Artificial Intelligence (AI) And Machine Learning (ML)

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Table of contents

  1. MACHINE LEARNING
  2. LAWS AND REGULATION – ETHICS OF USE OF TECHNOLOGY IN FINANCE
  3. REFERENCES

Machine learning and artificial intelligence are related, often present in the same context and sometimes used interchangeably. From just being a figment of someone’s imagination in sci-fi movies and novels, they have come a long way to augmenting human potential in doing tasks faster, more accurate and with greater precision each time, driven by technology, automation and innovation.

The father of artificial intelligence, John McCarthy, in the 1990s, defined the term as “artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs”. Generally, the term “AI” is used when a machine simulates functions that humans associate with other human minds, such as learning and problem solving (Krawczyk, B., 2016).

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On a very broad account, the areas of artificial intelligence are classified into 16 categories. These are: reasoning, programming, artificial life, belief revision, data mining, distributed AI, expert systems, genetic algorithms, systems, knowledge representation, machine learning, natural language understanding, neural networks, theorem proving, constraint satisfaction, and theory of computation (Meiring, G.A.M.; [image: ]Myburgh, H.C, 2015)

A lot of us today already have AI enabled systems in their homes such as Siri, Alexa and Google Assistant to name a few. This technology is becoming an integral part of our daily lives which will go on to influence in much wider terms around business activities as well. PwC research suggests global GDP could be up to 14% higher by 2030 as a result of AI, making it the biggest commercial opportunity in today’s fast-changing economy.

So, what does an AI powered world mean for accountants? Will robots replace us? Certainly, the transactional aspect of accounting and the need to collect and process data are particularly susceptible to automation. Accounting software such as QuickBooks Online, Sage Business Cloud Accounting, and Xero are all leveraging the power of AI to classify transactions from bank and credit card feeds automatically. A Harvard Business Review study noted that only 3% of organizations’ data meet basic data quality standards; on average 47% of newly created data records have at least one critical error.6. By leveraging technology to streamline accounting tasks instead of spending time on manual and repetitive tasks, accountants will be able to focus on building and analysing reports that drive business insights and decisions, securing our role in corporate reporting and as strategic advisors. The long-term benefits involve making routine tasks automated, proper accounting classification of charges, processing large amounts of data at speed of the light, predicting outcomes using trends and patterns processing large amount of data. accountants can add value in terms of bringing their professional scepticism and ability to interrogate, and having oversight of what the algorithm is doing,’ says Vaidyanathan, head of technology insight at ACCA.

However, even the best of AI Machines need to be controlled by humans, as a computer would mean nothing unless intelligence is loaded onto it – through programs and software written by humans. Excessive automation can do more harm than good and humans are underrated in that context; the true potential lies in not replacing humans but to augment and amplify them. Foremost we need to take into consideration our customer privacy, the potential lack of transparency, technological complexity and cost structure of the company (i.e. the hardware and software need to get updated with time to meet the latest requirements and machines need repairing and maintenance which need plenty of cost.) There is no doubt that machines are much better when it comes to working efficiently but they cannot replace the human connection that makes the team.

One of the best approaches to implement AI is based on the four pillars below: Create an environment of learning, implement the change, monitor the input/output, manage the risks and training the teams to operate the system.

  • Manage/sustain an AI plan with a comprehensive roadmap to identify priorities and modify them on ongoing basis.
  • Select the right software, right platform (cloud/on premise) , the relevant costs, associated with the implementation.
  • Assess, develop a strategy around data, our needs and skill assessment, timelines, training management.

MACHINE LEARNING

Machine learning (ML) is a sub-set of artificial intelligence (AI) and is generally understood as the ability of the system to make predictions or draw conclusions, based on the analysis of a large historical data set. At its most basic level, machine learning refers to any type of computer program that can “learn” by itself without having to be explicitly programmed by a human. The underlying idea has its origins decades ago – all the way to Alan Turing’s seminal 1950 paper “Computing Machinery and Intelligence”.

Essentially, ML ‘learns’ in the sense that the outcomes are not explicitly programmed in advance. An ML system is fed more data, it can improve its recognition of the patterns therein, and apply this improved recognition to new data sets that it may not have seen previously. Data scientists can program machine learning algorithms using a range of technologies and languages, including Java, Python, Scala, other others. They can also use pre-built machine learning frameworks to accelerate the process;

[image: ]The capabilities that machine learning offers could assist the work of professional accountants in various ways over time. One of the key drivers of this is the proliferation of data. Applications for adoption range across diverse areas, including for example, invoice coding, fraud detection, corporate reporting, taxation and working capital management. Online accounting software provider Xero (Kevin Fitzgerald, Asia Pacific Director for Xero) announced in May 2018 that its ML software had already made more than 1bn recommendations to customers since it became available, with areas of invoice coding and bank reconciliations being prominent.

Risk assessment, being the advantage of ML is the ability to assess the likelihood of fraud, inaccuracy, misstatement, based on a mix of empirical data and professional judgement. In this risk assessment, supervised learning algorithms can be used to help identify specific types or characteristics that warrant greater scrutiny; and improve targeting of the areas of focus for the audit.

