Imarticus Learning – Page 324 – Finance, Tech & Analytics Career Resources

Trading Derivatives: 3 Things to Keep in Mind

Posted on April 7, 2023April 4, 2024 by Imarticus Learning

Derivative trading involves buying and selling financial instruments that derive value from an underlying asset. These assets include foreign exchange, stocks, bonds and commodities, and are traded in derivatives.

The trade life cycle of a derivative typically involves the initial agreement between buyers and sellers, followed by the fulfilment of the contract at a specified date in the future.

Derivative trading involves using a variety of financial instruments, including options, futures and swaps, to bet on the price changes of the underlying assets. The objective is to produce profits by correctly forecasting the direction of price changes and successfully managing risks.

Let us look at the three most important points one should remember while trading in derivatives.

1. Risks involved

Market risk

It is the potential for losses brought on by fluctuations in the underlying asset’s value.

Since the value of a derivative is determined from the value of an underlying asset, this risk is significant in derivative trading.

Credit risk

It refers to the possibility that a counterparty will fail to meet its obligations, leaving the other party holding the bag.

Since derivative trading is frequently done on margin or with leverage, which raises the risk of losses in the event of a default, this risk is significant.

Liquidity risk

It refers to the chance of being unable to sell or get out of a position because neither buyers nor sellers are available.

Restricted liquidity in some products makes it challenging to close a position at a fair price when trading in the derivatives markets.

Operational risk

It is the potential for losses brought on by errors in operating procedures or system performance.

This risk is essential in derivative trading because it involves intricate systems and procedures requiring careful management and supervision to prevent losses.

Legal and regulatory risk

It refers to the possibility of financial losses brought on by modifications to the rules or legislation governing the trading of derivatives.

Regulations can change quickly and significantly influence the value of investments, making this risk particularly crucial.

Counterparty risk

It implies that the opposite party to trade won’t keep its end of the bargain.

This risk is significant because it includes intricate and frequently lengthy contracts that depend on mutual trust and confidence.

2. Importance of having a trading plan

Having a trading plan is crucial for success in trading derivatives. Here are a few reasons why:

Minimise emotions: A trading strategy reduces the influence of emotions, which can trigger irrational and impulsive behaviour, in trading decisions.
Provides clarity: A trading plan simplifies the steps to be taken in various market conditions, what to expect from the market and how to manage risk. It aids traders in making defensible decisions without being influenced by feelings or ideas.
Increases consistency: A trading plan enables traders to maintain consistency in their trading strategy, which is crucial for long-term success. It allows them to imitate their triumphs and prevents them from repeating the same mistakes.
Helps manage risk: Well-defined stop-loss levels and restrictions on the amount of cash that can be risked in each trade and articulating how to handle unforeseen events enable traders to control risk.
Improves discipline: By offering a precise framework for trading activity, a trading strategy aids traders in maintaining discipline.

It guarantees that individuals trade in an organised manner, preventing rash judgements and lowering the likelihood of losses.

3. Strategies for successful derivative trading

Derivative trading is a complex and high-risk activity that requires a thorough understanding of the underlying assets and markets.

Here are some strategies for successful derivative trading:

Educate yourself: It’s crucial to have a solid understanding of financial markets, asset classification and risk management before starting with derivative trading.

A thorough understanding of the fundamentals of derivatives, such as options, futures, swaps and their associated trading strategies, is necessary.

Develop a trading plan: An effective trading strategy for derivatives must be carefully constructed. Your objectives, risk tolerance, entry and exit methods, and the amount of capital you are willing to risk on each trade should be outlined in the goal.

Manage your risk: Trading derivatives are inherently dangerous; thus, controlling your risk is essential. It entails placing stop-loss orders, attentively watching your trades and getting ready to leave a position if the market turns against you.

Use technical analysis: It entails examining charts and other market data to spot trends and patterns. It can also unearth trading opportunities and lead to better trading decisions.

Conclusion

Derivative trading is a complex activity that requires knowledge, skill and discipline. For those interested in pursuing a career in investment banking, expertise in derivative trading is a valuable asset.

Imarticus Learning’s Certified Investment Banking Operations Professional course provides comprehensive training on the complexities of foreign exchange and derivatives markets, trade life cycles and risk management. This course will help individuals develop the skills necessary to succeed in the competitive world of investment banking.

Gamification in Onboarding: Boosting Engagement and Retention

Posted on April 7, 2023April 2, 2024 by Imarticus Learning

Introduction

Effective onboarding is critical for the success of any company. The onboarding process is often the first real interaction that new hires have with their employer. It sets the tone for their entire employment experience. As such, it’s important to make sure that your onboarding programme is engaging, informative and memorable.

Corporate Training

One way to achieve this is through the use of gamification. Gamification is a method of using game design and mechanics in non-game settings like learning and training. Gamifying onboarding can improve the engagement, motivation and retention of new hires. It can be done by using elements such as competition, rewards and scoreboards.

Many UAE companies are interested in gamifying the onboarding process. We have covered some tips and best practices to help you get started.

Benefits of gamification for onboarding

Research has shown that gamification can improve

learning outcomes,
increase engagement and
enhance motivation among employees.

