The machine learning challenge: Why does it matter to banks in Singapore?

Understanding the intricacies of global best practices and adapting to it will set machine learning (ML) on a successful course; highly skilled accountants are vital to that process – including those in the banking industry.

According to a Bloomberg Intelligence analyst, Singapore banks in general are likely to lead their Southeast Asia peers in the adoption of new technology, having spent $1.42b last year – about three times more than Thailand’s four largest banks. This can be largely attributed to Singapore’s supportive regulatory infrastructure and its status as a technology and business hub for institutions with operations in ASEAN.

In the near future, the functions and operations of banks will be redefined with the adoption of ML – processes, products as well as customer and employee experiences will be reimagined. ACCA’s recent report ‘Machine learning: more science than fiction’ took a look at not just what ML actually is and how it operates, but it also examines the ethical considerations, taking guidance from the fundamental principles established by the International Ethics Standard Board for Accountants (IESBA).

Ethical behaviour and accountability underpin the standard of behaviour expected of a professional accountant and within any organisation, it is the key requirement of the finance function to provide tough yet constructive challenges to ensure business decisions are grounded on sound ethical principles.

In seeing ML’s potential, professional accountants in financial services organisations need to think of the potential benefits as well as the long-term sustainable advantages and ethical considerations.

Last year, the Personal Data Protection Commission of Singapore published a proposed model for a governance framework that articulates a set of ethical principles for AI. Named the “Model AI Governance Framework (“Model Framework”), it provides guidance on key issues to be considered and measures that can be implemented in organisations. The guiding principles expect decisions based on AI to be explainable, transparent and fair; and that AI solutions be human-centric.

To be fair, any bias must be identified and rectified. Dealing with bias is a frequently discussed source of ethical challenge. ML algorithms, both supervised and unsupervised, need to be fully interpreted in order to avoid issues such as confusing correlation with causation. For professional accountants, objectivity may be compromised and they have to consider whether they have been biased in favour of assuming the outcomes are valid merely because they are supported by an ML algorithm. Consequently, professional scepticism and a mindset willing to take on new challenges are essential to avoid being afraid to dig deeper if they are pressured to ignore the issue.

The most important and non-negotiable requirement for powering the use of ML is data, and organisations must develop a coherent data strategy as meaningful insight with a low likelihood of bias depends on having enough data across all categories.

Banking providers are the custodians of vast amount of data and it is this richness of data which enables the algorithm to identify relationships which might not be obvious to a human.

As with many important issues, Singapore’s founding Prime Minister, Mr. Lee Kuan Yew, was far ahead of his time in identifying powerful trends and their impact on the world. He knew that there was a great risk of science and technology turning into Frankenstein’s monster and that these must be developed and harnessed without losing touch with people’s core, ethical values.

“People must stay abreast of the state-of-the-art technology, but must never lose their core values. Science and technology are decisive in determining future progress. But they should not be allowed to break up families that have to imbue children with a strong sense of social responsibility and the conscience to distinguish between right and wrong.”

What matters here is professional competence and due care in recognising the potential for breaches. Accountants must understand the increasing risks to data, the regulatory requirements and the financial implications of breaching these. On a practical level, this could mean instances such as the ability to identify and assess the controls in place around a data lake or background checks on employees contracted into the team. From a risk management standpoint, they may also need to quantify the impact of a data breach on the business and its reputation.

In a complex landscape, a business would better confront ethical questions with the help of philosophy. Singaporeans should not be afraid to make deeper, value judgements. The debate about economics should go beyond technicalities of GDP growth and unemployment, and into deeper considerations of values like freedom, equality, justice, and the nature of society.

An important principle for ethically sound ML solutions is that they should be designed in a way that does not alter the pattern of accountability established by society, culture and law. At the heart of this, who takes responsibility for the consequences of decisions made – the human professional accountant or the algorithm?

How this is dealt with will be a key focus of the years ahead. The risk is that technology will take the credit when things go well, and humans will take the blame when things go wrong – a no-win situation for those trying to work with technology. Both the user and the developer of the application are accountable when using technology.

To ensure that things go right, accountants must consider possibility of oversight and review the algorithms’ decisions.