A LEVEL INFORMATION TECH 9626 CHAPTER 12 .pdf

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IT in Society

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12 Electronic currency
An electronic currency is a payment method that occurs in digital form. It does not
involve the exchange of physical currency, such as coins and bank notes. There are
several types of electronic currency.
These include:
• Digital currency
• Virtual currency
• Cryptocurrency
• Central bank digital base money
• Stored value cards.
These terms can sometimes be used interchangeably, but some information, for
clarity, is given for each.
The terms electronic currency and digital currency are often used interchangeably.

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12 Digital currency
Digital currency is any currency, money, or money-like asset that is primarily managed,
stored or exchanged on digital computer systems, especially over the internet.
Digital currency is money in an electronic form exchanged for goods and services
without the use of physical money such as paper bills or coins.
Technology is growing and evolving. As a result, digital currency is steadily replacing
physical money. Here's what you need to know about the types of digital currencies and
the advantages and disadvantages of digital currency.
As technology advances, so does digital currency. An early form of digital money was the
electronic exchange of cash between bank accounts or an electronic payment utilizing
credit. This still takes place (mostly by debit or credit card) with bank-to-bank
electronic wires, an online payment system, or the use of a smartphone that carries a
user's payment information

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12 Virtual currencies
Virtual currencies are a broad category of digital or electronic currencies that
exist only in electronic form and lack a physical counterpart like coins or
banknotes. These currencies are typically decentralized, relying on
cryptographic techniques to secure transactions and control the creation of
new units. Virtual currencies can be used for various purposes, including as a
medium of exchange, a unit of account, and a store of value. Here are some
key types of virtual currencies:

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12 Cryptocurrency
The first decentralized cryptocurrency (Bitcoin) created by Developer Satoshi Nakamoto in 1990
that used cryptography hash function.
Many people use bitcoin to buy and sell products over the internet. Some support the use of a
decentralized currency, as it allows people, especially businesses, to make payments that do not
involve the usual charges that are applied by banks in a centralized system.
The ‘crypto’ in ‘cryptocurrency ‘refers to the fact that many encryption algorithms and
cryptographic techniques are used to ensure security across the network. This level of security
also makes cryptocurrencies hard to counterfeit.
Many cryptocurrencies operate as blockchain-based decentralized systems without the need
for a trusted third-party such as a central bank, or credit card company. In this instance, peer-
to-peer transfers are facilitated through the use of private and public keys.
Bitcoin is undoubtedly the most well-known – and most widely used – blockchain-based
cryptocurrency. It’s also the most valuable, currently sitting at $3,821 per coin.

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12 Advantages of Virtual Currencies
1. Convenient
The major advantage of virtual currencies is convenience. Payments with virtual
currencies are fast and easy due to their network-based nature. The use of virtual
currencies is especially convenient in international transactions.
2. Decentralized
Additionally, decentralization also avoids intermediaries. It lowers transaction costs
and avoids the security failure of the central administrator.
3. Secure
All transactions are completed using encrypted methods, so data is sent securely

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4. Faster
he use of contactless payments can speed up payment for products and services.
5. Contactless Payments
The user doesn’t necessarily need to remember their PIN number each time, if they
are using contactless payments methods.
6. Prevention from Debt
The use of payment methods, such as stored value cards, can prevent a person from
getting into debt, as they can only spend the set amount that is stored on the card.

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12 Disadvantages of Virtual Currencies
1. Lacks comprehensive regulation
The regulations over virtual currencies are not comprehensive or systematic enough,
hindering their worldwide acceptance. Lacking supervision from a central
administrator, decentralized virtual currencies provide opportunities for illegal
transactions and money laundering.
2. Highly volatile
Out of the charge of a central bank, the value of a virtual currency is highly volatile.
Therefore, it is a less favorable tool to store value or medium of exchange. For
example, Bitcoin peaked at the end of 2017 at nearly $20,000 per unit. It later
dropped to around $3,000 per unit within one year.