Also, gamification can help to create a sense of community and teamwork among new hires.

Steps to effectively incorporate gamifying onboarding strategy

Setting clear objectives

You first need to identify clear objectives for the programme.

What do you want your new hires to learn?
What skills do you want them to develop?
What outcomes do you want to see because of the programme?

By answering these questions, you can design a gamified onboarding programme that aligns with your company’s goals and values.

Identifying the right gamification techniques

There are a variety of gamification techniques that you can use in your onboarding programme. Techniques like points, badges, leaderboards and quests can be effective. However, not all are suitable for every organisation. When selecting gamification techniques, consider factors such as your employees’:

demographics
learning styles
training needs

You should also ensure that the techniques you choose are aligned with your company’s culture and values.

Designing activities that align with your training goals

The next step is to design game-based activities that effectively teach new hires the skills and knowledge they need to succeed.

For example, you could create a scavenger hunt that takes new hires through various departments of your company to learn about distinct roles and responsibilities. Or, you could create a simulation that allows new hires to practice specific job-related tasks.

Incorporating feedback and rewards

Feedback and rewards are essential components of a successful gamification strategy. By providing feedback on new hires’ progress and rewarding them for their achievements, you can keep them engaged and motivated throughout the onboarding process.

You can provide feedback after each completed activity or offer badges and certificates for completing certain milestones.

Tracking progress and analysing data

To ensure that your gamification employee onboarding programme is effective, track progress and analyse data regularly. This allows data-driven decisions to improve the programme over time.

For example, you could use data to identify which activities are the most engaging for new hires. You may also evaluate the overall effectiveness of your feedback and reward systems.

Ensuring accessibility and inclusivity

When designing a gamified onboarding process, it’s important to ensure that it’s accessible and inclusive for all employees. This means creating activities and materials that are accessible to:

People with disabilities
People who speak languages other than English.

Different employees may have different learning styles or preferences. So, you should also consider providing multiple pathways for completing the onboarding process.

In conclusion

Incorporating gamification into your onboarding programme can increase engagement, motivation and retention among new hires. By following these best practices, UAE companies can create a memorable and effective onboarding experience for their employees.

Imarticus Learning offers gamified onboarding programmes designed to align with company objectives and values. We provide feedback and rewards to keep employees engaged and motivated throughout the process. This helps create a memorable onboarding experience. Connect with us today.

Logistic Regression vs. Linear Regression: Which is Better for Machine Learning?

Posted on April 6, 2023June 26, 2024 by Imarticus Learning

Logistic regression and linear regression are two integral algorithms of machine learning. These two are supervised learning methods that are used for making forecasts and predictions.

Although both regression techniques are used for the purpose of machine learning, they still have a lot of differences in the way they are used. A good data analytics course with placement can help one understand the distinctions in a simple way. When talking about logistic regression vs linear regression, we need to understand that logistic regression is mainly used to solve classification problems, whereas linear regression is used to solve regression problems.

Read on to learn more about logistic regression vs linear regression – the regression techniques of machine learning.

Logistic Regression

Logistic regression is one of the most used machine learning algorithms. It is a supervised technique that is generally used for classification problems but can also be used for regression problems. It uses simple independent variables to make predictions on categorical and dependent ones.

Logistic regression works well with Python programming, which requires minimal coding and does the job of solving classification problems. the output of this technique ranges between 0 to 1. It is based on the institution of maximum likelihood estimate, which means observing data should be the most obvious.

Linear Regression

Linear regression is an old and simple form of a machine learning algorithm. It is a supervised technique that is specifically used for solving regression problems. This technique uses independent variables to make predictions on the continuous dependent variables. It is also compatible with Python programming, much like its counterpart.

As the name suggests, a linear regression graph is always in the form of a straight line, and the main objective is to find the best-fit line for accurately denoting the variables. If only one independent variable is used, then it is called a simple linear regression, and if more than one independent variable is employed, then it is known as multiple linear regression.

Logistic Regression vs Linear Regression – The Differences

Both regression techniques are widely used for machine learning, yet each one addresses particular problems. However, both can be used for data visualisation, and there are certain tasks that each performs separately. The key differences in logistic regression vs linear regression can be explained as follows:

Type of variable and output

Logistic regression is predominantly used to specifically predict and deal with the categorically dependent variables. A particular set of independent factors is associated with this regression technique. This technique is mostly based on probabilities.

On the other hand, linear regression uses only continuous dependent variables based on a particular set of independent variables. These independent variables take numeric values and reference from staying in any category or group.

Degree of complexity

Logistic regression has a more complex structure of equations, that makes it challenging to interpret and understand this model.

Whereas linear regression is a simple model and involves a comparatively simple structure of equations. It is easily understandable and interpreted.

Application and scope

Logistic regression is mainly applied to solving classification problems. It can, however, solve some regression problems as well. It is slightly wider in scope.

On the other hand, linear regression is only applied to solving regression problems. It cannot solve classification problems. Hence, it is comparatively narrow in scope.

Type of graphical representation

In logistic regression, the graph forms an S-shaped curve. Thus, the type of graph is S-shaped. It can be easily used for classifying samples.

