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Average Salary of Investment Banking Analysts in the US

Source: Payscale

As per Payscale, investment banking analysts in the US make $67,817 annually, which is certainly good pay at the entry-level because you don't have any experience by then. You are supposed to learn more, then apply, in the first year of your employment as an investment banking analyst. For a banking aspirant, the best aid available to prepare for the entry-level job roles would be the industry-relevant investment banking certification programs that are available online.

The Role of an Analyst

Investment banking analysts help big corporations with expert advice on large-scale financial transactions, such as issuances (debt & equity), and M&A (mergers and acquisitions). In simple terms, he is the one who does everything that their senior counterparts ask them to do, in connection to closing or winning a deal. You, in the role of an analyst, would be in the assistance' role to associates or senior analysts. The job position demands an amalgamation of financial skills such as deal-making, negotiation, marketing, and sales. Analysts while starting out lack this skill set, but they learn them on the job in the first couple of years, while assisting their seniors.

The Day-to-Day Work Responsibilities of an Analyst

Employment of financial analysts in the U.S. in 2019, by industry (in 1,000s)

Source: Statista

Here are a few primary responsibilities of an investment banking analyst on a day-to-day basis:

• Analysts help their senior counterparts in sourcing information for key investment-specific decisions. They do analyse the information themselves before passing it on to their senior team members.
• Analysts assist their wealthy clients nurture their already-fruitful monetary investments in the financial markets. In this role, you need to understand well your clients' goals.
• You do a lot of market research in order to keep a constant check on the effectiveness of your client's existing market investments. If any of the investments feel like going redundant, it's the analyst's job to inform about the same their clients.
• You will be asked from time to time to analyze and assess an investment opportunity on behalf of the company you work for.
• A majority of analysts specialize in one or the other sub-function of investment banking, i.e. emerging markets, manufacturing, healthcare, among a few others. And hence, the specialized banking analysts take care of the banking operations in their specific area of expertise.

The Three Most Common Banking Operations That Analysts Deal With

Deals

Deal-making involves execution of transactions pertaining to companies being sold or bought. Analysts would be responsible for managing the schedule, the format of excel files, jotting down notes, and preparing PowerPoint presentations pertaining to investment deals.

Sales Pitches

Banking analysts need to prepare sales pitch books that constitute the key attributes of the firm, which is eventually leveraged by the bank's sales teams to help sell services and products and thereby attract new clients. More than often, analysts are asked to draft PowerPoint presentations or a printed pitch book, ready for their senior counterparts to produce before the clients. Analysts would write slides based on industry shifts, executive summaries, and their analysis criteria.

Market Research

An analyst needs to track market trends, perform industry research, and search for financial information on a day-to-day basis. They will be asked by their team leaders and senior colleagues to perform comprehensive research on a specific investment's viability. Additionally, analysts will be asked to provide recommendations and advice on a potential investment opportunity by submitting a written report, or a PPT to his senior team member.

Preferred Skillset for an Investment Banking Analyst

To ensure a rewarding career as a banking analyst, you must acquire the below-mentioned skillset:

• Great communication skills
• Analytical abilities
• Ability to work under pressure
• Investigation capabilities
• Problem-solving skills
• Market research abilities
• Quantitative skills and a keen interest in numbers
• Decision-making capabilities
• Familiarity with working on computers
• Expertise at analyzing and presenting data
• Being an efficient team player
• Drive to succeed and self-confidence
• A keen interest in current affairs, especially associated with the financial world
• Well-defined long-term career goals

Speaking foreign languages does help elevate your career fast. Besides, tech-savviness is rewarded in the investment banking industry, alongside a strong grasp on leveraging social media.

Concluding Words

In totality, the primary role of an investment banking analyst is to support their senior counterparts, and assist them with anything work-related, that they demand for. This might seem like a mundane job profile with little to no opportunities to grow personally in your banking career, but it certainly pays off immensely, once you become a senior banker after spending a couple of years in the said entry-level role.

