Data Analytics In Investment Banking

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The Client Experience Evolves

Financial Analytics with Python | Financial data Analysis using Python | Great Learning

Client expectations have evolved alongside digital transformation. While financial services industries have been somewhat notoriously known for digital resistance , clients have long been demanding experiences that align with those offered by the consumer brands they interact with.

The pandemic eliminated any resistance banks may have wanted to maintain to offer a fully digital experience. In 2020, banks were forced to move operations and core client communications fully online, things previously deemed impossible not to do without at least some true face-to-face interaction.

A 2021 survey conducted by digital solutions provider Atos included 400 retail banking leaders across North America and Europe. Responses showed that 66% of banking leaders consider transforming the digital client experience a top priority for the next year.

Digital disruption is not new to the banking industry, said Adrian Gregory, Global Head of Financial Services & Insurance at Atos, technologies advance and hungry market entrants are emerging all the time. What has changed, however, is the massively accelerated shift to digital channels as a result of Covid-19.

Investing not only in the ability to provide a digital client experience but to evaluate what the resulting data says about future trends is the key to staying a step ahead of industry competitors and ensuring analysts come to important conclusions faster.

Mass Migration To The Cloud

Dont try and do this softly,says Yolande Piazza, former CEO of Citi Fintech and current Google VP of Financial Services. Now is the time to rip the Band-Aid off.

This is her advice to banks thinking about adopting cloud capabilities and we agree.

For banks to take advantage of the many benefits digital transformation and big data have to offer, the migration of that data to a centralized location will be absolutely necessary.

From an internal perspective, the past 18 months have demonstrated how essential it is that analysts can access information to work from anywhere, as the workforce has shifted to fully remote .

Offering analysts a centralized location to search for reputable data, helps them quickly source, aggregate, validate and share information with their clients, resulting in stronger relationships. and Salesforce have already recognized the opportunity and gotten in on the game, offering cloud solutions specifically tailored to the financial services industry for better cloud capabilities.

Beyond a centralized location, groundbreaking AI technology has been built to understand financial language and allow analysts to move faster, capturing all the relevant information around a topic that a simple CTRL+F cant match.

For investment banks, these solutions and others like them, mean faster and more secure sharing of market data, smarter portfolio management, and better client services.

The Future Of Big Data In Banking Looks Bright: Make Sure To Keep Up

As you can see, there are many examples of how big data is used in banking. Yet, all those attempts have barely scratched the surface. The maximum potential of big data in banking is still to be harnessed.

According to the whitepaper by Global Transaction Banking, 62% of banks agree that big data is critical to their success. Yet, only 29% of them report getting enough business value from their data.

Banks need to rethink their operations and adopt data-driven approaches if they want to stay relevant and competitive. Plus, big data in the banking sector can help you improve and grow your business.

If you are looking to explore this opportunity but are struggling to find appropriate big data applications in the banking sector for your business, we at Eastern Peak can help you out.

Our team has vast experience implementing fintech products of different complexities as well as building big data solutions from scratch. Among other projects, we helped Western Union implement an advanced data mining solution to collect, normalize, visualize, and analyze various financial data on a daily basis.

So, if you want to discuss opportunities and big data implementation options in banking, call us now at +1.646.889.1939 or request for a personal consultation using our contact form.

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Better Employee Performance And Management

Big data solutions in banking allow companies to collect, make sense of and share branch performance metrics across departments in real time. This means better visibility into the day-to-day operations and an elevated ability to proactively solve any issues.

A global banking provider, BNP Paribas, collects and analyzes data on its branch productivity to identify and swiftly fix existing problems in real time.

Using the companys data analytics software, branch managers, as well as chief executives, can get a birds-eye-view on the branchs performance based on a number of metrics, i.e. customer acquisition and retention, employee efficiency and turnover, etc.

Data Science Is Becoming A Business Role

Big Data analytics in the banking sector

Data science is an umbrella term used for a lot of roles involving data.

But if you subtract from it other discrete jobs like AI research, ML engineering, data engineering, and software engineering, what remains looks a lot like a business/analytics role.

Conceptualizing problems, interpreting data, visualizing results and presenting incites to non-technical stakeholders. In a way, this is what investment bankers do.

