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Day: October 29, 2021

What Are Some Fintech Companies?

Posted on by Imarticus Learning

The Basics of Fintech

At the very core of any technological evolution is a determination to provide something better than what already prevails in the market. Can you think of any industry that is not influenced by technology? I am wondering it’s very hard to think of one. Fintech is the short form of Financial Technology, meaning the use of technology in the financial domain to provide better finance-related services over tthe traditional methods.  

The Fintech industry comprises companies and start-ups that are focused on providing solutions to various problems that exist in the industry. The bottom line of using cutting-edge technology in the financial domain is to provide more efficient services that could work in conjunction with our fast-paced world, ultimately increasing customer satisfaction. Let’s dig more into the players in the Fintech industry who are revolutionizing how people use banking and financial services.

Players in the Fintech Industry

Some of the key players in the Fintech domain are mentioned below.

Ant Financial 

One of the major global players in the Fintech industry is based in China. Established in October 2014, Ant Financial Services Group is on a mission to bring inclusive financial services worldwide. It is also the official operator of Alipay, the digital payment portal by the Alibaba group for its e-commerce platform.

Xero

Xero is one of the fastest-growing software as a service (SAAS) provider worldwide that already has more than 2 million subscribers. Founded in the year 2016 in New Zealand, it aims to provide accounting solutions for small and medium business enterprises through its cutting edge software technology. Xero has also backed the title for the World’s Most Innovative Growth Company in 2014 & 2015 by Forbes.

Avant

Founded in the year 2012, Avant is one of the key players in the Fintech domain. Originally registered as AvantCredit, Avant is a Chicago based Fintech firm that provides online credit solutions for customers. On a mission to eradicate the obstacles in the borrowing process and minimize the cost of borrowing for individuals, Avant brings more transparency to the whole system through its revolutionary technology. 

Tala

Tala is a game-changer in the Fintech domain for providing micro-loans through its smartphone application. Founded in the year 2011, Tala has a customer base like no other, it provides credit facilities to people in the remotest parts of the world. On a mission to expand financial access and choices to billions of underserved people worldwide, Tala is using technology for the best! 

PayPal

One of the most prominent players in the industry, PayPal was the harbinger of the Fintech revolution. With a customer base of more than 277 million active users, PayPal believes in empowering individuals and businesses to connect and prosper in the globalised economy through its digital financial services platform. It adds value by enabling individuals and merchants to indulge in monetary transactions in multiple currencies worldwide.

Stripe

Stripe is a US-based Fintech company that was founded in the year 2009. It uses its software technology to provide payment solutions for individuals and business owners that help to receive and make payments online. Stripe has built a customer base in the e-commerce domain by catering to the payment solution needs of the online business owners.

Robinhood

Robinhood is a US-based Fintech firm that provides investment solutions through its mobile and web applications. It was founded on the core principle of increasing participation of people in the finance industry through commission-free access to investing. Robinhood is a fast-growing company that already has a customer base of more than 6 million active users, a major percentage of which belongs to the millennial population.

 

What Are the Algorithms in Machine Learning? How Does It Work?

Posted on by Imarticus Learning

Machine learning is a vast field comprising of various data related operations such as analysis, prediction, decision making and much more. These applications require a set of well-defined steps to proceed with the idea designed for model construction. A set of well-defined instructions that produces some output or accomplishes a particular task is called an algorithm. The machine learning algorithms are broadly classified into 3 categories – Supervised, Unsupervised and Reinforcement Learning.

To choose an appropriate algorithm in machine learning, identifying the kind of problem is very necessary as each of these algorithms obeys a different plan of attack to deal with the proposed problem. Supervised learning uses an approach where the output is already known to the user or the individual while unsupervised learning concentrates on the concept of similarity in properties of the objects. Reinforcement learning differs from both of them and uses the art of learning from experiences.

Supervised learning

Supervised learning is used in machine learning tasks such as classification, regression, and analysis. It is considered as a concept that deals with labeled values. This means that the objects are categorized or assigned to different classes based on their properties. The algorithm implementation in supervised learning is done by a two-step procedure namely model construction and model utilization.

Firstly, the given data is cleaned and divided into training and testing sets. The model gains the ability to produce output by learning from the instances contained in the training set. The test set gives a measure of the model performance by producing accuracy. The accuracy indicates the amount or rather the percentage of unseen data that was computed correctly by the applied algorithm.

There are several metrics to determine the performance of the model and improve it if the performance is not up to the mark. This includes performing tasks like cross-validation, parameter tuning, etc. Hence, we can conclude that supervised learning uses labeled classes and target values to classify an unseen data point.