The post What Does it Mean Being an Investment Banking Analyst appeared first on Datafloq.

Despite the global impact of big data, the focus is too frequently viewed through the lens of developed economies, such as the United States and Western Europe. Although the utilization of big data is clearly more widespread in developed economies, it is also becoming more dominant in emerging markets as well. Policymakers and entrepreneurs in emerging economies must appreciate the contributions made by advances in data technology.

Data analytics can also be useful for stakeholders abroad. They can use insights from data mining to identify financial trading and entrepreneurial opportunities in emerging economies that would have otherwise been overlooked. This can be great for forex traders. They can use data analytics to focus on forex software.

Data analytics can be most beneficial when it comes to identifying the impact of major socio-political events. This can play an important role in anticipating changes in currency prices. Some of the ways that data analytics can be invaluable for forecasting trends in emerging markets are listed below.

Identifying risks and opportunities associated with sovereign trade deals

During the beginning of 2019, Murat Ozemre and his colleague Ozgur Kabaduus from Yasar University published an article on the use of big data analytics for forecasting trade volumes among global supply chains. The research showed that data analytics can be incredibly useful for improving the efficiency of supply chains.

The principles of their findings can be applied at the macroeconomic level as well, particularly in a developing economy. Instead of focusing on the inter-relation between a supplier and end purchaser, they could assess the change for a broader economy participating in a trade deal with another sovereign entity.

Trade partnerships have tremendous implications for all parties involved. However, they tend to have the largest effect on smaller economies, particularly those that are part of an emerging market. A trade deal could move the needle in terms of economic growth for a large economy, such as the United States or Italy. On the other hand, it could cause a substantial stimulus effect or wreak devastation in an emerging economy in Africa or Latin America.

Although the expected outcome is easy to gauge with qualitative data, it is much more difficult to anticipate the exact impact with quantitative data. However, it is possible to use predictive analytics to draw such a conclusion.

Predicting the impact of a change in the political dynamics of a country s
leadership

Leadership changes occur at the national levels of almost every country occur at least once every 20 years. Of course, some of these changes are more significant than others. A regime change in a dictatorship transitioning to a democracy could have earthshaking implications. The election of a new president from the same political party would have much less of an impact.

The impact of leadership changes in emerging economies can be more significant than that of developed economies. These leaders have the power to influence policy to a greater degree, due to existing authoritarian power structures. They also can enhance or stifle optimism in countries that they have formed partnerships with.

Nevertheless, any change in leadership is going to have positive and negative implications for an economy. These changes can have a broader impact on an emerging economy, since these areas are more sensitive to such events.

Predictive analytics can use various models to anticipate the consequences of a change in political leadership. They can look at:

• The net impact from similar types of leadership changes in peer countries within the past 20 years
• The political positions of the new leaders
• Expected responses from business leaders, trade partners and other stakeholders based on poll data
• Levels of support for the new leaders by other politicians

Predictive analytics models try to account for every possible factor in these models.

Predicting the consequences of natural disasters

Natural disasters have considerable implications for national economies, which may be felt for years to come. The exact impact is easier to anticipate with the right data analytics models. It is also easier to predict natural disasters with big data in the first place.

Predictive analytics technology can account for disruptions in the supply chain within a country, as well as between other trade partners. It can also assess the degree to which it reduces or incentivizes innovation, as well as the economic toll of the physical destruction itself. All of these factors have a considerable impact on an economy. They can be even more significant for emerging economies, so it is important to use sophisticated data analytics models to draw accurate conclusions.

The post Data Analytics Assesses Event Impacts on Developing Economies appeared first on Datafloq.

WORD-CLASS GLOBAL SPEAKERS

Hear from over 25+ global innovators and thought leaders in the DeFi space on the disruptive products and services they have built and expert opinions on the challenges that exist including the future of DeFi.

