The Australian Securities and Investments Commission (ASIC) revealed its corporate plans on Monday for the next four years, between 2022 and 2026, highlighting its priorities around the design and distribution of products and technology risks.
Additionally, the Aussie regulator is going to focus on sustainable finance by “proactive supervision and enforcement” of regulatory standards and on retirement decision-making, which is to protect customers investing in superannuation products.
“The plan identifies work we have underway to address a number of emerging trends and important law reforms that are reshaping the financial system, including digitally enabled misconduct, emerging technologies, climate risks and an aging population,” ASIC’s Chair, Joe Longo said.
Moreover, the regulator will supervise the Australian Securities Exchange (ASX) in the replacement of CHESS with a 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-based solution.
On top of that, ASIC will be monitoring the compliance of two ASX subsidiaries, ASX Clear and ASX Settlement, with additional license conditions that were imposed in November 2021, a year after an outage on the stock trading platform.
“We will also analyze and assess the ASX operating rules changes necessary for the CHESS replacement, and provide ASIC’s advice to the Minister’s delegate as to whether to disallow all or a part of the changes,” the 31-page long official document stated.
Reporting Changes
The Australian market supervisor is going to align the over-the-counter (OTC) derivatives trade reporting requirements with international requirements over the next two years. It is further going to enhance data aggregation and surveillance capabilities, and effective information sharing.
For enhancing market supervision, the regulator will review artificial intelligence and machine learning
Machine Learning
Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Read this Term practices, risks and controls among market intermediaries. Furthermore, it will bring rules and guidance for automated order processing.
“ASIC is looking to the longer term and planning over the next four years. But, we’ve seen that scenarios can change quickly. We remain alert to changes and developments in our operating and regulatory environment, and we will continue to make rapid, strategic decisions to adapt where needed. When we do so, it will be transparent,” Longo added.
The Australian Securities and Investments Commission (ASIC) revealed its corporate plans on Monday for the next four years, between 2022 and 2026, highlighting its priorities around the design and distribution of products and technology risks.
Additionally, the Aussie regulator is going to focus on sustainable finance by “proactive supervision and enforcement” of regulatory standards and on retirement decision-making, which is to protect customers investing in superannuation products.
“The plan identifies work we have underway to address a number of emerging trends and important law reforms that are reshaping the financial system, including digitally enabled misconduct, emerging technologies, climate risks and an aging population,” ASIC’s Chair, Joe Longo said.
Supervision and Monitoring on ASX
Moreover, the regulator will supervise the Australian Securities Exchange (ASX) in the replacement of CHESS with a 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-based solution.
On top of that, ASIC will be monitoring the compliance of two ASX subsidiaries, ASX Clear and ASX Settlement, with additional license conditions that were imposed in November 2021, a year after an outage on the stock trading platform.
“We will also analyze and assess the ASX operating rules changes necessary for the CHESS replacement, and provide ASIC’s advice to the Minister’s delegate as to whether to disallow all or a part of the changes,” the 31-page long official document stated.
Reporting Changes
The Australian market supervisor is going to align the over-the-counter (OTC) derivatives trade reporting requirements with international requirements over the next two years. It is further going to enhance data aggregation and surveillance capabilities, and effective information sharing.
For enhancing market supervision, the regulator will review artificial intelligence and machine learning
Machine Learning
Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.This includes examples and indirect experience or instruction to help detect patterns in data. In doing so, the goal is to make better decisions in the future based on the examples that are provided. In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.Machine learning can take two different form, i.e. supervised or unsupervised learning. Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. As such, the system is able to provide targets for any new input after sufficient levels of training. Learning algorithm can also compare its output to find errors in order to modify the model accordingly.By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Read this Term practices, risks and controls among market intermediaries. Furthermore, it will bring rules and guidance for automated order processing.
“ASIC is looking to the longer term and planning over the next four years. But, we’ve seen that scenarios can change quickly. We remain alert to changes and developments in our operating and regulatory environment, and we will continue to make rapid, strategic decisions to adapt where needed. When we do so, it will be transparent,” Longo added.