Unsupervised learning

In contrast to the supervised approach that already knows the predicted outcome, unsupervised learning uses the basis of similarity in properties to classify the unseen data points in the given n-dimensional space.

The main idea is to take a data point that is new to the given space, extract the behaviors of the data point, compare it with the already existing properties of the other objects and accordingly classify or categorize them into the appropriate group. The common examples of unsupervised learning are clustering, Apriori and K-means algorithm.

Reinforcement learning

Reinforcement learning is very similar to the animal kingdom where the animals do not train their offspring to perform a particular task but they leave them out in the ecosystem to learn from the experiences that it gains while struggling to accomplish a particular task.

The basic idea of performing reinforcement learning is to let the model learn on its own. It uses a trial and error strategy to gain knowledge from the available environment. According to the experiences gained from the conditions, it is exposed to, appropriate predictions and decisions are made. Markov Decision Process is an example of reinforcement learning.

Conclusion

Because of the wide variety of applications offered by machine learning, there are several Machine learning courses dedicated to offering the training in machine learning algorithms so that an individual can recognize the problem efficiently and work towards building an appropriate solution. Learning and understanding of machine learning algorithms are very easy. It just needs a proper classification of the interest in performing the desired operation.

Why Big Data Analytics Is a Good Career Move?

Posted on by Imarticus Learning

Data is wealth, this doesn’t sound hyperbole in the modern-day scenario. A data analyst turns raw data into meaningful information that enhances the business and market share and who doesn’t want to grow? Let us look at the key points discussed in the article justifying Big Data Analytics as a good career choice.

Statistics indicate that the average salary of a data analyst in India is 10 lakhs per annum and this salary will keep on growing as you level up. There is a demand for candidates in this corporate world who have good command over big data and its analytics.

Big data analytics is trending globally, surveys show that the USA will have around 2.7 million job postings in big data analytics by 2020. If you are going for big data as your career choice, not only will you gain knowledge over a variety of languages, applications, and strategies, you will also have a chance to grow as the flow of data is never going to stop.

Big data analytics closely works in coordination with the Internet of Things and the outcome of big data analytics helps in business development and predicting trends. Big data also supports various other streams. It is a vast field full of opportunities. Big data analytics is not all about programming languages and statistics, it is also a way of providing solutions to existing problems. It is a way of providing strategies that help in the growth of businesses.

You will have to convey your point of view in your workspace and how it will help in enhancing the business. These things will help you to grow as a good orator and leader. You will also make a lot of business relations in this career. Business intelligence (BI) will also come into play if you work as a data analyst. So, there are a lot of parallel fields if you are a data analyst, such as the importance of big data and its analytics nowadays.

Surveys show that there is a huge demand for big data analysts but there is not enough skill set in individuals to meet the criteria. If you have studied well then you should go in the field of big data analytics as you would be valued and respected there. This deficiency of good data analysts and data scientists are faced in all countries and that is why education centers are providing quality education on big data and related fields.

You can find a lot of online certified course on big data analytics by trusted sources. Every company is trying to gain an edge ahead over others in this competitive era by predicting smart trends and business ideas using big data analytics. There are a lot of companies that consider big data analytics as a topmost priority as it helps them in increasing the efficiency and quality of their business. Why would you reject to be a part of this cutting-edge technology?

Big data analytics is quite versatile, you can choose among prescriptive, predictive and descriptive analysis depending upon your interest and work environment. You can also choose among a variety of job designations such as big data analytics business consultants, big data analytics architect, big data analytics, etc.

Whatever smart solutions analytics is providing today, there is always a scope of improvement and a need for human intelligence. There is a lot of requirement of people in current days who can think out of the box, who can understand and analyze big data and help in improving the business. This was all about the pros of joining the field of big data analytics.

Why is Equity Research important?

Posted on by Imarticus Learning

Investing in Equity has been considered a raffle for ages now which is enveloped by uncertainties. There is a perception that the investing game is all about fortunes and lucks and someday if a miracle happens, you can make millions overnight and if misfortune strikes you can lose millions in a minute.

All of it is true but to a very small extent. To save an investor from the wrath of the stock market, Equity research comes into the picture. Equity research acts as a shield for the investors and protects them in the best possible ways.

What is Equity Research?

 The literal meaning of Equity Research training is about your research before buying the stocks of any company. Equity research involves analyzing and studying various companies and the potential risks associated with those companies. Equity research is the first step towards your investing decision.