In contrast, the graph of linear regression is a straight-line curve. It focuses on finding the proper fit line for precisely predicting the output.

Method used

Logistic regression uses the maximum likelihood estimation method. In this method, the coefficients of the regression method are chosen to maximise the probability of the variables until the iteration processes are complete.

On the contrary, linear regression uses the ordinary least square estimation method. In this method, the coefficients of the recreation method are chosen to lower the sum of the squared variables to find the best-fit line.

Mathematical equation

The mathematical equation used in logistic regression is:

y(x) = e(a0 + a1x1 + a2x2 + … + aixi) / (1 + e(a0 + a1x1 + a2x2 + … + aixi))

On the other hand, the mathematical equation used in linear regression is:

y = a0 + a1x1 + a2x2 + … + aixi

Where,

y stands for the response variable

xi stands for the predictor variable

ai stands for the average effect on y as xi increases by one (when all other predictors are fixed)

Type of output

Logistic regression must always have a categorical or numeric output that ranges between 0 to 1 or is present in the form of a clear yes or no answer, and so on.

On the other hand, the output of linear regression must always be in the form of a continuous value. For instance price, weight, age, and so on.

Correlation between independent variables

In the case of logistic regression, there exists no correlation between the independent variables.

On the contrary, in the case of linear regression, there may or may not exist a correlation between the independent variables.

The Better Approach to Machine Learning

This is another debatable topic for many specialists. When it comes to supervised Machine Learning then linear regression is considered to be the best-suited approach. Linear regression is best suited for predicting continuous outcomes.

For instance, linear regression can be used for predicting the weight of an individual if his height is known.

The equation appears as follows:

Weight = 70 + 2 x (60) = 190 lbs

Where height= 60 inches

Here, the effect of one independent variable on the outcome is analysed.

However, when unsupervised or semi-supervised Machine Learning is considered then logistic regression is the most suitable approach. It works best with assumptive data and refrains from using a rigid approach. Logistic regression is best suited for producing a discrete value.

For instance, logistic regression can be used for predicting whether a student will be able to crack a competitive examination or not. Whether a political candidate will win the election in an area or not. This approach presents binary outcomes where it has only two straightforward alternatives.

Conclusion

Regression is an essential Machine Learning tool that makes predictions and creates relationships among variables. Machine learning experts are in demand in every organisation nowadays. If you are looking to gain professional knowledge about regression approaches and learn what is logistic regression vs linear regression then a data analytics course with placement can help you to get a good piece of work. Sign up for the Postgraduate Program In Data Science And Analytics by Imarticus and kickstart your career in data science.

Trade Life Cycle in Investment Banking

Posted on April 6, 2023February 6, 2026 by Imarticus Learning

The selling and buying of financial products and instruments are known as trade or deal. But, the process of completing a trade is not so simple. From the moment a trade is initiated through an order to when it is settled, the steps and stages involved in the process together form a trade life cycle. It is important for financial institutions like hedge funds, investment banks, pension funds, etc., to maintain their trade life cycle.

Considerable planning and subsequent follow-up are required to complete a trade. This article will attempt to break down the entire trade life cycle and give an account of each step. Read on to learn in detail.

Why is a trade life cycle important in investment banking?

Trades involving financial products are worth a lot. Hence, even a small mistake in the steps can cause either party to lose a lot of money. Inefficient management can nullify a trade. Thus, financial companies that trade frequently need to keep track of the trade life cycle.

If you are aiming for a career in investment banking, then apart from foreign exchange and derivatives markets, an in-depth understanding of the trade life cycle can give you the necessary professional edge to succeed.

Stages in a trade life cycle

Depending on different types of trades, there can be some different steps in the trade life cycle. It can also vary from institute to institute. However, when talking about the stages of a trade life cycle in the context of investment banking, the main stages involved are:

Pre-trade prep

In investment banking, the trade begins with preparing the deal. The institution ensures that the trade abides by local laws and that all the legal documents related to the case are in order. Evaluation of counterparty credit risk, management of risk and collateral collection are also part of the process.

Beginning and execution of the trade

After the preparation for the trade is done, both parties initiate the trade process. Following negotiations, the parties reach an understanding and the counterparty places an order with the institution which executes the trade. A trade confirmation is then sent to the client as proof of the deal.

Trade capture

The process of trade capture involves booking the trade into the various office systems of the institutions. It includes recording the trade in both the front, middle and back office systems. The main details usually recorded from the trade are price, quantity, underlying assets, the date and time of the trade, etc.

Trade validation and confirmation

The back office system evaluates the trade before the trade settlement is confirmed. The limits and risks of the valuation are also checked in this stage. More details are added to the records while the professionals give a final check to identify any lingering problems or loopholes in the records.

If two institutes are working for different clients during an investment process, then all the details are verified and confirmed by both parties.

Trade settlement

After every detail is confirmed and assessed by all the parties involved, they can finally move on to the settlement process. It involves the exchange of security and money; usually, the back office staff is in charge of overseeing a smooth and fair transaction.

