Investment bankers deal with large datasets. A good part of an investment banker’s job, especially at the lower ranks of the investment banking hierarchy, involves collecting, formatting, and presenting large volumes of data so that they are fit for analysis. Having a good percentage of your valuable human resources tied up in the drudgery of number crunching not only leads to a wasteful utilization of resources but also creates significant room for error and inefficiency. Imagine the confusion and bottlenecks created if unstructured and error-prone data enters an organization’s workflow. This becomes a challenge especially for organizations with silo-like structural divisions where communication flows between the different divisions are not always very efficient.
The good news is that automation technology has the potential to take the burden of such manual labor away from investment bankers without threatening their jobs. Data processing and collection tasks are among the most straightforward to automate, leaving investment bankers free to devote their resources to more-high-value tasks such as deriving creative insights from the data at hand. Investment banks understand that they must adapt to stay competitive, and they have discovered that using artificial intelligence (AI), robotic process automation (RPA) and cognitive automation (CA) is a highly effective way to do so. In the next five years, we can expect to see accelerated adoption of automation technologies by investment banks, but with considerable variation in organizational vision, willingness, and capability to develop and apply RPA, CA, and AI for competitive survival and advantage.
Application Of Automation Tools In Investment Banking
There is significant customer insight contained in the data held by investment banks, although much of this insight was difficult to extract until recently. This is now changing as a result of adopting technologies such as AI, RPA, and CA in investment banking. Let us take a look at how automation tools support the investment banking industry.
Acquiring Customer Insight
Extrinsic market data and internal proprietary data are both securely stored in a user’s customer relationship management (CRM) system. However, traditional CRM platforms have shown limited ability to sequence unstructured data, resulting in a vast archive of insightful information going unused. Automation tools can extract more value from such data by showing new customer insights, strengthening opportunities in areas such as mergers and acquisitions. With automation, the data is all in one place, allowing users to generate real-time, actionable insights that were previously inaccessible.
With the ability of automation tools to leverage combined data, investment banks can drive workflow efficiencies and uncover previously unknown connections, players, and opportunities. Investment banking, being a highly competitive, relationship-driven industry, relies heavily on the ability to find similarities among divergent datasets. AI-driven insights can present new opportunities, allowing account managers to collaborate proactively with their clients. Data-driven AI enables investment bankers to approach clients with confidence, unlocking new growth opportunities by identifying significant catalysts across terabytes of unstructured data.
Transforming Legacy Systems
Implementing digitalization against the backdrop of legacy systems and a naturally conservative outlook remains a challenge for investment banks. Many influencers emphasize the importance of embedding digital processes throughout an organization’s DNA for it to be completely successful. Automation tools, combined with powerful data, have the potential to make this a reality. They put computing control in the hands of business users, not just technology teams, who can use data analytics tools to improve their business models.
Better Utilization Of Existing Resources
The increasing adoption of automation in investment banking does not necessarily threaten existing jobs. There is always room for human innovation, creativity, and intellect, no matter how advanced technology gets. As a direct reaction to automation, many organizations would, in fact, attach greater value to the position of junior bankers – they would expect them to have a more diverse set of talents, as well as the capacity to think creatively and bring in fresh ideas, rather than just crunching data.
Data was perhaps the biggest story of the previous decade, so much so that organizations now sit on large volumes of data captured through various processes over the past decade. Growth will likely be driven by intelligent narration built on these large stacks of data. This means organizations would need to shift their most valuable resources and their best minds to being creative with data and using it to weave intelligent narratives that solve business problems. This, of course, necessitates their time being freed from the mechanical drudgery of collecting and processing the data. Going forward, these tasks look set to be increasingly assigned to automation tools so that organizations can focus on their core competencies.