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3. Potential security issues
Virtual currencies also raise security concerns. Despite
improving encryption techniques, the loss or leakage of
authentication information is still possible and can cause great
losses to virtual currency owners.

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Cryptocurrency
Centralization vs. Decentralization:
•Digital Currencies: Digital currencies are a broad category that
includes both centralized and decentralized forms of currency.
Centralized digital currencies are typically issued and regulated by
a central authority, such as a government or central bank.
•Cryptocurrencies: Cryptocurrencies, on the other hand, are a
specific type of digital currency that operates on decentralized
networks based on blockchain technology. They are not controlled
by any central authority, and transactions are verified by a
distributed network of participants.

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Cryptocurrency
Technology:
•Digital Currencies: Centralized digital currencies often rely on
traditional banking infrastructure and technologies. Transactions
are recorded in centralized databases maintained by the issuing
authority.
•Cryptocurrencies: Cryptocurrencies use blockchain technology,
which is a decentralized and distributed ledger. Transactions are
recorded in blocks, and each block is linked to the previous one,
forming a chain. This technology ensures transparency, security,
and decentralization.

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Cryptocurrency
Examples:
•Digital Currencies: Examples of digital currencies include
central bank digital currencies (CBDCs) and electronic money
issued by financial institutions. Examples include the digital
representations of traditional fiat currencies.
•Cryptocurrencies: Bitcoin, Ethereum, Ripple (XRP), and Litecoin
are examples of cryptocurrencies. Each operates on its own
blockchain and has unique features.

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Cryptocurrency
Control and Regulation:
•Digital Currencies: Centralized digital currencies are typically
subject to government regulations and are issued and controlled
by central authorities. They may be used to implement
monetary policies.
•Cryptocurrencies: Cryptocurrencies operate on decentralized
networks, making them less susceptible to government control.
They are often designed to be deflationary, with fixed supplies,
and operate independently of traditional financial systems.

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12 The Strengths and Weaknesses of Digital
Currency
Strengths:
1.Efficiency and Speed:
1. Strength: Digital currencies often enable faster and more
efficient transactions compared to traditional banking
systems. Transactions can be processed in real-time or
within a short time frame, particularly in the case of
decentralized cryptocurrencies.

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Currency
Cost Savings:
•Strength: Digital currencies can reduce transaction
costs associated with traditional banking and financial
services. This is particularly true for cross-border
transactions, where fees can be significant.

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Currency
Financial Inclusion:
•Strength: Digital currencies have the potential to provide
financial services to individuals who are unbanked or
underbanked. They can access financial tools and services
without the need for a traditional bank account.
•The terms "unbanked" and "underbanked" refer to individuals
who have limited or no access to traditional banking services

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Currency
Accessibility:
•Strength: Digital currencies can be accessed and used
by individuals with an internet connection, providing
financial services to people in remote or underserved
areas.

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Transparency and Security:
•Strength: Blockchain technology, which underlies many digital
currencies, provides a transparent and secure way to record
transactions. It reduces the risk of fraud and enhances the
integrity of financial records.

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Volatility:
•Weakness: Many cryptocurrencies are known for their price
volatility. While this volatility can present investment
opportunities, it also poses risks for users and businesses,
especially in terms of value stability.

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Regulatory Uncertainty:
•Weakness: Digital currencies often face regulatory
uncertainties and evolving legal frameworks. Governments and
regulatory bodies worldwide are still working to establish clear
regulations, which can create challenges for businesses and
users.

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Security Concerns:
•Weakness: While blockchain itself is secure, the platforms and
wallets used to store digital currencies may be vulnerable to
hacking or cyber attacks. Security breaches can result in the loss
of funds.

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Lack of Consumer Understanding:
•Weakness: Many people still lack a clear
understanding of digital currencies, how they work,
and their potential benefits and risks. This lack of
understanding can hinder widespread adoption.

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Scalability Issues:
Weakness: Some digital currencies face scalability
challenges, especially during periods of high
transaction volume. This can result in slower
processing times and higher transaction fees.

