Banking
Feb 11, 2025
How Data Reasoning and AI Can Streamline the Investment Banking Document Review Process
Context
The document review process in investment banking has become increasingly resource-intensive over the decades, creating bottlenecks at nearly every phase of a modern deal. With so much manual effort required to complete necessary tasks, today’s investment bankers have had no choice but to hire more associates and, thus, expand their overhead. Fortunately, data reasoning has emerged as an AI solution that can help investment bankers streamline the document review process to become more efficient, accurate, and profitable.
The most successful investment bankers in the coming decade will be those who leverage technology to do diligence more efficiently, allowing them to spend time with the right issuers and not waste time with deals that may not succeed. With less time spent on manual document review, bankers will be able to spend more time prioritizing existing relationships.
This article will explore how investment bankers can use data reasoning to assist in the document review process at different stages of a deal to improve productivity and close deals faster.
Outperforming Competitors in Modern Investment Banking
Banking has become more demanding than ever in recent decades and modern bankers must navigate an environment where speed, precision, and adaptability are paramount. The bar for success continues to rise each year as firms continuously adopt and implement new strategies and technologies to identify opportunities, mitigate risks, and close deals faster than their competitors. However, with just 24 hours in a day, even the most hardworking bankers can only achieve so much in one day. To overcome this bottleneck, most firms hire analysts to help conduct analysis, put together presentations, and complete other manual tasks to help close deals more quickly. This strategy of hiring additional assistance – while necessary – is not ideal.
Hiring additional analysts is an expensive way to increase productivity. To start, every analyst requires a salary (which is often a significant expense), healthcare, other employee benefits, additional office space, and resources to onboard and train them. Each additional salary contributes to the firm’s expense center, despite junior analysts bringing in marginal value to the deal itself. Additionally, analysts often work grueling hours which can put them even more at risk of creating errors or missing inaccuracies that could harm the deal.
Modern investment bankers are long overdue for a solution that can help increase productivity while maintaining – or even reducing – overall headcount. Enter: data reasoning.
Data Reasoning: Your Solution to Scale
Data reasoning is the practice of using artificial intelligence to analyze, interpret, and apply logic to data, in order to draw insights that can help deliver informed decision-making. Solutions like Gradient’s Finance Reasoning platform are capable of going beyond basic data collection and analysis by understanding the why behind the what. At Gradient, we describe this as moving from basic operational tasks to higher-order operational tasks.
AI-driven data reasoning solutions are emerging as a transformative technology in investment banking that helps bankers do more with less. Data reasoning can help your firm:
Complete analysis and routine tasks more quickly: This reduces the time it takes to analyze a deal, perform due diligence, update models or presentations, and much more.
Increase accuracy throughout the deal process: AI can conduct analysis at an unprecedented pace while also achieving greater accuracy, which can improve the likelihood of the deal being placed.
Reduce overhead to improve deal profitability: Data reasoning can help a bank improve efficiency and complete more deals while reducing operating expenses by automating a wide range of manual tasks.
Let’s explore a few specific ways that data reasoning solutions can help automate and transform investment bankers’ most complex workflows.
AI for Document Review: Automating Repetitive Tasks
Perhaps the most monotonous part of the investment bank is the incessant prevalence of manual document review at nearly every stage of a deal. Common documents that need to be reviewed through the deal process include:
Due diligence materials: Bankers need to review financial statements, contracts, corporate governance documents, and any legal or compliance-related materials to verify the target company’s financial health and legal standing.
Term Sheets or Letters of Intent (LOIs): These documents outline the key terms of the transaction, requiring meticulous review to ensure they align with the client’s goals and minimize potential risks.
Regulatory filings: For deals requiring regulatory approval, such as mergers and acquisitions, bankers need to review and prepare filings like Form S-1s, registration exemptions, antitrust submissions, or SEC disclosures to ensure compliance.
Transaction agreements: These include purchase agreements, merger agreements, or financing documents. Bankers collaborate with legal teams to ensure terms, covenants, and conditions are clear, accurate, and favorable to their client.
Marketing materials: In capital-raising deals, pitch decks or prospectuses are reviewed for accuracy.
In total, the document review process can take hours upon hours to complete from start to finish. What’s even more frustrating is that many parts of this process – like contract review and compliance checks – are often boilerplate tasks that are similar for each deal. Fortunately, this type of monotonous repetition may slowly become a thing of the past as data extraction and reasoning can improve the accuracy, speed, and efficiency of these tasks.