Based on the method, there are two types of settlement:

- Delivery-versus-payment (DVP) – Cash and securities are exchanged simultaneously in this case.

Free-of-payment (FOP) – Here, securities and cash are delivered separately. Usually, in this case, the security is delivered before the payment is made. It is a risky affair since the counterparty may retract.

A trade settlement, thus, should be done within 1-3 days of the trade date. This minimises associated risks.

Termination

Sometimes trades are terminated or expire before maturity. The termination of a trade can be a long process.

Risk management

Risk management at the end of a trade life cycle is extremely crucial for both the client and the institute. Measuring the profits and losses of the transaction, assessing the counterparty’s credit risk, preparing reports and managing trade reconciliation are all part of risk management after trade settlement.

Conclusion

It is important for any finance professional to know the different stages of a trade life cycle and why managing it is so important. If you want to build a career in investment banking, then learning the ins and outs of the trade life cycle will prove valuable. Thankfully, the Imarticus Certified Investment Banking Operations Professionals programme or CIBOP, is there to help you build your investment banking career.

If you are a finance graduate with some work experience under your belt, then this course is perfect for you. You can avail yourself online live classes, experiential learning and career support. The course also comes with job assistance. So, boost your investment banking career with the Imarticus CIBOP course.

UAE – Gamifying onboarding: A fun and interactive way to welcome new hires

Posted on April 5, 2023 by Imarticus Learning

Introduction

Looking for a fun and interactive way to welcome new hires? Look no further. We got you.

Onboarding gamification is a new trend in the UAE corporate world. It aims to make the employee onboarding process more engaging and interactive.

Gamified onboarding is a fresh approach to introducing new employees to a company’s culture and values. It provides them with the necessary knowledge and skills to succeed in their roles.

Why gamified solutions for onboarding are gaining popularity

The traditional onboarding process can be boring and overwhelming for new employees. Research has shown that gamification in onboarding can lead to higher levels of engagement, retention and motivation among new hires. In fact, a study found that 90% of employees are more productive when engaged in some form of gamification at their workplace.

How gamification can transform the onboarding process

Gamification can transform the onboarding process in several ways:

1. Increased engagement

Gamification can make the onboarding process more engaging and enjoyable for new employees. Game elements such as challenges, rewards and leaderboards can be added to the process. It’s more likely for new hires to remain engaged and motivated throughout.

2. Improved knowledge retention

Gamification can help new hires retain information better. Quizzes, simulations and mini-games are examples of interactive elements. New employees can learn and practise new skills in a fun and engaging way.

3. Faster onboarding

Breaking down the onboarding process into smaller tasks can make it more manageable for new employees. Providing immediate feedback can help them progress through the process more quickly.

4. Increased socialisation

Gamification can help new employees feel more connected to their colleagues and the company. Team challenges and social learning can help new employees interact with colleagues and build relationships. This can increase their sense of belonging in the company.

5. Reduced costs of onboarding

Gamification provides an engaging and efficient method. Companies can reduce the time and resources required for onboarding, which can result in cost savings.

Elements of successful onboarding programs

1. Learning portals

These are web-based platforms that provide access to various resources such as training modules, videos, assessments and other learning materials.

2. Responsive solutions

Gamified onboarding programs should be designed to work on various devices such as smartphones, tablets, and desktops.

3. Theme-based learning approaches

Gamification is all about creating an engaging experience for employees, and a theme-based approach is a great way to achieve this. Themes can be based on company culture, industry-specific concepts or even popular culture references.

4. Immersive designs

Immersive designs are used to create an engaging and memorable experience for new employees. This can include the use of interactive videos, simulations and virtual reality experiences.

5. High-recall visual imagery

Using high-quality images and graphics can help employees retain information better.

6. Story-based onboarding

It is a way that involves telling a story to new employees that shows the company’s culture, values and mission. This helps to create an emotional connection with the new employees and helps them to understand the company’s goals and objectives better.

How to implement onboarding gamification in your company

To implement onboarding gamification successfully, companies should:

Ensure that the game mechanics align with their business objectives and employee needs.
Provide clear instructions and feedback to employees.
Create a sense of competition among employees.
Recognise and reward employees’ achievements.

Parting note

With the benefits of engagement, retention and cost savings, it is no wonder that more companies are turning to gamified solutions for onboarding. It is becoming a popular strategy as it can help employees learn company procedures and culture while also having fun.

Imarticus Learning Onboarding is one such platform that offers gamified solutions for onboarding. It provides a fun and interactive way to welcome new hires. Visit Imarticus today!

Financial Modelling in Decision-Making and Cash-Flow Forecasting

Posted on April 5, 2023April 4, 2024 by Imarticus Learning

Financial modelling is a crucial step in the process of financial statement preparation. Financial models refer to spreadsheets built in MS Excel, used by companies to forecast the financial performance of a business. It is based on the historical performance of the company as well as future expectations and assumptions. To efficiently carry out financial modelling, analysts first prepare income statements, balance sheets, cash flow statements, and supporting schedules. This is also commonly known as the 3-statement model.

As a budding expert in this field, having a detailed understanding of the concept of financial modelling is key. This article will provide you with a detailed understanding of financial modelling and how important it is in decision-making and cash-flow forecasting.

