Artificial Intelligence: A Reality Check for Professional Accountancy

Think of a Candle - Its invention, gaining popularity and continuous development. But no matter how much it is developed it has become obsolete with the invention of electricity powered bulb. Like Electric bulb as Candle’s replacement same thing will be applied for Artificial Intelligence (AI), which is today’s buzzword that has been buzzing around more frequently with higher and ever-increasing intensity every passing day. Reason is as clear as a crystal; its power and possibilities it can create.

AI is nothing but the science of embedding cognitive abilities into machines. Simply said, it is to allow machines to undergo numerous numbers of experiences so that it would decide what works in what scenarios. This is not about coding into the system but allowing, rather enabling the system to code for itself.

Accountants have embraced waves of automation over many years to improve the efficiency and effectiveness of their work. But to date technology has not been able to replace the need for expert knowledge and decision-making. Although AI techniques such as machine learning are not new, and the pace of change is fast, widespread adoption in business and accounting is still in early stages. In order to build a positive vision of the future, we need to develop a deep understanding of how AI can solve accounting and business problems, the practical challenges and the skills accountants need to work alongside intelligent systems.

So, how will artificial and human intelligence work together?

We need to recognize the strengths and limits of this different form of intelligence, and build understanding of the best ways for humans and computers to work together.

HUMAN DECISION-MAKING

Humans make decisions in two different ways.

• INTUITION: Much of our thought process is instinctive and unconscious, taking place very quickly and with little effort. This type of thinking is rooted in recognizing patterns based on what has happened before, and is often described as intuitive.
• REASONING: We also use logic and reason in order to answer questions and make decisions. This conscious process uses our knowledge and typically takes over when intuition has not produced a satisfactory answer. This process takes time and effort.

Accountants, as expert decision makers, use both ways of thinking – they apply their knowledge to specific situations to make reasoned decisions, but also make quick intuitive decisions based on extensive experience in their field.

Though intuitive thinking is particularly powerful; it is not perfect. It is subject to many biases and inconsistencies such as:

? Availability bias – more recent or common examples tend to come to mind, which can skew our decision-making processes.

? Confirmation bias – we tend to see only things that are consistent with our existing views.

? Anchoring – we are strongly influenced by prior suggestions. 

STRENGTHS OF MACHINE LEARNING

1. Enormous Data Handling: They can process huge amounts of data (structured and unstructured) – much more than humans ever could; for example, the results of every piece of medical research carried out on a topic, or every piece of financial regulation. This provides a stronger and more powerful basis for learning.

2. Complex & Changing Patterns: They can pick up weaker or more complex patterns in data than we can. Therefore, machines may be better in environments that we find less predictable. Where feedback loops can be built into the models, they can also be highly adaptive and learn from errors or new cases.

3. Consistency: They can be far more consistent decision-makers. They do not suffer from tiredness or boredom. They also do not exhibit human biases and therefore provide opportunities to eliminate cognitive biases – such as availability or confirmation bias – as well as socially-based biases, such as racism.

LIMITS OF MACHINE LEARNING

• Data quantity and quality is fundamental, and not all problems have the right data to enable the machine to learn. Many models require substantial amounts of data.
• Data often reflects existing bias and prejudice in society. The model’s algorithm will only be as good as the data it trains on – commonly described as “garbage in, garbage out”. This means biased data will result in biased decisions.
• Furthermore, not every problem will be suitable for a machine learning approach. For example, there needs to be a degree of repeatability about the problem so that the model can generalize its learning and apply it to other cases. For unique or novel questions, the output may be far less useful.
• The outputs of machine learning models are predictions or suggestions based on mathematical calculations, and not all problems can be resolved in this way. Other considerations may need to be factored into decisions, such as ethical questions, or the problem may require deeper root cause analysis.

