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Day: October 19, 2021

What is Role of Investment Banker in Underwriting Process?

Posted on by Imarticus

When it comes to choosing lucrative career options, the investment banking field has got its own charm. The big fat paycheck certainly adds a lot of weight when it comes to increasing the competition in the field. The big cheques bring big responsibilities to the table, the role of an investment banker is very diverse and requires a combination of hard and soft skills to prevail in the face of cut-throat competition.

One of the key roles of investment bankers and investment banks is highlighted in the context of the underwriting process. Before jumping over to the crucial role played by the investment banks, let’s understand the nitty-gritty of the underwriting process.

Understanding the Underwriting Process

In the contemporary financial landscape, underwriting has become one of the most crucial functions. From insurance to the stock market and banking underwriting is omnipresent in this industry. In the most generic sense underwriting process can be described as the practice of undertaking the risk associated with a venture in lieu of a specified premium amount to be borne by the party initiating the venture.
The process of underwriting in the case of the stock market involves determining the price of a given security and assessing the risk involved with it. The underwriting process in the context of the stock market differs from the insurance and banking sector. Underwriting in the insurance industry involves determining the risk and setting up the premium amount for a particular client based on their exposure. In the banking scenario underwriting assists in the crucial function of appraising the creditworthiness of a potential customer through their past financial records.

Investment Banks in the Underwriting Process

From guiding on alternative investment opportunities to assisting in mergers & acquisitions and facilitating the underwriting process, the investment plays a holistic role in the functioning of the global economy. In addition to this investment banks also aids in asset management for large investment funds and individuals with high net worth.
Investment banks have a crucial role to play when it comes to underwriting in the context of the stock market. The role of investment banks in the underwriting process begins as a pre-underwriting advisor and continues even after the distribution of securities in a similar capacity. The investment banks do a thorough financial analysis of the company before underwriting any security to determine the real value of securities and publish relevant information for the perusal of investors who will likely purchase the stock.
You must’ve heard of Initial Public Offerings (IPOs) which is where the investment banks have a frequent intervention. The investment banks play the middlemen by acting as the mediator between the corporations who have to go public with the IPOs and the investors who are eyeing for the stocks of the company. So what investment bank does here is it purchases the shares of the corporation going public and then, in turn, sells those shares through a stock exchange.
This process is complicated and needs assessment of the fair price of the shares for the company going public. This involves a fair amount of risk in case the IPO is not subscribed fully when selling through the exchange, for this the investment bank charges a premium. In the case of big corporation’s IPOs, there are multiple investment banks involved so that the risk for any single investment bank is not paramount.

Furthermore, there are three types of commitment in the underwriting process that is carried out by investment banks in the case of IPOs.

1. Firm Commitment
In this case, the investment bank as an underwriter agrees to buy the entire issue of shares by the firm going public at an agreed-upon value per share. Any financial loss in case the shares are not fully subscribed is borne by the investment bank.

2. Best Efforts
In this scenario, the investment banks agree to sell as many shares as possible at the agreed-upon value per share. It doesn’t have any obligation if it fails to fully sell the shares to the public, so it is not legally accountable for the unsold shares of the company going public.

3. All or none
The name is very self-explanatory and doesn’t require much guidance to understand. Under this scenario, the underwriter will only get the deal if it can help sell all the shares issued by the firm at the agreed-upon value per share. If they fail to sell the entire stock they will be discarded from the agreement.

Skills and Tools Which Are Required in Fintech Industry

Posted on by Imarticus

FinTech which stands for Financial Technology is a rapidly growing but highly expansive field. FinTech’s areas of interest are many such as Net Banking, Cryptocurrency, Insurance, Trade, Application Design, and Online wallets. Since this interdisciplinary field is so widespread, it entails professionals from a wide variety of disciplines within its folds. A software designer and an accountant both can work in the field of financial technology, as this field caters to a large set of skills.