EXCITING TOPICS AND DISCUSSIONS

Two tracks with lots of diverse topics. Experience some of the most innovative DeFi projects and their use cases first-hand. Learn about the latest developments and trends in the space.

NETWORKING

Network with some of the industry's most progressive professionals across industries such as finance, insurance, trading, investment, legal, regulation and more.

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Become one of our DeFi Conference 2020 sponsors and showcase your brand, product and services to our global audience. If you would like to receive the sponsorship prospectus, please click the button below. For any enquiries please email us at [email protected]. The main sponsorship packages are listed below. Prices include vat. Please visit the website for more information regarding each sponsorship and all its benefits.'

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The focus of the event is on financial inclusion, DeFi adoption, compliance and regulation, open finance and security.

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Key Topics

• Global new innovations and trends in insurance analytics and AI
• Implications of big data and AI for innovation, competition and privacy
• Big data and analytics to enhance insurer operations in APAC
• Transform big data analytics into a competitive advantage
• AI applications in insurance: strategies and struggles
• Digitalization with big data and AI in Insurance in the USA
• Strategies in the adoption of big data, advanced analytics and AI in insurance
• AI and advanced analytics in insurance product development
• Insurance actuaries in the age of big data, predictive analytics and AI
• Insurance product development with IoT data and AI
• How analytics and AI enable insurance marketing and business strategy
• Segmented approaches and strategies with data analytics and AI
• AI and machine learning in insurance telemarketing
• AI-based sales agent management
• AI as the enabler across the entire insurance value chain
• The impact and possible use cases of AI in insurance
• Case study– application of AI in underwriting
• Case study– virtual assistant to improve customer services
• Case study– AI for illness and disability claim predictions
• Case study– AI in claims processing
• Case study– fraud detection with advanced analytics and AI
• Innovations in insurance underwriting and claims
• Automating insurance underwriting with big data and cloud
• Big data as a fraud detection tool in medical insurance claims
• Customer-centric initiatives with big data and AI

More Hot Topics'

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I keep things running smoothly between modelers and data analysts'

Ron Keur, Senior Project Manager, Credit Risk Modeling

Ron Keur (36) is the project manager for the team that is developing a new credit risk model for Rabobank. The team collects, interprets, and organizes a huge amount of data for large business clients around the world. The team then uses the data to develop smart algorithms to predict risks.

Sharp, ambitious people

I'm currently working on a mega-project: building a new credit risk model. We do that together with a team of around 50 employees that includes modelers and data analysts, as well as colleagues who are directly involved in providing services to our clients. They're all sharp, ambitious people with a wide range of backgrounds and talents. As the project leader, I'm the lubricant between all of the team members. It's important that they can all communicate effectively with each other, that they understand one another and can clearly explain complex subjects. My responsibility is to make sure that happens.

Estimating risks to stay strong

Credit risk models are vital for a bank, because they let you accurately estimate whether a client is credit-worthy or presents too great a risk to the bank. For example, how likely is it that a large commercial client won't be able to pay back a loan? And if that happens, how much of the loan will we lose? Accurate predictions help keep us reliable and strong as a bank, so it's understandable that so many people at Rabobank are looking over our shoulders to see how the project is progressing. We think that's cool, because it underscores that we're doing extremely relevant work.

Keeping the banking world safe

Another group that are looking over our shoulders are the supervisory bodies for the financial world. So the team is laser-focused on only using the data that we're allowed to use for that purpose. Strict regulations have been drawn up to that effect, which is only natural because those regulations protect our clients and keep the banking world safe. The challenge for us, then, is to build a credit risk model that's accurate and statistically supported within the framework of all of those conditions.

Past is prologue

In order to develop a model like that, we need vast quantities of customer information from the past. That's the power of data: we can make reliable predictions about the future based on data from the past. But first, we have to collect, correctly interpret, and organize the data. That's where our data analysts come in. They can present useful data sets that our modelers can use to build the statistical model. To do that, our data analysts utilize SQL Server, Python, and dashboards to quickly show which data is available, and to provide insight into the quality of the data and how it will be interpreted during the model construction phase.