What is a financial model and what is it used for?

Financial models have many types and many use cases. Every financial model serves as an important part of economic decision-making and pinpointing KPIs that are important for business forecasting. All in all, it is an integral tool that helps perform financial analysis, both inside and outside the company. Financial models are used for making decisions concerning the following:-

Getting acquisitions (assets or business)
Raising capital (equity or debt)
Organic growth of the business (e.g., opening new stores, entering new markets, etc.)
Business forecasting and budgeting
Disposing or selling business units and assets
Allocating capital
Business valuing
Analysing financial statements or ratio
Accounting management

What is Financial Modeling Used for

Financial modelling is a combination of business, accounting and finance metrics to develop a forecast of the future results of a company. It aims to forecast the future financial performance of a company accurately. Financial experts and analysts use financial modelling for the following:-

To assess future finance-related events in the business and economic changes that affect the company’s performance and market value.
To make an estimate of the company’s valuation and performance with its market competitors. The cost required to invest in new projects is estimated and assessed to evaluate their profitability. This facilitates rational decision-making.
Financial models also help executives in raising capital via equity or debt, merging with or acquiring a company, diversification of business, investing in new projects, allocating the budget, and determining the business valuation.
Financial models can help project the existing and forecasted financial position a company is in.

Role of Financial Modelling in Decision-Making

Decisions can make or break a company. Be it marketing, financing or business-related, the role of decision-making paves the pathway to either success or downfall of an organisation. The financial model of a company consists of important KPIs all compiled in a spreadsheet for stakeholders and executives to review and make finance-related decisions.

Financial modelling compiles accounting, business and finance metrics into one sheet. This spreadsheet is crucial as it forecasts future development and results of the company and the future performance of a company. Decision makers therefore heavily bank on financial models to streamline financial decision-making. Financial modelling is further used for determining company value and whether they should consider raising capital or growing business via acquisitions or organically.

Some of the key present value decisions that bank on efficient financial modelling are:-

Linear programming decisions: Banks on the company’s background and its history of management techniques.

Business value decisions: Banks on analysing the cost of capital, forecasting assumptions and Pro-forma financial statements.
Pivot tables decisions: Banks on data analytics.

Businesses also depend on the following past value decisions that can only be facilitated via efficient financial modelling:-

Financial dashboard decisions: Banks on common financial measures and dashboard principles.
Budget management decisions: Banks on the development and management of budgets and cash-flows.
Amortisation table decisions: Banks on corporate bonds, bank borrowing and PP&E (Property, Plant, and Equipment)

Role of Financial Modelling in Cash-flow Forecasting

Cash-flow forecasting is conducted to foresee the future financial position of a company to streamline financial management. It is a core planning tool that companies use in both decision-making and managing overall finances.

Cash flow forecasting helps project the company’s financial position in the market by taking into account the anticipated receivables and payments. A cash flow forecast is extracted from the income statement and the balance sheet. The Financial modelling involves forecasting cash flows from three types of activities:-

Operating activities: The operating activities involve operating costs and revenues extracted from financial statements. For every operating asset or liability, the forecast year is compared with the previous year.
Investing activities: Investing activities include purchasing and selling assets. All items related to investing activity are from PP&E (Property Plant & Equipment) forecasts and fixed assets. Fixed assets are forecasted based on the assumption that the assets are depreciated during disposal and there is no business-related activity (Purchase or sale) involved.
Financing activities: Financing activities include raising debt and issuing shares. Financing activity items are mostly calculated via a simple comparison of the projected year with the previous year. For instance, Dividend cash flows can fall under either financing or operating activities depending on what the organisation wants. However, the financial statements should reflect them with accuracy.

Monitoring cash flows should be prioritised in every organisation. Forecasted cash flow is further compared with actual cash flow for the present period. Accurate cash-flow forecasting needs:-

Efficient communication lines
Proper detection of inflows & outflows
Predicting several scenarios accurately
Comparing the results.

However, forecasting is a crucial element in financial modelling and not only requires financial strength but also a team of experts to overcome cash flow crises.

Conclusion

Financial forecasting is a key process in decision-making because it accurately projects how a company will fare in the future. It is a crucial tool that analysts use to facilitate appropriate decision-making for businesses and organisations at large. Financial modelling, therefore, is a highly valuable skill to have.

The most ideal way to become a financial modelling expert is through regular practice and staying updated with equity reports. There are numerous professions and career tracks for which hands-on experience in building financial models can come in handy. Some of them include investment banking, corporate development, equity research, accounting and FP&A. To make it big in the field of investment banking, private equity or corporate finance, you can sign up for the Financial Analysis Pro-degree offered by Imarticus Learning.

How is Blockchain Disrupting the Finance Sector

Posted on April 5, 2023April 2, 2024 by Imarticus Learning

Blockchain’s digital record-keeping system allows for the easy recording of transactions with the added advantage of security and resilience. But, why is blockchain technology in the news?

Blockchain has saturated how operational fees and security measures work. Jamie Dimon, CEO of JPMorgan Chase & Co., has expressed concern about how blockchain operates by simplifying asset acquisition.