Some of the most sought after skills and tools in FinTech Course

● Cybersecurity expertise: One of the main threats that the FinTech industry faces is malware infections and hackers. If a financial technology system is infected and its data gets breached then, the corporation can face penalties; hence people who can secure the computer systems from virus invasion and prevent a breach of privacy are of great value in FinTech.

● Machine learning and Artificial Intelligence: FinTech works heavily with artificial intelligence. This field needs people who have apt statistical knowledge and instincts. One needs continuous analysis and study of data in FinTech. A candidate who knows how to use the machine learning to add, edit, remove, and study statistics will always be a useful addition in the FinTech industry. A person is needed to be proficient in Hive, SQL, and Pig. They are also expected to know how to work with toolkits like Weka, Python, NumPy, and MatLab.

● Soft Skills: The nature of FinTech industry is highly collaborative. A person is required to work closely with people who come from different backgrounds. This makes it necessary for a worker to have soft skills like teamwork, the ability to work in a group and be amicable. This industry also requires people to be highly open to ideas, edits, and suggestions so a person who is not receptive to feedback will not be a good fit for this industry. People with soft skills help in team-building and communicating ideas. They are useful in managing human resources and resolving conflicts that might arise in different groups due to the dense presence of workers from different disciplines.

What are the challenges and questions of the FinTech industry?

Like all industries in the world, FinTech also has its share of problems and challenges. FinTech is a relatively new industry; hence its challenges are still being explored. But over the years, government regulations and breach of data are the two main challenges faced by FinTech companies.These restrictions and lack of government support prevent these innovative companies from establishing themselves due to legal and bureaucratic hurdles. These problems are particularly harmful to start-ups than established companies.

Another major challenge that surrounds FinTech is that of hacking and breach of privacy. In the world of Big Data, information is gold, and hence hackers and cybercriminals are always on a lookout for profitable data. The breach of security by hackers or malware infection in FinTech brands does not just ruin the credibility of the company but it also leads to fines and penalties.
Some of the strictest penalties for FinTech corporations are made by the European Union (EU). All FinTech companies that are practicing in the European Union are mandated to adhere to the GDPR guidelines that deal with privacy and data protection. Any FinTech company that fails to protect the data of its users can face up to 20 million euros in fines or give up 4 per cent of their annual turnover in penalty. To prevent any intrusion by hackers, FinTech institutions end up splurging a copious sum of money into developing firewalls and other security systems such as anti-viruses. Not just money but time is also spent in ensuring that the information base is secure from all kinds of Trojans, malware, and snooping.

Conclusion

Contrary to popular belief, FinTech is not completely run by engineers or bankers only. Even people who are not particularly tech-savvy can work here in a highly collaborative space. In this cabalistic and digitalized world, by taking a Fintech Course you will be able to understand the immersion of financial technology which is in almost all kinds of fields and disciplines around the urban world.

Master the Concepts of Banking and Finance with this Cutting-edge Online BBA

Posted on by Imarticus

The field of banking and finance delves into the area of money, stocks, investments, and credit. Finance is an important aspect of our economy because it helps us understand how to manage assets, cash flow, capital and ensure financial security in the future.

If you major in banking and finance, then you will learn about the systematic movement and management of money. The finance’s role in organizations guarantees that operations are supported with equity or debt, and they can choose value-adding projects and manage cash flow, risk, and liquidity in the best interests of their stakeholders. Understanding the price of assets and derivative securities is critical in financial markets.

Banking and finance is one of the major career options in banking and finance for 12th pass students. It prepares you for a variety of job roles in banking, brokerage, consulting, funds management, insurance, and superannuation fields. You can work with corporations, financial markets, or the government. It is also a discipline that can take you all over the world.

Here is one of the BBA online courses that will help you master the concepts of banking and finance

BBA in Banking and Finance:

This comprehensive BBA Finance program delves into new-age banking and financial solutions through a practical learning approach with technologies such as application programming interface, blockchain, cloud computing, artificial intelligence, machine learning, RPA, IoT, and big data.

