The Rise and Dramatic Fall of European Investment Banks in the US

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The modern investment banking industry is one of the most important aspects of the capitalist economy. It is also seen as one of the most sought after career choices for people in the finance industry given the lucrative remuneration and perks that the job offers. Ever wondered how this industry came into existence? How did it help to channel liquidity in the economy and boosted wealth creation? Let’s peep into the history of the glorious investment banking industry.

In its nascent phase, the investment banking industry was limited to investment financiers. The investment financiers were extremely wealthy individuals who had the surplus wealth to spare. They provided funds to governments and the Royals as loans which were backed by taxes collected from the citizens. The modern investment banking started much later and is a part of modern history.
The United States was the founding nation of this industry during the times of the Civil War where Government bonds were circulated to investors as a financial instrument of investments. It followed by issuing war bonds to the public for raising money. After the war was over, the investment banking industry was formally established to fund large scale projects related to railways, manufacturing, etc. Investment bankers were primarily mediators who matched the parties seeking a fund with parties looking to invest in profitable avenues.

European Investment Banks In The US Territory

There was a time when the European investment banks went on an acquisition spree in the United States to tap on one of the most promising trading and banking markets in the whole world. The year 1978 saw the first move from European lenders in the US market when Credit Suisse made an exemplary move into First Boston, a top tier advisory firm in the US. By 1998, Credit Suisse acquired the third position in the industry in terms of investment banking fees, beating both Morgan Stanley and Goldman Sachs.
The pre-global financial crisis market saw the proliferation of European investment banks in the US market. Some of the most prominent acquisitions which were the highlights of the growing European expansion were as follows; Deutsch Bank’s acquisition of Banker’s Trust, in a record-breaking $10 billion deal (1998), HSBC’s takeover of Consumer Finance Business Household (2003), Credit Suisse’s acquisition of DLJ (2000).

All these deals amounted to billions being pumped into the US market from European lenders. European investment banks became prominent players in fixed income trading and leveraged the finance segment by hiring top-notch talent in Wall Street. The period between the years 2002-2007 saw a rise in the market share of European investment banks in the US territory.
The reign of the European Investment Banks in the US territory began to crumble in the last decade around the global financial crisis. By the year 2007, the Deutsch Bank was ranked 9th in terms of Investment banking fee that was approximately equal to $1.6 billion, far less than its competitor JP Morgan’s $4.4 billion. Retail operations of the European Investment banks also struggled; HSBC’s household business was shut down just after the few years of its purchase.

The period between the years 2012-2019 marked a steep decline in the proliferation and profitability of the European Investment Banks. In more recent developments it was found that HSBC was shutting down 30% of its US branches after admitting the losses from US operations. In addition to this, another prominent European player the Deutsch bank closed its global equities business which resulted in thousands of job losses. One of the most prominent reasons for this steep decline has been the strict regulatory challenges faced by the European Banks after the global financial crisis that shook the world.

Top 7 FAQs About Business Intelligence For Beginners: Answered

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Top 7 FAQs About Business Intelligence For Beginners: Answered

Business Intelligence is referred to as the applications and practices used to represent business data. These practices help in making better decisions and also help in developing the business. The Business Intelligence system helps any particular organization/firm to collect, store and manipulate data/information in such a way that it helps in the growth of the business. A lot of questions are asked regarding this field, especially by the newcomers. Let us look at some frequently asked questions about business intelligence and their answers.

Q – What are the skills required for Business intelligence?

A – A person must have soft skills like communication and writing skills, problem-solving approaches, etc. Besides soft skills, one must know how to use various analytics applications such as SQL, ZOHO, Microsoft Business Intelligence, etc. He/she must be able to analyse the given data and use it for business development. A person should be good at taking business-related decisions. These skills are enough for a beginner but you will have to learn more and more if you want to grow after settling in this field of BI.

Q – Where to start learning about business intelligence?