ML is also being seen to have applications in relation to tax. It has a role to play in making tax query systems more effective. Using the ML technique of reinforced learning, AI chatbots and speech engines can train themselves to become more effective over time.

However, ML needs enough time to let the algorithms learn and develop enough to fulfill their purpose with a considerable amount of accuracy and relevancy. It also needs massive resources to function. Also machine learning is autonomous but highly susceptible to errors. Suppose we train an algorithm with data sets small enough to not be inclusive. We then end up with biased predictions coming from a biased training set to thus leading to irrelevant information being generated.

An example is Uber’s pricing system. Where 10 years ago this would have been hard-coded logic, a trained model now makes these decisions. It looks nothing like artificial general intelligence, but it performs a specific task to great accuracy. Viewed from the outside, the embedding of this AI software creates an increase in the operating effectiveness of the whole – a cost-saving development even if not a radical change.

LAWS AND REGULATION – ETHICS OF USE OF TECHNOLOGY IN FINANCE

In the wake of the financial crisis of 2008-2009, regulators worldwide, but particularly in the United States and Europe, have sought to increase accountability and foster ethical behaviour in their financial services industries. Environmental, social and corporate governance (ESG) issues have now become an essential part of non-financial reporting and of managing risk in today’s uncertain world.

Among the regulatory changes aimed at improving ethics are enhanced disclosure and reporting requirements. AI is the new subject of a wide-ranging debate in which there is a growing concern about its ethical and legal aspects. Ethics is specially needed when regulation is lacking. Law, however, is essential. Law implies a binding legal commitment, including for instance those ethical contents that are common and/or shared and therefore reach the statute of obligatory norms.

These include curbing speculative investment in commodity markets, addressing conflicts of interest in the provision of financial advice, and bringing greater transparency to algorithmic trading (known as high-frequency trading). Firms engaged in algorithmic trading, now must divulge their trading strategies among other elements.

The UNI Global Union based in Switzerland represents more than 20 million workers from over 150 countries in the fastest growing sectors in the world. This organisation adopts 10 principles for ethical AI:

  • AI Systems are transparent
  • Equip AI system with an Ethical Black Box
  • Make AI serve People and Planet.
  • Adopt a human-in-command approach
  • Ensure a Genderless, unbiased AI
  • Share the benefits of AI system
  • Secure a Just Transition and ensuring support for fundamental freedom and rights.
  • Establish global governance mechanisms
  • Ban the attribution of responsibility to robots
  • Ban AI arms race

On the other hand, the IEEE defends these principles:

  • Human Rights
  • Well-being
  • Data Agency
  • Effectiveness
  • Transparency
  • Accountability
  • Awareness of misuse
  • Competence

Other EU regulations relate more directly to customers, such as Anti-Money Laundering/ Know Your Customer requirements. The European Union’s General Data Protection Regulation, which aims to protect EU citizens’ data privacy wherever their data is stored, has considerable implications for how multinational financial services capture, store and transfer customer data.

Earlier this year, a high-level expert group on artificial intelligence (AI HLEG), set up by the European Commission, published guidelines for trustworthy AI, while similar principles were also adopted by the OECD's 36 member countries, along with Argentina, Brazil, Colombia, Costa Rica, Peru and Romania last month. Ethical issues are also being explored by the UK's newly created Centre for Data Ethics and Innovation and Office for AI. Organisations exploring the use of AI in financial services need the help of these global standards bodies to shape more detailed requirements on the use of AI that address the issue of ethics, as well as legal and regulatory compliance. Professional accountants need to consider, and appropriately manage, potential ethical compromises that may result from decision making by an algorithm.

REFERENCES

  1. Goh, C., Pan, G., Lee, B. and Yong, M. (2019). Charting the Future of Accountancy with AI. CPA Australia and Singapore Management University School of Accountancy.
  2. Mills, T. (2019). Council Post: Five Benefits Of Big Data Analytics And How Companies Can Get Started. [online] Forbes.com. Available at: https://www.forbes.com/sites/forbestechcouncil/2019/11/06/five-benefits-of-big-data-analytics-and-how-companies-can-get-started/#660f915817e4 [Accessed 25 Feb. 2020].
  3. Mohindru, R. and Kohli, P. (2019). Artificial Intelligence And Machine Learning: Industry Insights And Applications. [online] Infosys. Available at: https://www.infosys.com/Oracle/insights/Documents/ai-machine-learning.pdf
  4. Liu, S. (2019). Global Big Data market size 2011-2027 | Statista. [online] Statista. Available at: https://www.statista.com/statistics/254266/global-big-data-market-forecast/ [Accessed 1st Mar. 2020].
  5. Kühl, N., Goutier, M., Hirt, R. and Satzger, G. (2019) Machine Learning In Artificial Intelligence: Towards A Common Understanding. [online] Research Gate. Available at: https://www.researchgate.net/publication/327802544_Machine_Learning_in_Artificial_Intelligence_Towards_a_Common_Understanding.
  6. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, (2005) “The elements of statistical learning: data mining, inference and prediction,” Math. Intell., vol. 27, no. 2, pp. 83–85.
  7. Robles Carrillo, M. (2020). Artificial intelligence: From ethics to law. Telecommunications Policy, p.101937.
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