The learning opportunity allows you to apply what you learn in the curriculum to real-world situations. If you want to further your career by becoming a new-age banking and finance expert, then your quest has come to a conclusion and a remarkable career is waiting for you.

Perks of taking up this online BBA training course:

  1. When you enroll in this JAIN Online BBA in Banking and Finance program, you will obtain special access to five professional learning courses on the LinkedIn Learning platform. Every program is meant to improve your capacity to comprehend complicated subject matter throughout the main BBA course by expanding your knowledge of essential finance features through an easy online learning experience.best BBA online courses in India
  2. This is one of those rare BBA online courses that grant you access to resources powered by our research, knowledge, and technology partners, such as PWC, Rise Mumbai, Automation Anywhere, and BTA, to stay up to date on the latest financial sector trends and practices.
  3. You will be given a JAIN Online e-mail address. As part of this forward-thinking program, you will have ongoing access to the JAIN Online lab environment, allowing you to put theory into practice.
  4. Following completion of your BBA in Banking and Finance, you will be provided with unrivaled career support and job placement options through the JAIN Online corporate relations team as well as Imarticus Learning’s specialized employment team.
  5. In addition to seminars on resume writing and interview preparation, this course maintains a specialized corporate relations team to help you find the ideal career path. The corporate relations team provides regular feedback on your CV and social media profiling, as well as 1-on-1 mock interview sessions.You can apply for appropriate positions and attend interviews online using our Connect To Careers e-hiring system. Job fairs are often conducted by Connect To Careers to connect you with employers.

best BBA online courses in IndiaMost young people remain confused about what to do after school. This is one of the major career options in banking and finance for 12th pass students that give them hands-on experience on the practicalities of this profession. This online BBA training course will help you to explore deeper the world that awaits you once you step into the corporate sector.

3 Technologies to Utilize In Real Estate Finance & Accounting

Posted on by Imarticus

The Real Estate industry is among the most prominent industries in the modern capitalist society. It has always been one of the robust ones given the value that real estate holds for the people. The real estate industry is also evolving with new technological innovations around. Commercial real estate is all about thriving on technological innovations. Data is the most important fuel for businesses in contemporary.

It helps them to obtain valuable insights into the business and plan for challenges that might arise in the future.The commercial real estate industry is also looking forward to leverage data and make prudent decisions. Finance and accounting for the real estate industry can get very complex. To comprehend to ease these complexities, players in the real estate industry can focus on some of the most valuable technological innovations.  Let’s delve deeper and explore how these technologies help the real estate sector, especially for finance and accounting.

Predictive Analytics

The predictive analytics model uses a wide range of statistical techniques including data mining, machine learning, predictive modelling, etc. to analyze the historical and present data and makes predictions. It is a part of advanced analytics that is used to predict uncertain events and reduce any causality for the business. Players in the real estate industry can use predictive analytics to make an accurate forecast of the future and plan their business strategies accordingly.

The predictive analytics tools use complex machine learning algorithms and artificial intelligence-driven models to make predictions regarding future market conditions. Now to facilitate all this, predictive analytics requires tons of data to analyze various scenarios and learn about the behaviour, patterns and trends. This learning by the machine is applied to real case scenarios by feeding the present variables that are required to make accurate predictions.

To make it more clear for you, let’s take the pricing model of Uber that uses predictive analytics. So you might have observed the price fluctuations at different hours of the day. This happens because Uber’s dynamic pricing model uses a combination of predictive analytics and real-time data to show the actual price at a particular time given the demand and supply of cab services in that area.

Similarly, predictive analytics can be used in the real-estate sector by integrating some of the most common variables like construction rates, inflation, location, etc. into an algorithmic model to accurately predict the value of a real estate in future. This will help businesses to make more informed decisions. In addition to this, equipment predictive analytic tools can be used in construction to help lower repair and maintenance cost. This can be done by evaluation of the historical financial data for repair and maintenance.