As data science matures and analytical infrastructure arrives out-of-the-box via SaaS, well see this accelerate. Technically-interested people may opt for software engineering instead, while MBA-types fill the gap in data science.

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The Growth Of Big Data In Investment Banking

Data is a topic that has remained in the spotlight throughout the year. Its one reason why weve seen IHS Markit in the news around several developments, including S& P Globals recent purchase of the data giant for $44bn. This follows IHS Markits own acquisition of Ipreo in 2018.

Separately, although the London Stock Exchange Groups purchase of data provider Refinitiv isnt a done deal yet, it is prepared to spend $27bn. LSEG expects to triple its revenues and gain a huge competitive boost in areas that require large volumes of market data, such as ESG and other areas of the automated trading landscape.

How To Structure The Analytics Team

We are going to elucidate on the approaches in structuring the analytics team.

1. Decentralized approach: It involves in embedding a small analytics team within each department of an organization. In this model, a group of Business Risk Analysts would report to the Chief Risk Officer and Marketing Analysts would report to the Marketing Head. This way, each group excels in its own domain expertise over a time period.

2. Centralized approach: This approach involves having a center of excellence for analytics. It consists of a group of Data Scientists who does all the important work. Best work practices are easier to share, and all Data Scientists are exposed to new skills quicker than when they would work in smaller teams as in the decentralized approach.

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Join Savvy Investor And Keep Up

The flipside to exponential growth of data, of course, is the everyday problem of data overload that we all face. At Savvy Investor, we try to make life easier for institutional investors by curating the best papers, reports, news and blogs from around the web, and then providing a tailored experience to members who specify the topics they wish to follow. Why not register today?

Data Analytics Trends In Investment Banking

What do Industrial Engineer’s specialize in? Data Analytics + Investment Banking

AlphaSense

12 min read

Digital transformation, long-emerging, has officially arrived in investment banking. Driving this transformation is the power of data analytics, giving institutions more information and insight than ever before.

JP Morgan recently announced that they are hiring 4,000 new employees specializing in digital skills like cloud technologies, big data, and cybersecurity. Financial institutions are expanding their executive leadership teams to include more technical experience and expertise.

After more than a year of accelerated digitalization, clear data analytics trends are emerging for investment banks.

Here are five data analytics trends we have our eyes on.

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What we have done

Big Data Is Getting Too Big

With so many different kinds of data and its total volume, its no surprise that businesses struggle to cope with it. This becomes even more obvious when trying to separate the valuable data from the useless.

While the share of potentially useful data is growing, there is still too much irrelevant data to sort out. This means that businesses need to prepare themselves and bolster their methods for analyzing even more data, and, if possible, find a new application for the data that has been considered irrelevant.

Despite the mentioned challenges, the advantages of big data in banking easily justify any risks. The insights it gives you, the resources it frees up, the money it saves data is a universal fuel that can propel your business to the top.

The question is how to use big data in banking to its full potential.

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How An Investment Banker Can Benefit From Ai Data Science

Big Data analytics in the banking sector

Investment banking is part of the banking industry, and its primary business is sourcing and managing capital on behalf of other companies and businesses. Investment bankers are actively engaged in the planning and launch of Initial Public Offerings , Mergers and Acquisitions and various other big-ticket deals in the business and corporate world.

For investment bankers, there is a lot at stake. In the first nine months of the current year, global mergers and acquisitions have topped $4.5 trillion from over 40,000 deals. All these deals and exchange of money were made possible by relentless effort and data-crunching by investment bankers.

In the banking and finance sectors, data is essential to the business. But when you consider investment banking, data is all the more critical. It is what makes the job of decision-makers in this industry all the more complex.

For example, if a company is seeking an M& A deal, it will appoint an investment banker whose responsibilities will include creating appropriate financial reports, seeking eligible partners, preparing blueprints, investment ideas and the financial details of the deal. They have to analyse huge amounts of data and draw insight from it.

This brings data science and other tools of Artificial Intelligence into the picture.