Solutions like Gradient’s Finance Reasoning Platform are capable of manually reviewing documents – even when the data is in an unstructured format – conducting analysis, flagging potentially incorrect sections, and highlighting key points. This automation can help bankers drastically improve efficiency and potentially capture a larger percentage of deal’s revenue by reducing reliance on associates and large compliance teams.
Improving Speed and Efficiency
Data reasoning solutions can drastically reduce the time that it takes to review documents manually. With Gradient, document reviews that used to take hours can be completed in minutes. This reduces the back-and-forth between humans and helps eliminate long delays that might be spent waiting for stakeholders to respond to emails or return calls.
There are several key benefits of increasing speed and efficiency by automating the document review process:
Allows bankers to focus on the deal itself: Automated document review frees bankers from getting bogged down in the minutia of the documents. Instead, they can prioritize time spent networking and pushing deals along.
Flags potentially inaccurate data: By integrating AI into other datasets, investment bankers can cross-reference the data in the deal documents more quickly with things like background checks to ensure there are no bad actors involved in the deal. This alone can make an investment in AI well worth it.
Helping bankers do more with less: Instead of employing a team of analysts, investment bankers can lean on automated AI solutions to conduct the manual review process which can help improve a bank’s profitability.
One of the core differentiators of Gradient’s AI-powered Finance Reasoning Platform is that it can accurately review documents and other data sets even when that data is unstructured. Let’s explore that in more detail.
Extracting Key Figures and Terms
A large percentage of documents in investment banking have unstructured data, including investor decks, proposals, and deal documents. These types of documents hold an immense amount of valuable information. But, they are not formatted in a way that has traditionally been compatible with AI and machine learning solutions. Thanks to Gradient’s Finance Reasoning Platform, unstructured data is no longer a roadblock.
Gradient’s Finance Reasoning Platform allows bankers to extract key figures and terms from sources like PDFs, screenshots, and client communications which can help a deal move more efficiently through the diligence process. AI can also be integrated into data rooms to dynamically extract and highlight changes in data points. Taking it a step further, data reasoning can even measure the accuracy and consistency of the data provided, potentially reducing errors that may be caused by a junior analyst.
Data reasoning platforms also excel at identifying patterns within immense data sets that may not be easily recognizable by the human eye. This use case alone has widespread implications for compliance departments. Moving on to yet another potential use case for AI in investment banking document review, AI can assist with data room organization.
Organizing Data Rooms
Data reasoning solutions can automatically organize documents (and key sections of these documents) into a structured format. For example, Gradient’s Finance Reasoning Platform can review a dataset and organize the information into categories like finance, operations, and legal while also labeling the data appropriately. This allows bankers and compliance teams to quickly sift through the documents in order to identify critical information. Again, this can save hours of time that would otherwise be spent manually searching through hundreds of different documents.
In addition to increased efficiency, organizing data rooms with AI can also provide bankers with the assurance that the facts and figures provided within the documentation are accurate. Consistent data can then be affirmed and carried throughout the process.
For example, it’s possible to layer a Co-Pilot search tool that allows for a more organized and efficient method of searching through documents – a technological update that has immense implications for bankers, issuers, and compliance teams.
Final Thoughts: Automating Document Review Processes
This article discussed a total of five different ways that data reasoning can help investment bankers automate the document review processes:
Automating repetitive tasks
Increasing the efficiency of deal execution
Extracting key figures and terms
Organizing data rooms
Harnessing predictive analytics
By leveraging these benefits, investment bankers can increase the number of deals they land per year, enjoy better deal quality, and improve overall profitability by reducing reliance on analysts and onerous compliance processes. All of this is made possible by data reasoning.
Interested in learning how a high-performing, cost-effective custom AI system could benefit your business? Contact the Gradient team today to learn more.
We hope that you’ve found this article valuable when it comes to learning more about how investment bankers can use data reasoning to automate the document review process.
FAQ: Data Reasoning in Investment Banking
What is data reasoning?
Data reasoning is the practice of using artificial intelligence to analyze, interpret, and apply logic to data in order to draw insights that can help deliver informed decision-making. This technology has widespread applications in investment banking, particularly in automating the document review process.
Can AI be used to automate investment banking document review?
Yes. Data reasoning solutions can use AI to help investment bankers automate large chunks of the document review process including automating repetitive tasks, drawing insights from large unstructured data sets, and even organizing cluttered data rooms.
How can I get started automating investment banking document review?
The best way to automate document review in investment banking is to leverage a data reasoning solution like Gradient’s Finance Reasoning Platform.
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