A model you can trust blindly

Our customer advisors, account managers and financing specialists want to present our clients with an attractive offer for financing. If the model doesn't work properly, and clients are estimated to be riskier than they actually are, then we'll have to ask a relatively high price for our products. And then there's a good chance that the clients will go to another bank to get a better deal. But if the model estimates the risk as lower than it really is, then we won't comply with the supervisory body's regulations. So it's very important that we build a model we can trust blindly. If we can do that, then we'll be able to help our clients faster and more efficiently.

Do you want to create real value for real people? Discover Data & Analytics at Rabobank. Check the possibilities at rabobank.jobs

The post How to Keep Things Running Smoothly Between Modelers and Data Analysts appeared first on Datafloq.

DataCon Africa has become a firm favourite in the African data analytics events calendar in just 2 years.'

Valuable is one way to describe it. Fun is another. DataConians received their own branded (data science) lab coat and giant Corinium Teddy in 2019.'

DataCon is designed to meet the information needs of senior data analytics professionals who are tasked with developing the capability within their organisations.'

2020 has got you covered with our most comprehensive event yet! Our themed tracks will cover'Data Management, Strategy & Operations, Advanced Analytics, Data Privacy & Protection and Data Engineering & Architecture.'

Plus! Dig deep into data governance in the ‘Future Data Governance Focus Day.”

Speaker Spotlight

Diego Iturralde is'Heading up a demography division at the national statistics agency, Stats SA. The major deliverable he is responsible for is of annual population estimates which take all population dynamics into account for such a purpose. In addition to that, he takes a particular interest in issues related to migration, its measurement, analysis and management. He currently serves on positions on structures of the IOM and the UN Statistics Division related to migration and is an advocate for better migration data to quell anti-immigrant sentiment and to inform policy more effectively.'

Diego will be speaking on the'Efficient use of data for maximum citizen experience
It will be a paper that speaks to the use of data for evidence-based policy making with a focus on misinformation around migration fueling popular discourse versus what robust science tells us about this emotive subject.'

CTICC | Cape Town, South Africa”
11 – 12 March 2020 Main Conference'
10 March 2020 Focus Day”
Join the conversation #dataconafrica

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These types of economic forecasts tend to be reasonably accurate. However, it is difficult to cite them with exact precision. A number of conflating factors could reduce or increase the demand for data analytics jobs in the foreseeable future.

Here are some of the factors that could impact future demand for this rapidly growing profession.

A shortage of skilled data professionals could temper demand

Supply and demand for big data professionals is a two-way street. If there is a shortage of people willing and able to fulfill the job requirements, then demand could also stagnate.

Why would this occur? If new startups can't attract reliable data professionals, then they may have to fold. This could lessen the number of big data startups, which in turn would cause the demand for entry-level big data analytics service jobs to decline.

The outlook is not set in stone, but there are valid reasons to be concerned that a shortage of skilled data professionals could threaten the market. According to a report by PwC, the average time that it takes a company to fill a data professional job is 90 days. This is twice as long as the average for all jobs requiring a bachelors in arts degree or graduate degree in the workforce.

Finding data professionals with sufficient expertise to fulfill specific needs is even more challenging. The PwC report states that many employers are looking for data professionals with more holistic skillsets, including creative thinking and communication skills. They have trouble finding these employees, which makes it more difficult for them to realize their ROI goals. This could also limit the demand for these jobs if employers aren't satisfied with the results.

The rate of growth of the IoT

The Internet of Things (IoT) is one of the most impactful technology trends in the 21st-century. Gartner estimates that the revenue that will be generated from IoT devices all over the world will exceed $300 billion by the end of next year.