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Irreversibility of Transactions:
•Weakness: Once a digital currency transaction is confirmed, it is usually irreversible.
While this feature enhances security, it can be a disadvantage in situations where
users need to dispute or reverse transactions due to fraud or error.

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12 Central bank digital base money
Central bank digital base money (also known as central bank digital currency (CBDC))
is the digital form of what is known as fiat money.
Fiat money is the currency that is the physical currency used in a country, that is
issued by governments and banks.
It is the bank notes and coins that you can use in payments every day. Creating digital
fiat money in this way would mean that banks effectively create a digital currency.
Each unit of currency in CBDC will act as an equivalent of a paper bank note. It will be
similar to bank notes, in that it will have a serial number. There will also be
distinguishable characteristics on the unit that will prevent fake units being created.
Banks intend to use it as part of the controlled supply of currency by the central bank,
and it will work alongside existing currency such as physical bank notes and coins.

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12 Data mining
Data mining is the process of analyzing massive volumes of data to
discover business intelligence that can help companies solve problems, mitigate
risks, and seize new opportunities.
This branch of data science derives its name from the similarities between the
process of searching through large datasets for valuable information and the process
of mining a mountain for precious metals, stones, and ore. Both processes require
sifting through tremendous amounts of raw material to find hidden value.
Data mining can answer business questions that were traditionally impossible to
answer because they were too time-consuming to resolve manually. Using powerful
computers and algorithms to execute a range of statistical techniques that analyze
data in different ways, users can identify patterns, trends, and relationships they
might otherwise miss.

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Data mining is used in many areas of business and research, including sales and
marketing, product development, healthcare, and education.
When used correctly,
Data mining can give you an advantage over competitors by making it possible to
learn more about customers
Develop effective marketing strategies, increase revenue, and decrease costs.at
delivers timely, reliable results, you should follow a structured, repeatable approach.
Ideally, that process will include the following six steps:

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Develop a thorough understanding of the project parameters, including the current
business situation, the primary business objective of the project, and the criteria for
success.

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Determine the data that will be needed to solve the problem and gather it from all
available sources.

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Get the data ready for analysis. This includes ensuring that the data is in the
appropriate format to answer the business question, and fixing any data quality
problems such as missing or duplicate data.

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Use algorithms to identify patterns within the data and apply those patterns to a
predictive model.

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Determine whether and how well the results delivered by a given model will help
achieve the business goal. There is often an iterative phase in which the algorithm is
fine-tuned in order to achieve the best result.

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Run the analysis and make the results of the project available to decision makers.

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12 Advantages of data mining
It helps gather reliable information –
Data mining allows companies, organizations, and governments to gather reliable
information. It can be used in marketing research to determine what products
customers might be interested in and then make those products available to them.
Data mining also helps organizations evaluate their own policies and procedures for
effectiveness.

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Helps businesses make operational adjustments –
Data mining helps businesses make profitable production and operational
adjustments. Data mining can be used to find correlations between products,
consumers, suppliers and other aspects of the business. This can help a company
identify trends that might not have been identified before, or at least help them
make more accurate predictions. If a company finds out that it is selling less of a
product than expected, they might learn what caused this and adjust their production
to increase productivity.

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The data mining process also works in reverse – if a company understands who their
customers are now, they will be able to create marketing campaigns specifically
targeting these groups in order to grow sales over time.

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Helps to make informed decisions –
It is often used for business purposes to improve decision making. As more data is
collected, the accuracy of data mining becomes greater. This technique can provide
insights that would be difficult or impossible to find just from reviewing records or
other sources. For example, it can help to identify various types of customers and
their purchase behaviour.

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It helps detect risks and fraud –
Data mining can help identify risks and fraud that may not be detectable through
traditional means of data analysis. It can find patterns in data that are otherwise
difficult to uncover, especially when the data is not organized in a way that makes it
easy to know what type of information to look for. One popular technique is
association rule mining, which finds any relationships between variables in the
dataset. This can lead to insight about the types of risks that are present and how to
mitigate them in the future.