Internet of Things (IoT)

If you look around yourself, you’ll be amazed to see the number of data points and smart devices that are being used. The Internet of Things can be understood as a giant network of interconnected technological objects that can collect and send data using a wireless network without any human intervention. The digital machines have a unique identification number that helps to determine the origin and source of data.

IoT technology is being largely used by businesses to obtain real-time data that can be analyzed and used for desirable results.This application also applies to the realestate industry that is increasingly relying on the use of data for making business decisions. IoT also helps the real estate sector in the construction of property through on-site management, material deliveries, etc. This is the most eminent benefit of IoT for real estate but it isn’t just limited to this.

Other applications include onsite building management, monitoring energy consumption, property security, etc. This improved communication and transparency helps tenants a great deal and also streamline operations those results in cost saving on account of efficiency. Accounting is also simpler with quick, automated, real-time information that helps to accurately report the numbers. It is highly beneficial for management accounting.

Business Intelligence

Business Intelligence is all about leveraging innovative technology and tools to make sense of the data accumulated. It’s all about transforming data into actionable insights that can help businesses to make sense of numbers put forward. It is frequently used to automate key processes and save resources to be utilized efficiently elsewhere.

It can also help to automate accounting processes that are repetitive in nature. It is used to process complex data that could’ve been misinterpreted by human intelligence, thus eliminating the chance of random human error. It also helps to reduce the vacancy rate for properties with automation and real-time updates.

Big Data Engineer Salary: How Much Can You Earn as a Big Data Engineer?

Posted on by Imarticus

Who is a Data Engineer?

As businesses across the globe are enthusiastically adapting the data-driven strategies to optimize their decisions, the demand of highly skilled Data Engineers has increased manifold. A skilled person who is able to convert the raw data into a self-explanatory form to analyze the trends by developing requisite algorithms is a Data Engineer.

The entire task of Data Mining, maintaining and extracting trends from different data sets in an organization is completed by a team of Data Engineers. Ultimately, the Data Engineers provide reliable infrastructure to maintain big data.

Skills required to be a Data Engineer

A Data Engineer must have deep understanding of SQL, Extract Transform Load, Apache Hadoop, in depth knowledge of Python, Java, Scala, Kafka, hive, storm and many more.

Big Data EngineerEnterprises now a days prefer the employees with the experience of working on the cloud platforms like Amazon Web Services etc. Sound knowledge of Data warehousing and Data modelling is also given a lot of preference these days.

The required skills and preferences may affect the salary of an Data Engineer by 10%-15%.

A Data Engineer deals in Big Data, the person should be proficient in the documentation skills and must also be good in his/her verbal and Non-verbal communication skills.

How to Become a Data Engineer?

Applied Mathematicians, Engineers, People holding Bachelor’s degree in Computer Sciences or related IT field find it easier to become a Data Engineer. The aspiring candidates then go for a Big Data certification course to have in depth understanding of required technological skills to be a Data Engineer.

Roles and Responsibilities of a Data Engineer

The generic tasks that a Data Engineer has to perform include:

  • Aggregation and Analysis of given data sets
  • Development of Dashboards and reports
  • Development of tools for business professionals
  • Providing improved techniques to access the Big Data

Three main domains in which a Data Engineer works are: Generalist, Pipeline centric, Database-Centric Generalists are the Data Engineers who processes, manages and analyses the data.

Big Data EngineerPipe-line centric Data Engineers work in coherence with Data Scientists to utilize their collected Data. Database-centric Data Engineers manages the Data-flow and database analytics.

Along with the technical skills, a Data Engineers must have some soft skills as well to communicate their analysis. Some of the key responsibilities are:

  • Acquisition of Data
  • To match their development constantly with the business requirements
  • Consistent improvement in the data reliability, efficiency and Data Quality
  • Development of predictive and prescriptive modelling

The key responsibilities vary from organization to organization.