With an increasing number of companies leaning on this technology, the future of the financial sector may be affected. Keeping these factors in mind, we have penned down a blog examining how blockchain disrupts the end.

How does blockchain work?

As a distributed ledger technology, blockchain stores data on multiple databases. It does so via a peer-to-peer network compared to a single central server.

The primary function of blockchain technology is to store the details of any transaction. Furthermore, the chain is not subject to change over time, making it impossible for further editing and altering.

Blockchain’s high-security functions are attributed to its distribution system. It implies there isn’t a single point of failure, making it tricky for hackers to hack into sensitive information and steal data.

Because of its distributed system, it is also more efficient than any known intermediaries like banks and other payment processors. Due to these reasons, blockchain technology has evolved to be a highly efficient and appealing technology for individuals and businesses alike.

Financial areas with chances of disruption

Introducing distributed ledger technology (DLT) has made it easier for blockchain technology to employ financial cooperation. In addition to being cooperative, it is expected to raise ledger standards and pave an easier way. A financial technology course can offer you more insights into blockchain and its usage with DLT.

Fundraising

Blockchain enables donors to know the whereabouts of their money and how it is being used, making the process transparent. This technology can also build an efficient fundraising model with reduced overhead costs. Initial coin offerings (ICOs) are looking to integrate a new model into the financial world compared to traditional donation processing.

Payments

The rise of Bitcoin and Ethereum has led to several decentralised ledgers for initiating payments. Majority of the European Payments Council members believed blockchain would revolutionise the industry by 2025. In terms of payments, blockchain technologies offer users a secure and instantaneous payment option. With reduced costs and enabling services such as micropayments, it facilitates better transactions and payments.

Clearance system/settlement system

As distributed ledgers utilise shared technology, operational costs decrease, making it easier to acquire faster transactions. These costs include both execution and reconciliation, which can be significantly simplified.

Finance and trading

A distributed ledger employed by blockchain can yield faster and better results than traditional trading methods. It will enable transparency, trust, and security measures in every transaction. The international trade processing costs are stipulated to save $17B to $24B by implementing a distributed ledger system.

Stocks and bonds

Blockchain can decentralise the process of creating stocks and bonds. It makes it easier to acquire securities with the latest technology at hand.

Credit system

Blockchain streamlines the traditional credit system and handling of loans, optimising the acquiring process. Due to its increased security measures, the operational costs associated with the technology are bound to be low. To sum it up, it makes acquiring loans a more accessible pursuit.

Know your customer (KYC) policy

Storing general customer information and sensitive data optimises decentralised assets. In turn, this paves the way for easier data sharing amongst stakeholders.

Security measures

Traditional financial and banking institutions are seeing a competitor in blockchain as it offers enhanced security measures. These include digital signatures, timestamping, and cryptographic encryption at the click of a button.

Compliance process

Blockchain has a way of optimising compliance processes by offering to audit customer activity and follow-up. Introducing KYC is also beneficial and makes for a better way to track customer verification.

Access to financial services

A distributed ledger can offer financial services in developing countries instead of traditional banking operatives. This financial inclusion can aid developing countries in creating more efficient systems accessible even from remote locations. It can even increase transparency in terms of land records and government services.

Conclusion

As distributed ledgers and blockchain technology gear up to enter a world of faster and more secure transactions, the global financial sectors are looking out for fierce competition. To understand more about the financial industry’s transformation with increasing technological advancements, opt for the SP Jain Fintech certification, which is brought to you by Imarticus.

The certification includes a comprehensive overview of the fundamentals of fintech, including proven business models and legal and regulatory frameworks needed for financial development.

It is ideal for professionals who wish to have a good idea of the industry to excel in a competitive workplace. Additionally, you can use it to create a competitive advantage. Enrol now and be a part of the future of finance!

A Comprehensive Guide to Becoming an Ethical Hacker

Posted on April 5, 2023April 2, 2024 by Imarticus Learning

The term ‘hacker’ was once used to describe professionals who redeveloped mainframe systems, boosting their productivity and enabling multitasking. However, the term has recently come to refer to programmers who gain unauthorised access to a computer system with malicious intentions. A hacker uses his/her skills to break networks and impair the system causing problems in functioning and data theft. Ethical hackers have come into the picture to protect computer systems from such malicious activities.

Theft of priceless information or monetary gain is the main motivation behind harmful or unethical hacking. With businesses and governments increasingly resorting to cloud storage to store data, cloud security is of utmost importance, amongst other things. This is where ethical hackers come in. This is the right time to build a career in ethical hacking, with lucrative job opportunities proliferating.

Read on to learn more about how to become an ethical hacker.

What is ethical hacking?

Ethical hacking is the legitimate practice that helps to find flaws in any application, computer system or organisation’s infrastructure that goes beyond the security of the system. It uncovers potential data breaches and network risks. Ethical hacking helps to find vulnerabilities that can be exploited by black hats.

Ethical hackers enhance the system security of a company and deflect any attacks by black hats. This is a planned and approved process which is absolutely legal as opposed to malicious hacking. Ethical hackers evaluate the system or network to find any loopholes that can be exploited by hackers.

