession of the banks was created as part of delivering banking services. The public expectation, perhaps naively, is that that is the only use they have consented to. The attempt by a third party to access and use this data to develop profiles of consumer spending habits goes well beyond their expectations. In this case, the legal authority to do this is irrelevant and disturbing. At the very least, a public education campaign describing why this is important to Canada and Canadians and how each individual will be protected in the process would have gone a long way to easing the public’s concern.

More fulsome consultation and offering individuals with the ability to opt out would likely have eliminated all barriers and created a positive opinion of the project. Each time an organization tries to fly under the radar when accessing large quantities of personal data, they create a risk of public backlash that will saddle the industry with stifling regulation.

The AI industry needs the right to ethically innovate and to do this, we need a regulatory environment that gives latitude to innovate. This requires the public to be confident that industry members will act ethically within the bounds of the legislation. Each time the AI industry goes against these expectations, the right to innovate is put at risk.

 

Congratulations, you have been entrusted to be the CEO… Now what?

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auditing services

Becoming a CEO is like becoming a new parent, there is no true user manual to guide you through the unique challenges you will face along the way. You can read a multitude of books about “how to” raise a child, however, there will inevitably be unexpected curve balls beyond what you already know and what you have read.

Ultimately it is your responsibility to ensure the safety and well-being of your child, under all conditions, 24/7, irrespective of the situation. So, let me start by defining the type of CEO that you are going to be. There are three models to choose from: the “plate spinner,” the “one-man band” and the “conductor.”

The “plate spinner” CEO generally does not have a full management team and by necessity has to rely upon themselves to juggle all, or nearly all, of the functional activities that must be executed by the team. The key drawback is that some tasks may fall between the cracks. It is similar to the “plate spinner” who must run back to the first plate once he has completed spinning the last plate to ensure that the plates do not drop to the floor.

The “one-man band” CEO has a more balanced management team but chooses to address and resolve most functional tasks by themselves. As with the “plate spinner,” this model does not lead to optimal execution or maximize the company’s success.

A great “conductor” can achieve melodious results from the members of their orchestra just by hand motions and facial expressions and without the need to speak a word. Similarly, a great CEO will have their management team working harmoniously. The “conductor” model is the optimal approach in creating an environment inside the company that fosters balanced execution by all members of the team. While the team remains under the guidance of the CEO, this approach leads towards sustainable and predicable growth.

Now that you are familiar with the CEO models, some of which you may be emulating, let me outline the actions that, I believe, a CEO must carry out to be highly successful.

Build the team –The top priority and most fundamental task of the CEO is to find and hire the best management team. The strength of the team will determine the degree of success of the company.

Work on the business, not in the business –This is a very simple notion, to spend most of your time managing and less time doing. Your role is to create an ecosystem for your management team to operate within by defining the vision and setting objectives. Then, create the infrastructure necessary to measure the team’s performance in meeting these goals.

Lead by example – People in the company will follow you if they believe in your vision and your actions. It is as they say that actions speak louder than words. The process is simple; initially, people will award leaders a certain amount of “respect and trust credit” but as time progresses, there could be a drop in the original “trust” level. A leader must build up and maintain his or her “respect and trust credit” by their actions which will earn additional “trust credits”, just as a battery loses its charge overtime and requires recharging. Be mindful that as the battery discharges to lower levels, it may be more difficult, or highly unlikely, to reach full charge again.

Listen – A great leader will do less talking and more listening. After all, you have two ears and only one mouth for a reason. People have the basic need to feel that their voice is heard and as a result will be more engaged and vested in the company’s well-being.

Make decisions – The most basic function of a CEO is to make decisions. A CEO must conduct a proper evaluation and analysis of the actions of their team and company performance even when there is only partial data available before making final decisions. Please remember that “no decision” is a decision in itself.

Think strategically – Wayne Gretzky, arguably one of the most successful hockey players of all time, coined the phrase: “I skate to where the puck is going to be, not where it has been.” Companies have to continuously assess their market position by assembling available data from competitors, current and future product capabilities/performance and market trends. With this data, the team must create scenarios and plan ahead. Based on this strategic thinking the organization then has a guide for tactical execution based on the merit of the potential outcome.

Ultimately, the question is whether these attributes can be acquired, or are they inherent as part of the DNA of the individual. In other words, nature versus nurture. The good news is that most of the above traits can be acquired over time. Given sufficient and consistent practice, an individual can acquire these traits and evolve as a true leader.

Note: Above blog post was earlier posted on Eli Fathi’s blog.