Machine learning (ML) is a technology which allows systems to conceptualize and imitate human skills such as logical reasoning, learning, decision-making, and language processing. It’s becoming an incredibly popular technology in finance and many other sectors that rely on complicated patterns of data.
One of the ways it is being implemented is through investment banking. This article gives you a simple introduction to ML in investment banking.
What Is Machine Learning In Investment Banking
Machine Learning in Investment Banking can be implemented by a number of ways, but it is most commonly used to analyze or predict trends within an organization.
For example, many investment banks use ML to create forecasts or buy signals for the company they are advising. The nature of ML is such that it can be applied to almost any field and given enough data, one can make meaningful predictions.
By 2030, banks all around the world will be able to cut expenses by 22% by utilising artificial intelligence technologies, predicts research firm Autonomous Next. Savings may amount to $1 trillion.
Machine Learning in Investment Banking is already being used extensively by a number of investment banks and companies across different industries. The use of ML in the financial sector has been particularly disruptive, in particular due to its ability to process enormous amounts of data.
Benefits Of Machine Learning In Investment Banking
As machine learning algorithms continue to improve, financial companies are looking to adopt them into their risk management practices. Machine learning applications have the potential to revolutionize the investment banking industry by providing more accurate and timely risk analysis.
60% of all professionals with the ability to design AI systems work for financial institutions. Predictive analytics is being used by financial services firms to help them make better decisions about their customers.
By using machine learning solutions, these firms are able to create customer support systems that can more accurately predict customer behavior. This allows financial services organizations to provide better customer service and support.
1. It Is Not A Devouring Machine
The process of data science is not defined by the machine learning or mining. It is the process of understanding and analyzing patterns of information about an organization, individual, or an event. So the machine learning does not need to carry out any specific task.
But it can learn slowly, but with high accuracy, which eliminates human error in making decisions. As it learns with every experience set and based on previous information and new events that you provide it with, it can provide you more accurate predictions than before.
2. It Is A Non-Discriminatory Agent
Machine learning is not to discriminate. It doesn’t have any pre-conceived ideas about the person or organization that it is analyzing.
For example, it does not matter whether you are an Asian, African, Caucasian or American to the machine learning, since it does not judge people based on their color or culture. This is extremely important for investment banking since all people need to be treated equally and fairly without any bias.
3. It Works Round The Clock
It is expected that face recognition technology will be used in the banking sector to prevent credit card fraud. In 2020, face recognition technology’s annual revenue growth rate will increase by more than 20%.
The machine learning will work round the clock when the client gives the data set for analysis. The machine will not take breaks and holidays like human workers do. It will be working for whatever period is necessary to produce the final results.
This eliminates errors that come from time limits and force the people who have to work on a specific project to take time off.
4. It Is Transparent
The machine learning technology can be transparent. The process of data science is also transparent and there are no hidden charges involved in it.
In case of an instance where a machine learning technology fails, the client can always go back to human workers and question them about whether they provided any incorrect data or not. This eliminates any kind of unfairness and allows great accuracy without any unnecessary errors in terms of data collection or analysis.
5. It Is Robust
The machine learning technology is extremely robust. It can handle any amount of data and can process it with great precision. Even if there is less data than expected, the machine learning will still provide the most accurate prediction outcome.
The way a machine learning technology forecasts further makes it extremely valuable in a finance industry where everyone has huge amounts of data to process.
6. It Is Highly Efficient
The machine learning technology does not waste much time in going through all data set, unless it requires more than what was given to it for analysis.
One of the factors that make the machine learning technology highly efficient is that it does not require huge amounts of human time, unlike people who have to analyze data. Also, the machine learning can cover many data sets at once without wasting time on analyzing them.
7. It Gives High Accuracy
As of 2020, the most popular application of artificial intelligence (AI) among investment banks worldwide was machine learning. Following closely behind with 60 and 58 percent of the respondents, respectively, were technology for virtual assistants and predictive analytics.
The machine learning technology has very high accuracy in terms of predictions and forecasts. As it learns from previous experience set, it can control and predict future events with greater accuracy than ever before.
