3 Technologies Being Integrated into Investment Banking
3 Technologies Being Integrated into Investment Banking
If you work in the investment banking industry, you have undoubtedly heard that technology is reimagining how banking business is carried out – but what exactly are these technologies?
For investment banks, there are 3 key innovations currently being integrated into business models. These are artificial intelligence, blockchain, and software-as-a-service. Understanding the benefits of adopting these technologies is crucial.
The digital era of finance is showing no signs of slowing, and will likely become the dominant force in the years to come. In this article, we will explore the 3 abovementioned technologies and discuss their impacts on investment banking.
1. Artificial Intelligence
Artificial intelligence (AI) is not only one of the biggest technologies making waves in investment banking but in the entire financial industry. The technology poses significant benefits that can optimize banking processes, from front-end customer service all the way to back-end automation.
Not to mention the potential cost savings of AI are massive. According to Business Insider, the size of cost savings opportunities when AI is used in banking are:
$199B for front office
$217B for middle office
$31B for back office
For investment banking specifically, there are a few key tasks that AI enables. These include:
Identifying Trading Patterns
Historically, the process of technical analysis – when carried out by humans – has lacked the accuracy necessary for chart patterns to be consistently effective and helpful.
AI offers investment banks both simplicity and precision when it comes to predictive technology. As a result, AI can be employed to read and analyze trading charts to identify ongoing or future patterns. In turn, traders can focus more of their energy on the actual execution of trades, rather than analysis of current markets and trends.
Language Processing Technology
There are many different branches of AI that diversify the use cases of the technology in banking. One such branch is natural language processing (NPL). NPL converts human text or speech into a computer format that AI can understand and respond to.
In investment banking, there are 3 key use cases for this technology:
Investment Insights: As banks have continued to massively scale up their business models, identifying and creating valuable insights has become more difficult. With NPL, however, this process is simplified, as this technology can sweep through the heaps of reports and documents with greater ease and efficiency than a human could.
Document Searching: Speaking of sifting through mountains of documents, NPL is majorly useful when searching and reviewing legal documents. Compliance and regulatory change have become major hurdles for banks to overcome on a day-to-day basis – but with NPL, the extraction of key legal information from thousands of documents occurs in mere seconds.
Customer Support: One of the more obvious but also most important uses of NPL is its ability to interact with and assist customers. NPL can help with everything from checking balances and transferring money to providing personalized recommendations.
High-Speed Trade Execution
High-speed trade execution has been in place for more than a decade, with AI greatly expanding its potential in recent years. Also referred to as high-frequency trading (HFT), this technology utilizes algorithms to exploit price changes that occur within extremely small windows of time.
“HFT trading ideally needs to have the lowest possible data latency (time-delays) and the maximum possible automation level. So participants prefer to trade in markets with high levels of automation and integration capabilities in their trading platforms”
AI ultimately helps to bring a greater level of accuracy that is needed for HFT to execute these speedy trades. As tech professionals learn to better harness the complexities of AI for investment purposes, this has the potential to result in AI that actively learns from its mistakes to maximize profit.
Unlike AI, which has had several years to be developed and adapted to finance, blockchain is a relatively new technology within the banking industry. Despite this, the potential advantages of blockchain are enormous and well worth paying close attention to in the coming years.
While the banking industry has traditionally relied on centralized business models, blockchain tends to encourage the adoption of decentralization. This allows investment banks to scale their operations on a global level.
Software-as-a-Service, or SaaS, is experiencing exponential growth in popularity in the banking industry. SaaS enables the use of cloud-based digital infrastructures, systems, servers, and networks.
As digital transformations are becoming more prevalent within investment banking, SaaS offers financial institutions a convenient, efficient, and scalable solution. Rather than having to invest capital into costly onsite equipment and in-house software development, SaaS provides investment banks with the resources and talent needed to keep up with the digital era.
Moreover, SaaS is often seen used to improve client relations while simultaneously minimizing institutional costs. With the support of SaaS, an investment banking team can gain much better data analysis and customer insights.
Keeping up with digital and technological change is essential for investment banks to succeed in the coming decades.
However, the implementation and integration of such technologies can be intimidating and daunting for institutions that may be behind in their digital transformation journey. Luckily, advances in FinTech have made it far easier for institutions to partner with external companies to optimize their systems and processes more efficiently.
Here at CPQi, we offer both AI and SaaS solutions to our clients. Partnering with our team means you will work with professionals who are regularly trained in both finance and technology. To get started or learn more about our services, contact our team today.