Financial dashboard with business intelligence BI. Blurred megapolis.
View Preview
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.
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 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.
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 …….
Source: https://www.datasciencecentral.com/an-overview-of-data-analytics-in-investment-banking/