Even if you give the machine predictions which are not accurate compared to human forecasts, it will still be more accurate than those issued by a person.
8. It Is A “Living” System
The machine learning technology is not like a computer that has been designed in the past and it will still be functioning the same way after many years. As it develops, it can process data with more accuracy.
Also, as it receives new information and experience set for analysis, it can handle that better without any issues. This makes Machine Learning in Investment Banking as a “living” system that develops and grows with time.
Risks Of Machine Learning In Investment Banking
Global financial markets have come under increased scrutiny in recent years, as financial institutions have been accused of money laundering and other illicit activities.
In response, many financial institutions have turned to artificial intelligence (AI) to help detect and prevent these activities. AI is also being used by investment managers to make better investment decisions.
Due to the Cambridge Centre for Alternative Finance and the World Economic Forum, many financial services organisations say they have embraced technology in areas like risk management (56%) and revenue generation through new products and processes (52%).
1. It Is An Unknown Factor
Human error is one risk that machine learning cannot eliminate. It can become more accurate, but it cannot stop humans from making mistakes in the process of gathering data.
If a human analyst makes a mistake, the machine learning can be less accurate than expected. This means that there are still risks associated with the job of mining data and analyzing it to get predictions in investment banking, if the predictions are based on incomplete or incorrect data sets.
2. Data Set Can Be Unreliable
Another factor that makes machine learning less reliable is that you have given it data sets which have inconsistent information.
For example, the data sets that you provide to the machine learning for analysis can be based on different people who have come up with their information. So if there are any contradictions in the data sets and they are recorded incorrectly, then these data sets will affect all other prediction results.
3. It Is Not Independent
The machine learning technology cannot work independently. Since it works on a constant basis and learns from experience set all the time, it relies heavily on its environment which means it has to get caught up with the company or organization that is using the machine learning technology and learn from them as well.
So instead of using its own learning system, it will be constantly working alongside what would be a superior developed one.
4. It Cannot Make Changes
As a machine learning technology is trained to do one particular task instead of many, it cannot make changes as and when required.
For example, if it is training and analyzing risk management patterns and predictions at the bank, then it cannot be used or applied to other departments.
This can make it hard to manage the technology that is supposed to manage the business, which can result in loss of time and money on the part of the company or organization that is using the machine learning technology.
5. It Can Be A Risk For Privacy Information
The machine learning technology can be a risk for privacy information in investment banking. This is because the company or organization that is using the machine learning technology will be able to identify and track down in a lot of information, which will go against privacy regulations in banking and insurance.
There are several reasons for this. One of them being that not only does it give you access to private information, but it also makes it easier to identify the people who made use of these predictions or the people who had access to private data sets.
6. It Can Be A Risk For Business And Personal Safety
There can be risks of business and personal safety from using machine learning technology in investment banking . If a machine learning technology is given access to confidential business data, it can pose great risk to the business as well as the clients.
Also, if the predictions and analysis that are done by the machine learning technology goes wrong and incorrect predictions are made, there can be personal safety issues as well.
For example, if financial losses are forecasted based on a dataset which includes inaccurate information on how much money is going to be spent on holidays or other events, who will suffer the consequences?
7. It Can Have A High Cost
The higher cost of using machine learning technology in investment banking will make it less efficient than usual. This is because it will cost more money to conduct research before giving the machine learning technology input.
However, despite this, it will also make it difficult to keep up with the revelations or innovations that are also happening in the world of machine learning.
Since it will take time to know whether using machine learning technology is a good idea or not, it can be said that using this technology can have a high cost.
8. It Can Be An Unregulated Technology
According to Insider Intelligence’s AI in Banking research, banks will save $447 billion overall by 2023 because to AI applications.
The machine learning technology is still an unregulated system despite being used in many industries and sectors these days. This means that its performance cannot be measured due to lack of regulations or standards since they are still not available or developed yet.
The future of machine learning in investment banking is still unclear. However, it is expected that as more research and development takes place, it will be used more often as it gives accurate and reliable predictions.
Also, you will see a lot more use of machine learning technology in the day to day operations of investment banks when financial software is released by banks which will make digitizing and automating most operations easier for the employees.