 The way accountants going to use AI capabilities

ACCOUNTING PROBLEMS

Accountants apply their technical knowledge about accounting and finance to help businesses and stakeholders make better decisions. To support their decision-making and advice, accountants need high quality financial and non-financial information and analysis. Technology can help them do this by solving three broad problems:

? providing better and cheaper data to support decision-making;

? generating new insights from the analysis of data; and

? freeing up time to focus on more valuable tasks such as decision-making, problem solving, advising, strategy development, relationship building and leadership.

? The very nature of machine learning techniques lend themselves to substantial improvements across all areas of accounting, and can equip accountants with powerful new capabilities, as well as automate many tasks and decisions. To date, there has been limited use in real-world accounting but early research and implementation projects include:

i) Using machine learning to code accounting entries and improve on the accuracy of rules-based approaches, enabling greater automation of processes;

ii) Improving fraud detection through more sophisticated, machine learning models of ‘normal’ activities and better prediction of fraudulent activities;

iii) Using machine learning-based predictive models to forecast revenues; and

iv) Improving access to, and analysis of, unstructured data, such as contracts and emails, through deep learning models. 

ROLES AND SKILLS

Accounting roles are already changing in response to new capabilities in data analytics. Indeed, accountants are well placed to work effectively with data analytics, as they combine high levels of numeracy with strong business awareness.

a) Some roles will continue to emphasize technical accounting expertise and human judgments to deal with difficult and novel cases. Other roles may expand to increase collaboration and partnering with other parts of the organization to help them derive the right meaning from data and models.

b) There will also be new jobs. For example, accountants will need to be involved in training or testing models, or auditing algorithms. They may need to get involved in projects to help frame the problems and integrate results into business processes.

c) Some accountants may be more directly involved in managing the inputs or outputs, such as exception-handling or preparing data.

d) This evolution will be reflected in the skills required of accountants. Some roles, such as training models, may require deep knowledge of machine learning techniques.

e) In other areas, accountants may just need a more superficial knowledge of machine learning to be able to have informed conversations with experts and other parts of the business.

f) Critical thinking and communication skills are likely to become increasingly important.

g) In addition to skills, accountants may need to adopt new ways of thinking and acting in order to make the most of machine learning tools. For example, spending more time on predictive and proactive activity – eg, putting predictions in context, or building capabilities to change course quickly – will need different ways of thinking.

So how accountants will embrace AI?

Embracing will also be ultimately driven by the economics and business case around AI. This will reflect two different ways that organizations will adopt machine learning capabilities.

First, machine learning is increasingly becoming integrated into business and accounting software. As a result, many accountants will encounter machine learning without realizing it, similar to how we use these capabilities in our online searching or shopping activities. This is how smaller organizations in particular are most likely to adopt AI tools.

Second, conscious adoption of AI capabilities to solve specific business or accounting problems will often require substantial investment. While there is a lot of free and open source software in this area, the use of established software suppliers may be required for legal or regulatory reasons. Given the data volumes involved, substantial hardware and processing power may be needed, even if it is accessed on a cloud basis. As a result, AI investments will likely focus on areas that will have the biggest financial impact, especially cost reduction opportunities, or those that are crucial for competitive positioning or customer service.

So the Bottom Line:

In the final analysis, AI is not a threat to the accountancy profession. It is not a threat to those accountants who embrace not only technology, but embrace change. With the proper perspective and an innovative mindset, AI is beginning to present a world of opportunity to a profession that has successfully evolved since Luca Pacioli "The Father of Accounting and Bookkeeping" invented double entry book-keeping in 1494.

P.S: First time reading my post? Thanks for taking the steps to stop by! If you enjoy this article, you may also like:

• Facing AUDITOR - it's easy never before!!!???
• Food for thought for Board members: Non-Current Assets Management
• Forensic Accounting - An area less cherished in eastern world.
• Golden rule of business - Make profit while procuring!!!
• Fear Factor: Tax audit. Would you like to overcome?
• Cost cutting: It's easy never before!!