Companies listed on the stock exchanges are kept under the magnifying glass and the overall aspects which surround the company are carefully studied and examined. Equity research is carried out by professionals who have in-depth knowledge of the subject matter and can conclude on the basis of various results whether the stock should be bought or not. These professionals guide investors in making a purchase decision.

What are the steps involved in Equity Research?

  1. The economic conditions of the country where the investment has to be made are carefully studied and in-depth research has to be done. Various parameters like the Gross Domestic Product i.e. the GDP, the demand and supply factors, political conditions, etc. are taken into consideration as these factors affect the overall health of the economy and hence affect that company too where the investment is being planned.
  2. Various financial statements, data points, etc. are put under the scanner and are read carefully to understand the financial stability, capital structure, cash inflows and outflows, time period of dividend payments, etc. are carefully analyzed. Balance sheets are also analyzed so that a complete picture of the assets and liabilities can be ascertained. Profits and loss statements, income statements, cash flow statements are analyzed before investing in any company.
  3. Various performance indicators such as the revenue streams and the profitability of the business are analyzed. The history of the company where the investment has to be made is carefully analyzed. Past performances, the standing of the company with respect to its competitors, Quarterly and Annual results are carefully studied before making any investments in the company.
  4. The company is then valued by using various valuation models based on revenues, discounted cash flows, the sum of parts, Assets, and Liabilities, Goodwill, etc. This is done to come at a fair valuation of the company and also to understand whether the company is overvaluing or undervaluing itself at a given point in time.
  5. The fair prices of stocks are calculated mathematically and theoretically by using various valuation models and are then compared with the stock prices currently prevailing in the market. A deviation of a minor degree is usually ignored while computing the exactness of the share values.
  6. If the fair price calculated by the equity researcher turns out to be more than the prevailing equity share prices, the company in most cases have undervalued their shares by varying numbers. In such a case, the buying decision is made as the potential of the company is huge and the prices will most probably shoot up.

Conclusion
Investing is a game of gamble but with a systematic approach, it can be dealt with in a way that is a multiple-time better approach to make investments. With careful study and efficient equity research, one can reap the best benefits of their investments.

Is Commercial Banking a Good Career?

Posted on by Imarticus Learning

This question needs to be answered by you only, I can provide you insights and pros of this job but in the end, it is you who has to decide. Commercial banks are generally profit-oriented and if you are a commercial banker, you will have to deal with the clients regarding loans and depositions. If we look at the statistics the compound annual growth rate and the amount deposited per SBA have increased significantly over the years. More and more people are opening their bank accounts.

The government is also helping people to open bank accounts and the country is witnessing a shift from cash to cashless transactions. This field will grow as usual and if you are interested in a banking career then there will be chances for your growth too.

To become a successful commercial banker, you do not have to earn many degrees, what you need is a charismatic character so that people can trust you with their money. If you are dependable and likable, you are likely to grow in this field. You will find different types of jobs in commercial banking ranging from relationship manager to credit score generator.

You need to find out your interest, if you are good at statistics and computation, you would do better on a desk job with the computers but if you love to interact and have good communication skills, you can deal with people face to face. There are many banking firms who are currently looking for a commercial banker and they will always be looking because there is an ever-growing demand in this field. Growth is there in this field but the growth would always be less than that of a corporate banker.

You will not be always sent out to deal with the people, you may be handed a computer and would be asked to determine credit scores of various people the whole day. You must be very clear about what you want in your life. If you are happy doing such stuff then only you should go. Commercial banking training is a good career choice but sometimes you have to start from scratch and then depending upon your capabilities you will be promoted.

The sales culture can get aggressive at times. It is an era of competition so your job as a commercial banker may get complicated at times but if you keep up the good work you will get promoted and the hectic nature of your job will decrease as you go up. If you are not interested in hard-earned money, I suggest a commercial banker is not the job you are looking for. Commercial banks look for profits, so they will always try to increase their market share and you would also feel the pressure.

If you are good at accounting and statistics, you can choose this field. But I would repeat, ask yourself and decide what do you really need? There is a growth in loan amount in India, many people are preferring credit cards nowadays so there is an ever-growing demand in this field and if you are working for a trusted firm, I suppose the risks are less. But in this era of competition not much micro-firms get their feet moving and they just vanish.

Conclusion
Overall it is a satisfactory job but there are even better options out there. If you are strict and feel like you have perfect skills for a commercial banker, you can go for it. But if you are yet confused then I must say that go for a corporate banker instead. The salary and growth, both are more in corporate banking. But then again, it is a comparative analysis. I would say commercial banking is a satisfactory job but not an overwhelming one. Rest whatever I tell here, the wand is in your hands. Hope this helps.