Banking
Feb 28, 2025
Data Reasoning Applications In Investment Banking Workflows
Despite rapid technological advancements over the past few decades, many aspects of investment banking still remain frustratingly manual. Repetitive tasks like data collection, document creation and review, and due diligence materials management require constant attention from analysts, which can slow down deals and increase costs for banks. But, fortunately for investment bankers, the age of automation is underway and data reasoning has emerged as a transformative solution that can help streamline many of these repetitive tasks.
This article will explore data reasoning applications in investment banking workflows and discuss some of the benefits of automation.
Data Reasoning and its Role in Finance
Data reasoning uses artificial intelligence (AI) to analyze, interpret, and apply logic to data to draw insights that help deliver informed decision-making. This transformative solution goes beyond basic data collection and analysis by understanding the why behind the what.
In essence, data reasoning solutions like Gradient’s Finance Reasoning Platform have the ability to process, interpret, and derive meaning from data in ways that align with specific business goals. This process is especially valuable in cases where data alone cannot yield straightforward answers but requires deeper analysis and contextual understanding to guide decisions. In other words, data reasoning is perfect for the data-intensive investment banking industry. Having data is half the puzzle. But, being able to draw insights from this data in real-time creates an immense amount of value that capital markets crave.
Gradient Finance Reasoning Platform can intelligently infer relationships, generate derived data, and handle knowledge-based operations, allowing investment bankers to automate even the most intricate workflows at the highest level of accuracy and speed. This automation frees up both senior and junior bankers to focus on more high-value tasks, which can improve deal flow and reduce overhead.
Data reasoning is especially effective at reducing latency in repetitive workflows
Identify and Eliminating Latency in Workflows
In computing terms, latency is the delay before a transfer of data begins following an instruction for its transfer. In investment banking, latency refers to the constant delays that can hinder a deal’s progress, such as conducting diligence, ensuring that documents are compliant, or drafting and reviewing regulatory filings. These are all processes that must be completed before the deal can proceed. Data reasoning solutions are ideal for reducing latency in workflows and essentially removing bottlenecks.
A few common areas where data reasoning can eliminate latency in workflows are:
Drafting presentations
Assembling and organizing company documents
Recording and tracking deal-related conversations
Organizing and maintaining due diligence rooms
Drafting and reviewing regulatory filings
Creating “libraries” of past deals and documents for more efficient tracking and investigation.
These tasks typically require constant manual effort from analysts and associates, whose labor is not cheap and leads to increased overhead for investment banks. As an investment bank grows, so does the amount of mundane, time-consuming tasks that must be completed.
Historically, investment bankers have sought to win deals and beat their competition by outworking them, routinely clocking in 80+ hour workweeks. However, moving forward, it’s likely that the banks with a competitive edge will be those harnessing generative AI to prioritize efficiency. Even the largest team of analysts cannot compete with an automated solution capable of analyzing immense datasets in seconds.
The investment bankers who dominate the coming decade will likely be those who leverage data reasoning to achieve more with fewer resources, optimizing revenue while reducing overhead.
Benefits of Automating With Gradient
At Gradient, we have leveraged AI and data reasoning to build a platform custom-made for the financial sector. There are five main reasons why Gradient is uniquely positioned to benefit investment banks:
Deep industry expertise: Gradient’s management team has a collective 100+ years of professional experience in financial services. We understand the products, the data, and common business challenges that investment bankers face.
Innovative AI approach: We have successfully delivered AI solutions to leading organizations and constantly tune models to ensure that performance gets better with each improvement in the underlying foundational models.
Our finance reasoning platform: By embedding domain-specific knowledge into our platform's data preprocessing and model tuning pipelines, our results are more reliable and higher quality than the competition.
Collaborative approach: We take a consultative approach to ensure that we identify the best solutions for your organization, placing an emphasis on ROI to deliver value.
Speed-to-value: Our approach is results and value-driven. Our use case library and accelerators, in addition to our industry expertise and rapid development approach, ensure we have a POC in 2 to 4 weeks. We want to create value for you and we want to do it quickly.