A – There is no shortage of books and resources available for learning Business ntelligence. Some books like ‘Successful business intelligence’ by Cindi Howson, ‘Business intelligence roadmap’ by Larissa T. Moss & Shaku Atre, ‘Business intelligence guidebook’ by Rick Sherman are regarded worldwide as some of the best books on Business Intelligence. Besides these, you can get many post-graduation courses and online courses on Business Intelligence. The IIBA (International Institute for Business Analysis) also offers certification courses and resources on business intelligence. If you just need to add Business Intelligence as a skill in your resume, go for online certification courses.

Q – What are the top job roles in Business Intelligence?

A – Some top job roles in Business Intelligence are as follows –
• Business Intelligence project manager
• Business Intelligence specialists
• Business Intelligence consultants
• Business Intelligence director
• Business analyst

Q – What is the future of Business Intelligence?

A – The backbone of business intelligence is data and information. As you can see big data and its analytics is expected to grow more and more. According to reports, there will be a rise of 14% in the hiring of Business Intelligence experts in 2020. The Business Intelligence tools and applications are giving more accurate results day by day. The big data is supposed to keep flowing with an increased rate in the future too, so to collaborate and manage this big data, analysts will be required. The future of Business Intelligence is quite vast.

Q – What is the difference between analytics and Business Intelligence?

A – It may be confusing in starting as both these terms are used simultaneously. Business Intelligence is a subset of business analytics. Analytics uses the generated data to fulfil current business needs. It helps in increasing productivity and making better business decisions. Whereas Business Intelligence only focuses on current business needs.

Q – What is the average salary for job roles in Business Intelligence?

A – In the USA, the average business analyst salary is $68,075. In India, it is ₹ 7,98,671 per year. The salary also exceeds as you step up, the Business Intelligence Directors in India have an average salary of ₹30 lakhs per year.

Q – What are the focus areas of Business Intelligence?

A – Business Intelligence focuses on the following sectors
• Customer satisfaction level.
• Smart business predictions.
• Cost customization.
• Cross-selling and up-selling.
• Market share analysis.
• Defect/loss reduction.
• Profit and revenue management.

Conclusion

Business Intelligence helps in making smart business decisions that increase the profit as well as the market share of the firm/company. It also helps in finding customer satisfaction levels and customer loyalty. This article was all about FAQs asked regarding business intelligence and its answers. The questions and answers are framed for the beginner level. I hope it helps!

Also Read: Difference Between Business Analyst & Business Intelligence

Top 5 Fintech Trends Everyone Should Be Watching in 2020

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The finance industry has undergone a massive technological transformation in the past few decades. This amalgamation of Finance and Technology has given birth to a new trend in the finance industry called Fintech. The Fintech industry is comprised of companies that leverage cutting-edge technology to provide creative solutions to traditional problems which were earlier unaddressed by the traditional system of banking and finance. The emphasis is on providing a seamless experience for the customer and boosting customer satisfaction by eradicating the roadblocks using technology.

With the proliferation of the internet and smart devices, consumer behaviour has changed in general. Consumers are more aware and more demanding today as compared to a few decades ago. The general technological advancement has been such that the need to provide instant solution to the problem is paramount. People are buying goods through the web using their smartphones. Now without the latest technological evolution this wouldn’t be possible, to cater to such growing demands from different industries the finance sector had to level-up and satisfy the needs of the customers. Digital wallet, digital banking, mobile banking, etc. are recent developments in the Finance industry that can be categorised under Fintech.

 

What’s Trending In The Fintech Domain?

The Fintech industry itself has evolved to a great extent in the last few years. With mobile banking and digital wallet being quickly adopted by the masses, some of the latest developments are still to be seen. Let’s delve deeper into the latest trends in the Fintech domain.

Contextual Banking

Contextual banking is the need of the hour, with the increase in hyper-personalization services customers are getting used to more specific products and services. The one size fits all approach is obsolete in the age of AI and machine learning. The contextual banking model is based on providing the right products or services to the customer at the right time and place. With the growth of big data and AI it is getting easier to provide contextual banking to customers based on their historical transactions and other relevant data.