Roles and responsibilities of an ethical hacker

Companies employ ethical or white hat hackers to identify weaknesses in systems and create defences against them. Black hat hackers could steal the data and destroy the files, resulting in serious financial loss for the company. Following are the roles and responsibilities of an ethical hacker in an organisation:

Performing multiple system penetration tests to constantly check the security system
Locating and noting down any security weaknesses and violations
Conducting regular meetings with the company’s security department to discuss the state of the current security system
Identifying the locations that require high security and focusing more on such areas
Conducting regular evaluation and assessment tests of the security system
Coming up with new ideas for strengthening system and network security
Assembling the penetration test reports for presenting them to clients
Implementing solutions to avoid harming the integrity of a company
Suggesting system security upgrades to protect data theft and breaches
Removing all of the breaches following a system vulnerability check

Benefits of ethical hacking

Ethical hackers are in demand as they are indispensable in organisations today. The following are the key advantages of ethical hacking:

Ethical hackers play a very important role in the fight against national security breaches and cyber terrorism.
It acts as a wall of protection against potential hacker assaults.
Ethical hackers build a system that is secure against any sort of hacker inclusions.
It also helps to protect crucial information in the banking and finance sectors.
Ethical hackers can prevent sensitive data loss with the correct measures in place.

Skills required to become an ethical hacker

To become an ethical hacker, one must have deep knowledge about protecting system security, network breaches, algorithms, etc. Here is a list of the major skills that one requires to become an ethical hacker:

Knowledge of programming: It is very important for security professionals working in the field of software development life cycle (SDLC). It enables them to write codes to protect them from malicious attacks.
Scripting knowledge: Security professionals who deal with host- and network-based assaults must have knowledge of scripting.
Networking skills: Networking is a very crucial skill because security threats generally arise from networks. A cybersecurity professional must know about the devices, their connections and how to protect them in case of any vulnerabilities.
Understanding of databases: Databases are the primary targets of attacks. Being familiar with database management systems like SQL will allow cybersecurity professionals to efficiently examine database activities.
Knowledge of hacking tools: A cyber security professional must be familiar with the various types of hacking tools available. One must be aware of the usage and application of such tools to effectively carry out the desired objectives. They must constantly update themselves on the different hacking tools and trends to keep ahead of black hats.

Conclusion

Ethical hacking is an excellent career option if you are interested in this discipline. Cyber security is a major concern in this technologically advancing world. The IIT cyber security course can be a perfect fit to learn everything about ethical hacking and related topics. You’ll gain a detailed understanding of the information technology (IT) world.

Sign up for the Advanced Certification Program in Cyber Security by IIT Roorkee and Imarticus Learning and gain all the insights from the eminent IIT faculty and learn job-relevant skills.

Linear Regression: Definition, Types, Examples

Posted on April 5, 2023April 4, 2024 by Imarticus Learning

Linear regression has been used extensively in multiple industries for prediction purposes. It is an analytical method used in data science to study data and predict the value of an unknown dependent variable based on the known dependent variable.

This article aims to cover the definition of linear regression and its types with examples for better understanding.

What is linear regression?
Linear regression is a statistical method used to visualise the linear relationship between two variables. It quantifies the correlation between one or more independent variables and the dependent variable.

The two types of variables are:

Independent variable: It is the variable whose value is known and is used to determine the value of the dependent variable.

Dependent variable: This is the variable whose value is being determined, which changes with the independent variable.

The value of ‘A’, the dependent variable, can be determined through ‘B’, the independent variable.
The graphical representation of linear regression is a straight-line graph, assuming a linear relationship between the variables. The method is highly accurate and reduces the discrepancies between the predicted and the actual outputs.

What are the uses of linear regression?

Linear regression has been used in scientific and academic research, behavioural science, and business. Some of the uses of linear regression are:

To predict future values like the estimate of revenue value upon changes in investment. To envision future trends in both science and commerce.

This can also be used to understand the strength of the relationship between the independent or predictor variable(s) and the dependent variable.

What are the types of linear regression?

There are two types of linear regression – simple and multiple.

Simple linear regression
Simple linear regression is used when there is only one independent variable at hand which is used to determine the dependent variable.

The equation for simple linear regression involves four coefficients:

A = 𝛽0 + 𝛽1B + ε

In the given equation, A is the regression coefficient or dependent variable, 𝛽o is the intercept, 𝛽1 is the slope, B is the independent variable which assumably is affecting A, and the epsilon (ε) is the adjustment of A or the error value in our estimate of A.

Simple linear regression makes four preconceptions:
The data has been collected using scientific methods without any bias and does not have any hidden relation.
The error size (value of ellipses) does not have any significant change across the independent variable values.
The data is symmetrically distributed and maintains a level of normality.

The relationship between the independent and dependent variables is always linear. It can never be a curve or hyperbola.

Simple linear regression is used in determining the value of a dependent variable that is directly influenced by the independent variable. Simple examples of this would be determining the marks a student may score depending on the hours of study, or the amount of increase in antibodies against the number of viral cells.