Let’s discuss a few of the benefits of leveraging Gradient’s Finance Reasoning solution within investment banking.
Shorter deal lifecycles from initiation to execution
Gradient’s Finance Reasoning Platform has been specifically created to help investment bankers automate tasks to help shorten deal lifecycles. This includes areas like document review, compliance reviews, tracking legal documents, and creating or managing critical documents. Reducing the time spent on these frustratingly manual tasks can help bankers complete deals more quickly while also reducing overhead.
Improve Operating Margins
Reducing friction in the deal process has the dual benefit of reducing costs while potentially increasing revenue. For example, if solutions like Gradient can eliminate a large number of manual tasks then investment bankers can avoid hiring additional analysts, thus reducing overhead. This enhanced efficiency also allows firms to focus on higher-value activities, thus completing more deals throughout the year and potentially improving top-line revenues.
Talent Management
Data reasoning solutions can also help investment banks better manage talent by building a more robust bench of talent. This talent will move up more quickly by learning to scale faster, as they will not need to waste time performing low-skill tasks that previous analysts had to perform, allowing them to focus on learning the higher-skilled parts of their business.
Onboarding a Data Reasoning Platform
Leveraging a data reasoning solution sounds resource-intensive. But, getting started is typically a simple three-step process:
Integration: Data reasoning solutions like Gradient’s Finance Reasoning platform quickly consolidate data from a number of different sources – both structured and unstructured – without pre-processing to be analyzed. The expert knowledge of your team can also be integrated into the model at this phase without any pre-processing required.
Reasoning: The AI system learns quickly by analyzing the dataset and any financial expertise that you may have provided. It also applies multi-step logic and reasoning to automate any complex tasks that you need to be completed.
Continuous Improvement: After deployment, Gradient provides comprehensive monitoring to ensure your AI systems are operating at their highest potential. This includes tracking accuracy over time to ensure long-term success, integrating directly into existing software and tools, and maintaining the highest level of privacy and compliance (e.g. SOC 2 Type 2, GDPR).
While it sounds daunting, onboarding a data reasoning solution is little different from leveraging any other enterprise software solution.
Final Thoughts: Increasing Efficiency With Gradient
Data reasoning presents a unique opportunity for investment bankers to work more efficiently and do more with less, which will allow them to secure a sustained advantage over their competitors. Some of the benefits associated with removing bottlenecks for investment bankers include:
Achieving shorter deal lifecycles from initiation to execution: Automating tasks like creating presentations, assembling and organizing company documents, compliance reviews, and much more can help drastically shorten deal lifecycles.
Improving operating margins: Data reasoning allows investment bankers to be more efficient while also utilizing less help from analysts, which can help improve efficiency and boost the firm’s overall operating margins.
Enhanced talent management: With data reasoning, analysts will no longer have to handle all of the manual analysis associated with most investment banking deals. Instead, they can start learning the finer aspects of the business which will help firms build a more robust bench of talent more quickly.
Interested in learning more about how data reasoning can benefit investment bankers? Be sure to read our guide Data Reasoning: Top 5 Use Cases for Banking Professionals.
We hope that you’ve found this article valuable when it comes to learning about how data reasoning is helping investment bankers remove bottlenecks.
FAQ: Data Reasoning in Investment Banking
How is data reasoning used in investment banking?
Data reasoning is the process of interpreting and extracting insights from structured and unstructured financial data using AI and machine learning. In investment banking, it can help automate repetitive tasks, make workflows more efficient, and improve profitability for banks.
What are the biggest challenges investment banks face with unstructured data?
Investment banks deal with vast amounts of unstructured data, such as analyst reports, earnings call transcripts, regulatory filings, and any form of data that is not organized in traditional datasets. Unstructured data has traditionally been incompatible with artificial intelligence tools. However, data reasoning solutions are solving this problem.
What are the key benefits of using AI-powered data reasoning in portfolio management?
Data reasoning has been known to help portfolio managers increase accuracy by 30% and reduce workloads by 80%.
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