Robo-Advisors

Technology has made it possible to train robots to carry out complex tasks that earlier required human intellect and interference. Robo-Advisor is a recent development in the Fintech industry where robots will provide complex investment and asset management solutions to customers. The advisory services can be availed online and require very minimal to no human involvements. Robo-Advisors factor in the risk-return profile of the customer and then accordingly provide investment solutions.

Robotic Process Automation

Robotic process automation continues to be a major trend in the Fintech segment. It not only helps the Finance companies to be more efficient in their functioning but also helps them to effectively comply with the latest rules and regulations set by regulatory bodies. The automation is not just limited to substituting human labour and performing a task but also includes suggesting improvements to the existing processes.

Blockchain

The recent technological developments have also exposed us to a new league of criminals who are tech-savvy and technologically advanced to break into our online portals. In the contemporary digital world, data is the real deal, identity fraud and theft of data, in general, is a major concern with increasing dependence on technology. Blockchain is being adopted to boost security in the financial services industry, from smart contracts to digital payments and identity management it has a whole lot to offer.

Innovations In The Mobile Payment Systems

With the increased internet penetration and affordable smartphones, the whole world has entered the digital revolution. Transferring payments using smartphone applications has been the norm since the past couple of years and rightly so given the convenience it brings to the customers. The technology however is not static and drives further innovations into this Fintech segment, like for example biometric access control that includes facial and finger print recognition. Other developments include suggesting users on their purchase decisions by factoring in their transaction history. There is much more going on in the mobile payment segment of the Fintech space.

Conclusion

The Fintech industry is still in its nascent phase and is growing by the day. It has proved to be a game-changer in the Finance industry and still has a lot more to offer.

What Is The Quickest Way to Learn Math For Machine Learning and Deep Learning?

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Synopsis

Math is integral to machine learning and deep learning. It is the foundation on which algorithms are built for artificial intelligence to learn, analyze and thrive. So how do you learn math quickly for AI? 


Machines today have the ability to learn, analyze and understand their environment and solve problems on the basis of the data given to them. This intelligence of the machines is known as artificial intelligence and the ability to learn and thrive is known as machine learning. Algorithms form the crux of everything you do in technology and a Machine Learning Course provides you with an understanding of the same. 

 

Today, individuals who are proficient after completely a Machine Learning Certification is highly sought after and employed. Companies invest a large sum of money to have professionals trained in AI as the applications of AI are vast and cost-effective.  It is a lucrative career to pursue one that involves complex and challenging problems that need to be solved in creative ways. 

 

Mathematics forms the foundation of building algorithms as all programming languages use the basics. Binary code is the heart of machines and the language used to teach them things is the programming language. So do you pursue Machine Learning Training, and also learn math quickly at the same time? 

 

Here are a few ways to understand how math is applicable in AI 


Learn the Basics 

Important sections such as  Statistics, Linear Algebra, Statistics, Probability and Differential Calculus are the basics of math that one needs to know in order to pursue learning a programming language. While this may sound complicated, they form the basis for machine learning, so investing in courses that teach the above-mentioned functions will go a long way in programming.  There are plenty of online resources that are useful repositories when it comes to learning math for deep learning. 

 

Invest Sufficient Time

Learning math depends on the ability to absorb and apply the math learned in machine learning. Applications of statistics, linear algebra is important in machine learning and hence investing 2-3 months to brush up on the basics go a long way. Constant applications of the lessons learned also helps when it comes to math for AI. Since the principles are the same but the various derivatives and applications can change with the algorithm constant practice and brushing up will help while learning the code. 


Dismiss The Fear

One of the biggest ways to learn math quickly for machine learning is by dismissing the fear associated with numbers. By starting small and investing efforts, one can move forward in the code. Since there is no shortage of resources when it comes to learning math, taking the initial step and letting go of any fear towards the subject will greatly help. 


Conclusion

Learning a programming language whose principles are based on mathematics can sound daunting and tedious but it is fairly simple once you understand the basics of it. This can be applied while programming for machine learning and artificial intelligence