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Helps to analyse very large quantities of data quickly –
Data mining can be used to analyse data that was previously too difficult to
understand due to the sheer volume or type of information. Moreover, it is an
important part of the modern world and most companies use it on a regular basis
because it helps them to make more informed decisions about marketing and other
business activities.
Helps to understand behaviours, trends and discover hidden patterns – Data mining
can be used to find patterns and trends in user behaviour. It does this by looking for
anything that is repeated in the data, such as instances of buying specific items. This
information can then be used to understand trends, discover hidden patterns, and
propose strategies for businesses to try.

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12 Application of Data Mining
You can use data mining to solve almost any business problem that involves data,
including:
• Increasing revenue
• Understanding customer segments and preferences
• Acquiring new customers
• Improving cross-selling and up-selling
• Retaining customers and increasing loyalty
• Increasing ROI from marketing campaigns
• Detecting and preventing fraud
• Identifying credit risks
• Monitoring operational performance

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12 Data processes
Data cleansing and preparation.
Raw data flows in from any number of sources in a wild mix of formats and quality.
Before it can be used in any meaningful way, that data must be transformed from its
raw state into a format that’s more suitable for analysis and processing.
This includes processes such as identifying and removing errors, calling out missing
data, and flagging outliers.

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Data warehousing.
Unless you are working with only a small subset of data, you will probably need to
collect data from a range of sources combine it into a single data repository before
you can use data to make decisions. This repository is generally known as a data
warehouse. It is the foundational component of most large-scale data mining efforts.

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Data analytics.
Once your data has been cleaned and collected, you can start examining it for past
trends that could be applied to future decision-making. The process of evaluating
historical digital information to provide useful business intelligence is known as data
analytics.

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Predictive analytics.
Where data analytics looks to the past to identify trends, predictive analytics uses
that data to anticipate future outcomes. Predictive analytics relies on data modeling,
machine learning, and artificial intelligence to uncover patterns in big data.

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12 Computer science concepts
Next, you should be familiar with some common computer science terms that
describe how various programs and algorithms interact with the data to deliver
meaningful insights.
• Artificial intelligence (AI). With modern technology, automated systems can
perform analytical activities that used to be possible only by applying human
intelligence. These activities can include things like planning, learning, reasoning,
and problem solving. When it comes to data mining, this refers to using a
computer program to identify meaningful trends in the data.

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Machine learning (ML).
The earliest computers needed an explicit program to instruct them through any
process, step by step — but that assumes that the programmer is already aware of
every possible scenario that may arise.
More recently, programmers use statistical probabilities to write machine learning
algorithms that give computers the ability to “learn” and adapt without being
explicitly programmed.

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Natural language processing (NLP).
Many valuable data sources, such as social media, aren’t easily broken down into
simple fields.
Natural language processing is a feature of AI that gives a computer program the
ability to “read” and understand casual or unstructured data sources.

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Neural networks.
Sometimes a single machine learning algorithm isn’t powerful enough to do the job
alone. A neural network is a collection of algorithms that work together to solve more
complex problems, thinking more like a human brain. Just like a simple machine
learning algorithm, neural networks have the ability to learn and adapt.

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12 Data mining techniques
There are many techniques used by data mining technology to make sense of your
business data. Here are a few of the most common:
• Association rule learning. Also known as market basket analysis, association rule
learning looks for interesting relationships between variables in a dataset that
might not be immediately apparent, such as determining which products are
typically purchased together. This can be incredibly valuable for long-term
planning.

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Classification.
This technique sorts items in a dataset into different target categories or classes
based on common features. This allows the algorithm to neatly categorize even
complex data cases.

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Clustering.
To help users understand the natural groupings or structure within the data, you can
apply the process of partitioning a dataset into a set of meaningful sub-classes called
clusters. This process looks at all the objects in the dataset and groups them together
based on similarity to each other, rather than on predetermined features.

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Decision trees.
Another method for categorizing data is the decision tree. This method asks a series
of cascading questions to sort items in the dataset into relevant classes.

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Regression.
This technique is used to predict a range of numeric values, such as sales,
temperatures, or stock prices, based on a particular data set.

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