Data Engineer: Employers and Salaries

Some of the top companies where Data Engineers are highly paid are:

  • com Inc
  • Tata Consultancy Services Limited
  • IBM Private Limited
  • General Electric (GE) Co
  • Hewlett-Packard
  • Facebook

Factors affecting Salaries of Data Engineers 

Experience:

Average Experience as a Data Engineer Average Pay-Scale based only on Experience
Entry level ₹400,000 approx.
1-4 years ₹739,916 based on 317 salaries
5-9 years ₹1,227,921 based on 179 salaries
10-19 years ₹1,525,827 based on 49 salaries

Job Location:

The Data Engineers working in the prime locations like Gurgaon (Haryana) earns 27.3% more average salary, in Hyderabad (Andhra Pradesh) 13.7% more average salary, in Bangalore (Karnataka) 12.5% more average salary than in locations across the nation.

The average salary of a Data Engineer in Mumbai, New Delhi and Chennai are relatively lesser than average salary across the nation.

What Is Distributed Computing Training in Machine Learning?

Posted on by Imarticus

The traditional machine learning approaches rely on using open-source tools for data analysis and prediction making. This approach does not work out well when the data is large. The RAM on the system gets damaged when large files like these are involved. We need to use an approach that not only helps us build the machine learning models successful but also ensures that the system is not burdened or damaged while an operation is being performed. Hence, we need to learn Distributed Computing in Machine Learning.

What is distributed computing?

An approach to improve the system performance, resolve scalability issues and increase the system efficiency by dividing the task being performed on a single machine to different systems is called distributed computing.

Distributed computing has many applications such as the world wide web, global financial systems, machine learning and much more. Here we concentrate basically on the concepts of Machine Learning Training with distributed computing.

Distributed computing training 

The main purpose of this training in machine learning is to help an individual master the skills in machine learning and resource allocation and management. Distributed computing came up as a technique to resolve the scalability associated with machine learning algorithms. It developed on a massive scale in recent years to provide large-scale operations such as big data analysis efficiently.

When we talk about distributed computing, there are two main approaches:

  1. Horizontal fragmentation- It uses an approach to store the selected portions of the available instance at different sites.
  2. Vertical fragmentation- Storing of the selected attributes of the subsets of the instances comprises of vertical fragmentation.

The data involved in machine learning is very massive if a real-time problem is involved. A situation might be encountered where the machine learning model needs to be trained again and again without disrupting the ongoing parallel task. In this situation, distributed computing serves as a boon by resolving the issues.

The training in distributed computing also highlights the importance of applying these techniques in fields such as medical computing where huge amounts of data are uploaded at every instance of the given time and need to be analyzed for relevant purposes.

Distributed machine learning platforms

Training in distributed computing for machine learning also provides information about the platforms that been developed to do so. Some of these platforms are listed below:

  • H2O- Developed by H2O.ai, H2O is an open-source platform for distributed computing in machine learning with in-memory support. It also provides support for traditional machine learning algorithms and includes AutoML functionalities.
  • TensorFlow- Distributed TensorFlow provides different servers each of which is considered to be a cluster and each process is made to run on an executive search engine.
  • DMTK- It stands for distributed ML toolkit and is developed by Microsoft to provide highly efficient techniques for performing a machine learning task.

Apart from the frameworks mentioned above, there are other frameworks such as Apache Spark Mlib and Apache Mount that assists in the machine learning applications as well.

Conclusion

Most of the problems that we encounter today are voluminous and very hard to process for machine learning tasks. Distributed computing left its footprints in the field of machine learning by solving one of the major issues that are big data handling. It has gained a lot of popularity in recent years because of its high degree of scalability, efficiency, and performance. It has not only helped in performing large-scale computations but has also helped in the optimization of the operating systems. To be accurate, it has revolutionized the world of machine learning training and computations.