An Overview of Data Analytics in Investment Banking –

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With the transformations of digitalization, investment opportunities have become accessible to everyone. The opportunities to invest your money are diverse, ranging from stocks and gold to investing in information technology (IT). As technology improves, the traditional way of supporting and engaging in any financial transaction is rapidly changing. Capital markets are the main pillars of the global economy. They bring together qualified finance, IT and economist professionals to make the best investment decisions and choose the perfect financing solutions. Optimizations and innovations have a huge financial impact, to better deal with this, data analysis in investment banking plays an active role.

In this article, let’s discuss how data analytics in investment banking is transforming the way investment banks operate, the challenges they face when engaging in this transformation process, cases of use, etc.

Data Analysis in Investment Banking

Analytics is a buzzword that is used everywhere and in various contexts. According to a recent survey by Atos, “66% of banking executives consider the transformation of the digital customer experience to be a top priority for the coming years”. Several research articles have been published on several international platforms, which clearly indicate that investment bank can derive the maximum benefits with the analysis.

Data analytics in investment banking is the result of a rigid business climate that has led to low returns compared to ancient times. Over the past few years, the financial sector and capital markets have experienced a few years of flat incomes on the back of falling margins and increasing regulatory complexity. Additionally, the Fixed Income, Currencies and Commodities businesses, which have historically accounted for the largest share of revenue, are facing a critically declining share for the same reasons.

  • Ways Investment Banking Uses Data Analytics

Data analytics has therefore created its place at the center of investment banking as it ensures better returns in a more deliberate manner.

Investment banking is where resources are heavily invested in risk, as the consequences of misjudging risk could be devastating. The financial crisis of 2008 and its impact on the world economy is the perfect example to describe the major role of this profession. To manage these risks, banks use data analytics tools to detect when the likelihood of default on loans is higher, allowing them to act quickly before things get out of hand. This applies to all kinds of risks. They are:

Reducing fraud is a common goal for investment banks. Data analytics can be leveraged to identify patterns of fraudulent transactions or atypical transactions to manage risk, and also alert appropriate personnel to investigate further instead of just detecting fraud.

Data analytics is useful for identifying and assessing individual customers who pose a risk of fraud, and then applying different levels of monitoring and verification to those accounts. Account risk analysis helps investment banks know what should be prioritized in their fraud detection efforts.

  • Liquidity and operational risk

Liquidity risk is macro, such as fluctuations in interest rates, changes in 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 cover its liabilities.

Liquidity risk arises when the availability of funds is insufficient. This may be due to bad debts (which may never be repaid) or lower than expected cash flow (which includes lower income/deposits). This is mainly risky for banks because their funding inputs are usually deposits, which are paid as net interest.

Operational risk describes the potential for loss due to actions taken by the business. These are possible losses that result directly from the risks associated with day-to-day operations, i.e. fraud, theft, computer security breaches, error in judgment or incompetence of an executive. superior.

Data analytics is used to track short-term and long-term liquidity at all times, they also assess the impact of trades on real-time liquidity and regularly run simulations and stress tests to ensure that the funds necessary for the operation of investment banks with precision.

Investment banks rely on analytics to manage the risk associated with the loans they make. This is done by monitoring the data they collect on individual customers. This data may have the following elements, but is not limited to:

  • Customer credit score
  • Credit card usage (how much you owe)
  • Amounts owing on various credit cards (total debt)
  • Amounts due on various types of credit (total debt/total credit)

Credit risk analysis is the analysis of past data to collect the creditworthiness of the borrower or to assess the risk associated with granting the loan. Where internal data on customers and counterparties are brought together with external data from the web, social media and news to get a comprehensive idea of ​​their financial situation and ensure that contingencies are well controlled. The results of this analysis will help investment banks analyze their risks and those of their clients.

  • 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 rates) that changes in response to various scenarios. When risk modeling is performed accurately and consistently across all assets, overall portfolio risk can be reduced and portfolio performance improved. Risk models are used in several areas with financial institutions to obtain the risky aspects

Sentiment analysis plays a role here to better understand customer demands and respond to them accurately. Data available on the web, including news, social media, research reports, and corporate websites, provides insight into the customer. The anticipation with which the customer may or may not appreciate, and guide him towards the most suitable products (cross and up-selling) at the right time. It also ensures better customer loyalty, appeal to them, and also makes attracting leads a more efficient process.

Data Analysis in Investment Banking offers massive and in-depth monitoring where patterns of incidents and problems are identified using machine learning (ML) algorithms. This makes manipulation and resolution a much easier process.

An Overview of Data Analytics in Investment Banking

Challenges Investment Banking Faces in Being Data-Driven

Investment bankers are tackling a myriad of data and productivity challenges, especially managing the demand side of the equation, which is record-breaking activity.

One of the main challenges that people who want to excel in the career of investment banking must know about this – investment banks face early in the use cases of analytics is prioritizing them. In the use cases listed in the above section, there are many interdependencies between the use cases because they mostly rely on the same data: a mixture of internal transactions and operations with the market and economic data . Thus, knowing and deciding on which use cases to opt for in priority is a question of business priorities and also a question of technical constraints, linked to the availability of data.

The initial point leads to the second concern of data analytics projects in the field which is good that all data is accessible. Capital Markets Analytics lies in the precise combination of internal and external data which is not always available in internal databases and is instead present on data providers’ platforms, social media and entity websites. regulators, ministries, national agencies and customers.

The massive amounts of investment banking data can end up being processed due to the vast scope of external data required for analysis, the nature of the data repository/data used can also be a difficult decision to make, and which has a significant long-term impact and price analysis initiatives. There is a critical trade-off between tight regulation of investment banks and the confidentiality of the data they need and the big data in investment banking volumes to be processed, a large part of which is mostly public and already present to everyone on the net.

One of the challenging situations is the technical and technological architecture of the environment hosting the data analytics use cases. The particularity of the external data necessary for investment banks is that they are strongly presented in several files (pdf, word, or excel) of small or medium sizes since each publication is available in a separate document. Block storage, which is widely used in data analysis in all other fields, is not the preferred option because it is not initially designed for storing small files.


A career in investment banking is a global, high value-added and highly competitive banking sector. Like every other industry, it relies heavily on data analytics not only for competitive advantage but also for routine functions. Through the use of data analytics for solutions, investment bankers can reduce repetitive and manual work and use their time and energy in high value projects.


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