Multiple linear regression

Multiple linear regression quantifies the relationship between two or more independent variables and the dependent variable. The independent variable may be consequent values or grouped values.
The equation for multiple linear regression involves the coefficients:

A = 𝛽0 + 𝛽1B1 + 𝛽2B2 + …. + 𝛽nBn + ε

Therefore, the coefficients change in numbers. As seen in simple linear regression in the above equation:
A is the regression coefficient

𝛽o is the intercept
𝛽1B1 is the first independent coefficient, the slope (𝛽1) of the first independent or predictor value (B1).
… signifies the number of coefficient values, the slope of the subsequent independent variables present between the second and last variable value.

𝛽nBn is the last independent coefficient
ε is the error value.

The above-given equation visualises the linear relationship between the dependent variable (A) and all the predictor variables.

Examples of linear regression

Linear regression is used in multiple scenarios –
Linear regression can also be used to predict the sales of a product based on the advertisement done through different media channels. This will employ the multiple linear regression model.

In medicine, simple linear regression may be used to determine the blood sugar level of a patient upon administration of a particular dosage of insulin. This can be used similarly to determine the relationship between different drugs and the patient’s vitals.

Conclusion
Data science has become an indispensable part of multiple industries, and linear regression, an important aspect of data science, has too. Therefore, knowledge in data science is a high-demand skill, and the best way to head-start your career is to earn a degree in data science certification. Learning linear regression makes it easy to calculate predictive values and estimate the condition of commercial or scientific variables.

Imarticus Learning offers a Postgraduate Analytics degree, a data science course with job interview guarantee. The PG Data Science and Analytics course is a job-interview programme that is trusted by industry leaders for the resources and quality of training provided. In this futuristic programme, students learn data visualisation with Python, Tableau and Power BI. Thus, the programme also helps learners in adapting to regular upskilling requirements.

What does Operational Risk Management mean? Explain its principles

Posted on April 4, 2023April 2, 2024 by Imarticus Learning

What is Operational Risk Management?

Operational risk management is the process of evaluating and dealing with the risks that come up in an organization’s day-to-day operations. These are things like operational risks, the organization’s assets, and how they are managed. Operational risk is the risk of the organization’s assets causing losses to the organization. For example, potential losses from physical assets such as a building, machinery, or computers.

There are also operational risks that come with managing these assets, such as the chance of an accident. To manage these risks, organizations must know the threats that could harm them. But, as you can imagine, it becomes difficult to identify every threat that could harm you. This is where risk assessment comes in.

What are the principles of operational risk management?

Risk assessment: you must identify the threats that could harm your organization. After you know what the threats are, you should look at your organization’s weaknesses to see if the threats could take advantage of them.

Risk Mapping: This is where you convert the information you collected during risk assessment into a format that you and your team can relate to. This includes key performance indicators (KPIs). Key performance indicators are metrics used to measure the success of an organization.

Risk analysis: At this point, you have all the information you need to evaluate the threats your organization faces and how they affect it. This is a risk analysis. You can use statistical tools to do this, like risk assessment tools, or you can use your own experience.

Controls: Now that you have all the information you need to decide on the controls needed to manage the threats identified during the risk assessment and analysis, you can either come up with a list of controls or put one in place and see how it affects the metrics you mapped out during the risk assessment.

Ways You Can Benefit From Operational Risk Management

Better Performances: The two main benefits of operational risk management are better performance and improved control. When you improve performance and control, your resources will work better, and your operations will be more stable.

Better Security: Another key benefit of operational risk management is that it leads to better security. When you deal with the risks found during a risk assessment, you can expect your risks to go down. This leads to better security as you can limit the number of threats and the damage they can cause.

Reduced downtime: You may also expect a reduction in downtime as you can anticipate issues before they occur. This leads to less downtime, causing less disruption to your organization and customers.

What are the main types of operational risk management?

There are several different types of operational risk, including financial risk, business risk, human resources risk, information technology risk, and compliance risk.

The most obvious type of operational risk is financial risk, which usually has to do with how money is handled.

Business risk refers to the risks associated with the selection of business ventures, the execution of business plans, and the management of risks associated with changes in business conditions.

Human resources risk is the most common type of operational risk. It means that employees could hurt an organization by doing or not doing something.

Information technology risk is the chance that something outside of an organization’s control could harm or stop its IT systems from working.

Compliance risk is the risk that an organization will break the rules and laws that govern how it works.

Why do you need a professional course on operational risk management?

Operational risk management is an important element of any business, and CFOS need to understand the risks involved in their operations. A professional course on operational risk management can help CFOs become better equipped to handle such risks.

The Postgraduate Certificate Programme for Emerging CFOs, jointly conceptualized by IIM Indore and Imarticus Learning, is an ideal CFO leadership training program in operational risk management. The program is a one-of-a-kind 12-month financial plan that was put together by experts with more than five years of experience. It not only teaches people the basics of finance and management but also gives them a full picture of how the financial services ecosystem works.

Through this program, CFOs can learn how to manage risks, spot possible risks, and come up with plans to deal with them. This program also helps them understand the legal and regulatory requirements that are related to operational risk management.