Artificial intelligence (AI) has been seen as a double-edged sword for virtually all industries. This includes the financial services industry, prompting the Monetary Authority of Singapore (MAS) to develop guidance, governing responsible use of AI and data  Analytics   Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Read this Term.

AI remains a divisive force given its limitless potential and departure from traditional mechanisms or the status quo. While many see this technology as the future, others see cause for concern. Given the potential for ethics breaches or any future abuse, MAS is working with key industry stakeholders to roll out a guide to help clarify the use of AI.

Clarification needed

A more unified guide will look to police and lay out both the responsible and ethical use of AI and data analytics by financial institutions. This technology already made its mark on the financial industry, though newer variations of AI could dramatically reshape the industry as we know it in a blink of an eye.

MAS is targeting the release of its comprehensive guide for the end of the year, which will eventually grow to cover all segments of the financial industry, including fintech. Such a roadmap would be instrumental for many venues, given the lack of clarity surrounding this area and the deployment of such quickly advancing technologies.

First and foremost, the guide will look to set out and establish key principles and best practices for the use of AI and data analytics. Big data is a huge area of research and development for venues, with advances helping govern algo trading strategies and new techniques. Given the extent to which venues are leaning on this technology, the need to lay out unified principles is paramount.

Curbing potential abuse

David Hardoon, Chief Data Officer of MAS and co-chair of the upcoming committee, commented, “AI and data analytics have huge potential to transform the financial industry for the better. But these technologies can also potentially be misused. MAS looks forward to working with the industry to encourage innovative uses of these technologies while putting in place the right conditions for their ethical use based on the principles of fairness, accountability, and good governance.”

The guide will, therefore, be key in helping strengthen internal governance while also looking to reduce risks of data misuse. Newer technology of this nature, especially ones with such potential have always raised concerns amongst regulators, with MAS being no exception.  Blockchain  Blockchain Blockchain comprises a digital network of blocks with a comprehensive ledger of transactions made in a cryptocurrency such as Bitcoin or other altcoins.One of the signature features of blockchain is that it is maintained across more than one computer. The ledger can be public or private (permissioned.) In this sense, blockchain is immune to the manipulation of data making it not only open but verifiable. Because a blockchain is stored across a network of computers, it is very difficult to tamper with. The Evolution of BlockchainBlockchain was originally invented by an individual or group of people under the name of Satoshi Nakamoto in 2008. The purpose of blockchain was originally to serve as the public transaction ledger of Bitcoin, the world’s first cryptocurrency.In particular, bundles of transaction data, called “blocks”, are added to the ledger in a chronological fashion, forming a “chain.” These blocks include things like date, time, dollar amount, and (in some cases) the public addresses of the sender and the receiver.The computers responsible for upholding a blockchain network are called “nodes.” These nodes carry out the duties necessary to confirm the transactions and add them to the ledger. In exchange for their work, the nodes receive rewards in the form of crypto tokens.By storing data via a peer-to-peer network (P2P), blockchain controls for a wide range of risks that are traditionally inherent with data being held centrally.Of note, P2P blockchain networks lack centralized points of vulnerability. Consequently, hackers cannot exploit these networks via normalized means nor does the network possess a central failure point.In order to hack or alter a blockchain’s ledger, more than half of the nodes must be compromised. Looking ahead, blockchain technology is an area of extensive research across multiple industries, including financial services and payments, among others. Blockchain comprises a digital network of blocks with a comprehensive ledger of transactions made in a cryptocurrency such as Bitcoin or other altcoins.One of the signature features of blockchain is that it is maintained across more than one computer. The ledger can be public or private (permissioned.) In this sense, blockchain is immune to the manipulation of data making it not only open but verifiable. Because a blockchain is stored across a network of computers, it is very difficult to tamper with. The Evolution of BlockchainBlockchain was originally invented by an individual or group of people under the name of Satoshi Nakamoto in 2008. The purpose of blockchain was originally to serve as the public transaction ledger of Bitcoin, the world’s first cryptocurrency.In particular, bundles of transaction data, called “blocks”, are added to the ledger in a chronological fashion, forming a “chain.” These blocks include things like date, time, dollar amount, and (in some cases) the public addresses of the sender and the receiver.The computers responsible for upholding a blockchain network are called “nodes.” These nodes carry out the duties necessary to confirm the transactions and add them to the ledger. In exchange for their work, the nodes receive rewards in the form of crypto tokens.By storing data via a peer-to-peer network (P2P), blockchain controls for a wide range of risks that are traditionally inherent with data being held centrally.Of note, P2P blockchain networks lack centralized points of vulnerability. Consequently, hackers cannot exploit these networks via normalized means nor does the network possess a central failure point.In order to hack or alter a blockchain’s ledger, more than half of the nodes must be compromised. Looking ahead, blockchain technology is an area of extensive research across multiple industries, including financial services and payments, among others. Read this Term technology’s slow, if not gradual adoption across the financial sector is testament to this trend.

Subsequently, MAS brought together a group of thought leaders and practitioners across data analytics in the financial sector to develop the guide. The full list of members of the Fairness, Ethics, Accountability, and Transparency (FEAT) Committee includes the following individuals:

• David Hardoon (Co-Chair), Chief Data Officer, MAS
• Hsieh Fu Hua (Co-Chair), Co-Founder and Advisor, PrimePartners
• V K Rajah (Special Advisor), Senior Counsel and Member of Singapore Group Practice
• Teo Swee Lian (Advisor), Non-executive Independent Director, SingTel
• Raymond Au, Chief Data Scientist and Head of Asia Lab, Allianz
• Paul Cobban, Chief Data & Transformation Officer, DBS
• Shameek Kundu, Chief Data Officer, Standard Chartered Bank
• Richard Lowe, Chief Data Officer, UOB
• Donald MacDonald, Head of Group Customer Analytics & Decisioning, OCBC Bank
• Kelvin Tan, Head of FinTech & Data, SGX

To help accomplish this feat, MAS over the next few months, will be engaging representatives and officials from across the industry to obtain views and feedback on the proposed guide in Q2 2018. Additionally, MAS is also working with the Infocomm Media Development Authority to coordinate a more concrete understanding of AI governance across all sectors.

“The guide would be very useful for the financial industry. As the industry increasingly adopts AI and data analytics to serve their customers, they must also play their part to ensure that these technologies are used in a responsible manner,” explained Hsieh Fu Hua, former chairman of UOB group, and co-chair of the FEAT committee.