IoT advances have been made possible due to the application of big data. Big data has played an important role in designing new smart devices and storing consumer data to bolster performance.

The growing importance of IoT is one of the biggest reasons that the demand for data scientists is rising. If the IoT infrastructure plays an even more important role in our future economy than Gartner forecasts, then the demand for data scientists will be greater than IBM and other organizations anticipate.

Calls for regulatory frameworks

Last month, Elizabeth Warren called for stricter regulation of technology companies in Silicon Valley. Many pundits pointed out that major tech companies have largely escaped the scrutiny of regulation.

The likelihood of a new regulation seems virtually inevitable. The question for aspiring data scientists will be what the implications will be for their future employment.

That is a difficult thing to predict. However, if I were to venture a guess, I would say that regulation in the technology sector would likely increase the demand for data scientist jobs more than reduce it. Why would expanding the regulatory framework for the technology industry increase the need for data analysts and data engineers? It would require organizations to retain more records for compliance purposes. They would also need to continually develop more effective encryption and security tools, which also require the contributions of data professionals.

Many factors affect the outlook for data professional jobs

Numerous experts agree that the demand for data engineers and data analyst is growing faster than any other profession. However, making exact estimates for the number of jobs that will be needed is clearly more difficult. There are a number of different trends that are going to shape the market for this profession. We will need to see the figures as time progresses.

The post 3 Factors that Will Alter Demand for Data Analysts Over the Next Decade appeared first on Datafloq.

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1. Incorporate BI into the entire organization

BI can be used to serve any part of your organization. It should not be confined to a specific department. Using it in one area of the firm may cripple other departments as the other areas grow. The strength of a team is dictated by their weakest player. Therefore, use this technology in all the teams in your company to get the most out of its services. All the departments in your firm should make decisions backed by data. Making these tools accessible to all the departments is a step in the right direction.

2. Automate

BI will only analyze the data that is provided. If you avail inaccurate data, you will get incorrect analysis. It is thus vital that you give data that is accurate. You do not want to make significant decisions based on inaccurate information. First, consolidate the data available in a standard location then format it before using BI software. An automated process will aid in getting accurate information from the operation of consolidating and formatting. Doing it manually will result in errors, inaccuracy and time wastage. Unfortunately, manual processes are conventional in most businesses where people extract data and transfer it manually to Microsoft Excel. Automate your processes to get the most out of business intelligence.

3. Use self-service

In the past, the IT department used to prepare reports for other departments to use. This process was frustrating and time wasting as other teams had to wait on the IT team to finish analyzing. This workload would increase the chances of making errors and limit productivity. Companies that still use this process need to change their ways immediately if they want to be competitive in the ever-changing marketplace. Incorporate self-service tools in all the teams for efficiency. Train the end users on how to operate the BI systems. It may be costly but will be worth it in the long run. Accessing data on time will prove invaluable to the business.

4. Install BI on all devices

The world we live in has become mobile. Majority of the people have mobile phones and tablets which they use to access information wherever they are. Acquiring software that is compatible with the mobile phone will increase the productivity of your team. A good example is when a member of the sales team may need to access a dashboard while on fieldwork to make an informed decision. It is a challenge to predict when someone may need access to data. Availing analysis on mobile devices will help solve this challenge. Do not install BI software on desktops alone. Extend its access across all the tools that are available.

5. Incorporate external data

Do not limit business intelligence to internal data. Use external data to get more insights into your business. You can use external data on potential customers to come up with methods on how to convert them to active clients. For instance, you can use data from the weather forecast to estimate its effect on sales in your organization. External sources of data are beneficial when combined with internal data. You may avoid surprises that come with the drastic changes in the marketplace if you incorporate external data in your analysis. Use software such as Amazon Redshift to analyze big data that is a common feature in the sources of external data.

Conclusion

Use BI to scale the heights of success in your industry. The tips highlighted above will come in handy when using this technology. Thanks to business intelligence, running business has become easier.

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