Data analytics is an essential area for the successful running of investment banking. Gain good knowledge of it to excel in the investment banking career

1. An overview of - Data
Analytics in
Investment Banking
Sep 09, 2022| Article
With the transformations in digitalization, investment opportunities have become
accessible to all. The opportunities to invest one’s money are diverse, ranging
from stocks and gold to investing in Information Technology (IT). As technology
enhances, the traditional way of supporting and engaging in any financial
transaction is quickly changing. Capital Markets are the key pillars of the global
economy. They gather skilled finance, IT professionals, and economists to get the
best investment decisions and choose the perfect funding solutions. The
optimizations and innovations have a huge financial impact, to tackle this in a
better way data analytics in investment banking plays an active role.
In this article, let’s discuss how data analysis in investment banking is
transforming the way investment banks work, the challenges that they get when
engaging in this transformation process, use cases, and more.
Data Analytics in Investment Banking
Analytics is a buzzword that is used everywhere and in various contexts.
According to a recent survey from Atos, “66 percent of banking leaders consider
transforming the digital client experience a top priority for the coming years.”
Several research papers were published on several international platforms, which
clearly state that investment banking can reap maximum benefits with analytics.
Data analytics in investment banking is a result of a rigid conjuncture that led to
weak returns compared to older times. In the last few years, the financial sector
and capital markets have witnessed a few years of stagnation of revenues
provided to the fall of margins and the growing complexity of regulations. Also,
the Fixed Income, Currencies, and Commodities business, which have historically
filled the greater share of revenues, face an essential share declining for the
same reasons.
Ways Investment Banking uses Data Analytics
Data analytics has therefore created its place at the center of the investment
banks’ as it ensures better returns more deliberately.
Better Risk Management
Investment banking is the area where resources are heavily invested in risk
because the consequences of a bad risk assessment could be devastating. The
2008 financial crisis and its impact on the global economy is the perfect example
to describe the major role of this business line. To manage these risks, banks use
data analysis tools to detect situations where there is a higher probability of
defaulting on loans which gives them to take early action before things get
uncontrollable. This applies to all kinds of risks. They are:
Fraud detection
Fraud reduction is a common objective for investment banks. Data analytics can
be leveraged to identify patterns of fraudulent transactions or atypical
operations to manage risk, and also alert the appropriate personnel to
investigate further instead of just detecting fraud.
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2. Data analytics is helpful to identify and rate individual customers who are at risk
of fraud and then apply various levels of monitoring and verification to those
accounts. Analyzing the risk of the accounts gives investment banks to know
what needs to be prioritized in their fraud detection efforts.
Liquidity and operational risk
Liquidity risk is macro, such as interest rate fluctuations, changes in foreign
exchange rates, and changes in the value of other financial instruments, such as
bonds. It is the threat that a bank's assets will fall below the amount needed to
get its liabilities.
Liquidity risk occurs when the availability of funds is inadequate. This can be
caused due to bad loans (which may not ever be repaid) or lower-than-
expected cash flows (which include lower-income/deposits). This is mainly risky
for banks because their funding inputs are usually deposits, which are paid out as
a net of interest.
Operational risk describes the potential for loss due to actions taken by the
business. They are possible losses that result directly from risks associated with
day-to-day operations, i.e., fraud, theft, computer security breaches, or error in
judgment or incompetence at an executive level.
Data analytics is used to keep track of the short and long-term liquidity every
time, they also assess the impact of transactions on liquidity in real-time and run
simulations and stress tests regularly to make sure that the required funds for
investment banks to function accurately.
Credit risk
Investment banks take help from analytics to manage the risk associated with
the loans they make. This is done by monitoring data they collect on individual
customers. This data can have the following, but it is not limited to:
Customer credit score
Credit card utilization (how much you owe)
Amounts owed on various credit cards (total debt)
Amounts owed on various kinds of credit (total debt/total credit)
Credit risk analysis is the analysis of earlier data to gather the borrower's
creditworthiness or to assess the risk involved in providing the loan. Where
internal data about clients and counterparties is gathered with external data
from the web, social media, and the news to get an exhaustive feel of their
financial situation and ensure that the hazards are well managed. The results of
this analysis will help investment banks to analyze their risks and those of their
customers.
Risk modeling for investment banks
Risk modeling is the process of simulating the portfolio of assets (stocks, bonds,
futures, options, etc.) or a single asset (interest rate) moves in response to
various scenarios. When risk modeling is done accurately and consistently across
all assets, one can reduce the portfolio's overall risk and enhance its
performance. Risk models are used in several areas with financial institutions to
get the risky aspects
Loyal Customer
Sentiment analysis plays a role here to better understand the demands of the
customers and address them accurately. The data available on the web,
including the news, social media, research reports, and corporate websites, gives
better ways to know the customer. The anticipation with which the client might
or might not appreciate, and direct them to the most suitable products (cross
and up-selling) at the right time. This also ensures enhanced customer loyalty,
pleasing them and also makes attracting prospects a more successful process.
Secure Ecosystem
Data analytics in investment banking offers massive and thorough monitoring
where patterns of incidents and issues get identified by using Machine Learning
(ML) algorithms. This makes the handling and resolution a much easy process.
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3. Challenges that Investment Banking faces to be
data-driven
Investment bankers tackle a myriad of challenges related to data and
productivity, particularly dealing with managing the demand side of the equation,
which is an all-time high activity.
Use Case Prioritization
One of the main challenges people who want to excel in investment banking
career must know about this – the investment banks face when beginning
analytics use cases is to prioritize them. In the use cases listed in the above
section, there are so many inter-dependencies between the use cases because
they mostly rely on the same data: a mix of internal deals and operations with
the market and economic data. Thus, knowing as well as deciding on what use
cases one need to opt for first are a matter of business priorities and also a
matter of technical constraints, related to data availability.
Data Availability
The initial point leads to the second concern of data analytics projects in the
area that is good that the whole data is accessible. The analytics in capital
markets lies in the accurate combination of internal and external data that is not
always available in internal databases and is rather present in data providers’
platforms, social networks, and websites of regulatory entities, ministries,
national agencies, and clients.
Cloud Integration
The massive amounts of data investment banking may end up processing due
to the vast scope of external data required for analytics, the nature of the data
repository/ data used can also be a tough decision to make, and one that
significantly impacts the long run and price of the analytics initiatives. There is an
essential trade-off to make between the strict regulations on investment banks
and the data confidentiality they need and the big data in investment banking
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4. volumes to process, in which a big chunk is mainly public and already present to
each one on the net.
Data Architecture
The technical and technological architecture of the environment hosting data
analytics use cases is one of the challenging situations. The peculiarity of the
external data required for investment banks is that it is highly presented in
several files (pdf, word, or excel) of small or medium sizes since every publication
is available in a separate document. Block storage, which is highly used in data
analytics in all the other domains is not the preferred option as it is not initially
designed for the storage of small files.
Summing up
An investment banking career is a global, high-value, and highly competitive area
of banking. Like all other sectors, it is based heavily on data analytics, not merely
for the competitive edge but also routine functions. With the use of data
analytics for solutions, investment bankers can cut down on repetitive and
manual work and use their time and energy in high-value endeavors.
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