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Are Investment Bankers Ready For Data Science And Digital Tools

Investment bankers can use Artificial Intelligence to work smartly. AI is a combination of data science techniques, machine learning advantages and data analytics insights. But for them to use Artificial Intelligence, there can be a steep learning curve to overcome. For example, anyone who wants to make sense of big data must have a background in computers, mathematics or statistics. Some knowledge of computer programming is one of the desired skills, if not downright mandatory.

However, once investment bankers know their way around data science and digital tools, they will witness a boost in their productivity and efficiency.

What Is The Job Outlook For A Data Analyst

The BLS did not break out the data analyst position in its latest forecasts, but the broader “financial specialist” job market is expected to experience a 6% growth between 2018 and 2028. In the near future at least, strong demand should exist for quantitatively inclined professionals who can cull pertinent information from large pools of data and use it to draw inferences and make forecasts.

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The Importance Of Big Data In Banking: The Main Benefits For Your Business

According to the study by IDC, the worldwide revenue for big data and business analytics solutions is expected to reach $260 billion by 2022. This year, the projected numbers will hit $166 billion, up 11.7% compared to 2017.

It comes as no surprise that banking is one of the business domains that makes the highest investment in big data and BA technologies.

The benefits of big data in banking arepretty clear:

  • Big data gives you a full view on your business: from customer behavior patterns to internal process efficiency and even broader market trends. This means you can make informed, data-driven decision and, subsequently, obtain business results.
  • It allows you to optimize and streamline your internal processes with the help of machine learning and AI. As a result, you get a significant performance boost and reduced operating costs.
  • Big data analytics in banking can be used to enhance your cybersecurity and reduce risks. By using intelligent algorithms, you can detect fraud and prevent potentially malicious actions.
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    The Bigger The Data The Higher The Risk

    Secondly, where theres data theres risk . It is clear that banking providers need to make sure the user data they accumulate and process remains safe at all times.

    Yet, only 38% of organizations worldwide are ready to handle the threat, according to ISACA International. That is why cybersecurity remains one of the most burning issues in banking.

    Plus, data security regulations are getting stringent. The introduction of GDPR has placed certain restrictions on businesses worldwide that want to collect and apply users data. This should also be taken into account.

    Putting Data Analytics And Ai At The Center Of Your Strategy

    Data Analytics Investment Banking Ppt Powerpoint Presentation Ideas ...

    Investment banks have more information than ever before at their disposal.

    Analysts and associates can eliminate redundant research and save their firms annually on wasted research-related costs. With the cost of research high and the underlying process slow, banks can increase their efficiency through an emphasis on data analytics, resulting in happier clients, stronger real-time decision-making, and more meaningful insight extraction.

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    Seriously Why Not Data Science Instead Of Ib/pe

    • A data scientist makes 41K pounds for 40 hours of work, while a banker make 58K pounds on average in London for 80 hours of work . This means that a data scientist makes 20 pounds an hour, while a banker makes 14 pounds an hour

    • Data Scientists arguably have a better chance of starting their own business as they 1) have expertise in a new technology that has lots of potential, 2) have a lot more free time to spend on side projects. Actually, let me rephrase 2) – a Data scientist has time for side projects, while a banker barely even has time to sleep.

    So, why is everyone here chasing IB/PE instead of Data Science? What am I missing?

    Business Process Optimization And Automation

    Further research from McKinsey reveals that around 30% of all work in banks can be automated through technology, and the key to this lies in big data.

    As a result of advanced automation, banks can experience significant cost savings and reduce the risk of failure by eliminating the human factor from some critical processes.

    JP Morgan Chase & Co. is one of the automation pioneers in the banking services industry. The company currently employs several artificial intelligence and machine learning programs to optimize some of their processes, including algorithmic trading and commercial-loan agreements interpretation.

    One of its programs, called LOXM, relies on historical data drawn from billions of transactions enabling them to trade equities at maximum speed and at optimal prices, reports Business Insider. The process has proven to be far more efficient than both manual and the automated trading used earlier, and resulted in significant savings for the company.

    Another data-based automation initiative from JP Morgan Chase is known as COIN. The machine learning algorithm, powered by the companys private cloud network, is used to reduce the time needed to review documents: this task which previously required about 360,000 hours of work, now takes just a few seconds to complete.

    The program also significantly decreased the human error associated with loan-servicing.

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