Experts at Deloitte estimate that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026. This article explores the possibilities of AI in investment banking, specifically when it comes to automating monotonous workflows and improving efficiency.
Experts at Deloitte estimate that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026. This article explores the possibilities of AI in investment banking, specifically when it comes to automating monotonous workflows and improving efficiency.
Experts at Deloitte estimate that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026. This article explores the possibilities of AI in investment banking, specifically when it comes to automating monotonous workflows and improving efficiency.
Experts at Deloitte estimate that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026. This article explores the possibilities of AI in investment banking, specifically when it comes to automating monotonous workflows and improving efficiency.
Overseeing complex capital structure transactions, such as M&A, primary or secondary issuance or restructurings is a complex task comprised of dozens, if not hundreds, of details and repeatable tasks to consider. With so many moving pieces, it’s easy to be skeptical that generative artificial intelligence (AI) tools actually have the capacity to improve workflows throughout the deal process. However, tools like Gradient’s Finance Reasoning Platform are already helping investment banking professionals eliminate automatable repetitive tasks and operate more efficiently.
This article will take a detailed look at how artificial intelligence can help improve investment banking workflows, particularly by automating repeatable tasks and allowing high-performing teams to do more while consuming fewer resources.
Overseeing complex capital structure transactions, such as M&A, primary or secondary issuance or restructurings is a complex task comprised of dozens, if not hundreds, of details and repeatable tasks to consider. With so many moving pieces, it’s easy to be skeptical that generative artificial intelligence (AI) tools actually have the capacity to improve workflows throughout the deal process. However, tools like Gradient’s Finance Reasoning Platform are already helping investment banking professionals eliminate automatable repetitive tasks and operate more efficiently.
This article will take a detailed look at how artificial intelligence can help improve investment banking workflows, particularly by automating repeatable tasks and allowing high-performing teams to do more while consuming fewer resources.
Overseeing complex capital structure transactions, such as M&A, primary or secondary issuance or restructurings is a complex task comprised of dozens, if not hundreds, of details and repeatable tasks to consider. With so many moving pieces, it’s easy to be skeptical that generative artificial intelligence (AI) tools actually have the capacity to improve workflows throughout the deal process. However, tools like Gradient’s Finance Reasoning Platform are already helping investment banking professionals eliminate automatable repetitive tasks and operate more efficiently.
This article will take a detailed look at how artificial intelligence can help improve investment banking workflows, particularly by automating repeatable tasks and allowing high-performing teams to do more while consuming fewer resources.
Overseeing complex capital structure transactions, such as M&A, primary or secondary issuance or restructurings is a complex task comprised of dozens, if not hundreds, of details and repeatable tasks to consider. With so many moving pieces, it’s easy to be skeptical that generative artificial intelligence (AI) tools actually have the capacity to improve workflows throughout the deal process. However, tools like Gradient’s Finance Reasoning Platform are already helping investment banking professionals eliminate automatable repetitive tasks and operate more efficiently.
This article will take a detailed look at how artificial intelligence can help improve investment banking workflows, particularly by automating repeatable tasks and allowing high-performing teams to do more while consuming fewer resources.
AI in Investment Banking
AI in Investment Banking
AI in Investment Banking
AI in Investment Banking
Other financial services verticals – like investment management or retail banking – have been quick to adopt artificial intelligence. For example, J.P. Morgan has established itself as an early adopter of AI by launching its institutional investment product called “Quest IndexGPT” which uses GPT-4 to enhance the thematic index construction process for institutional investors. Financial services companies have also already used AI to automate tasks like trade settlement, SEC marketing compliance, and investment analysis. However, tools like Quest IndexGPT are only scratching the surface of what AI is capable of. Thanks to advancing technology, financial services companies can now automate much higher-level workflows than were previously possible – an update that has far-reaching implications in investment banking as well. These new innovations and use cases allow bankers to do more with less, to be more competitive, and to accelerate their timeline for reviewing, planning, winning, and distributing deals.
Major consulting firms are very optimistic about the future of AI in investment banking.
Deloitte estimates that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026 when adjusted for inflation. This improvement stems from enhanced productivity and efficiency from employees, which is a topic that we will discuss in more detail in the coming sections.
According to Deloitte’s study, greater employee productivity could result in additional revenue of $3.5 million per front-office employee by 2026. This enhanced efficiency can also help expedite the deal evaluation process – specifically when it comes to document drafting and regulatory filings – potentially allowing investment banks to close more deals each year. Outpacing their competitor's ability to perform diligence can help banks win more deals.
McKinsey is similarly optimistic and projects that enhanced employee productivity due to AI could increase revenues for investment banks by as much as $200 to $340 billion annually. From a high level, this enhanced revenue stems from automating manual tasks that monopolize bankers' day-to-day schedules, such as due diligence processes. Removing these time-intensive manual tasks helps free up a significant portion of investment bankers’s schedules, allowing them to focus on higher-level tasks.
The investment banking sector is uniquely positioned to experience an outsized benefit from AI, thanks to the volume of both internal and external data sources available in the industry. The financial services sector is perhaps the most data-intensive sector globally. Modern investment banks have access to massive treasure troves of data including market data, corporate news, public financial filings, historical transactions from the dealer’s desk, economic indicators, industry benchmarks, CRM data, alternative data sets, second and third-party data platforms, and more.
This plethora of data creates dozens of strategic ways that investment banks can leverage AI. However, to do so, investment banks first need to access the full potential of their data and staff.
Unlocking Data is Key
The financial services industry is overflowing with data, which is why it’s considered one of the industries most poised to benefit from the rise of generative AI. This is highly advantageous because AI solutions are only as good as the data that powers them. Data plays such a critical role when it comes to implementing AI that McKinsey listed it as a key ingredient for success when implementing generative AI at scale in investment banking. McKinsey stated:
“Most commercial and investment banks are already working to consolidate and optimize market, reference, and client data. But, they don’t always have a strategy for unstructured data such as text, images, client documents, and so on. All of these can be vital for successful gen AI applications; firms may need to establish the infrastructure for people across the firm to access unstructured data from anywhere on their platforms.”
Access to unstructured data plays a critical role in onboarding an AI solution to automate manual tasks. However, one area where we disagree with McKinsey is the implication that investment banks need to establish an infrastructure of people across the firm. We created Gradient’s Finance Reasoning Platform to help make it easier for investment banks to leverage the power of AI without allocating resources to a team of data scientists or ML engineers. This is highly advantageous when it comes to time-consuming tasks such as valuing private companies. Valuing private companies has grown more complicated in recent years, with companies staying private for longer. However, investment bankers can leverage data reasoning to review the unstructured data sources of private companies, build parallels to valuations of other companies, and include market transactions of private companies to generate up-to-date valuations. All of this can be automated and completed in just a fraction of the time – without bringing on additional team members – by using a solution like Gradient’s Finance Reasoning Platform.
Artificial Intelligence technology is evolving rapidly. While the AI technology from five years ago likely couldn’t assist with many of the complex functions required in investment banking, today’s AI solutions are capable of automating much more complex workflows. With that in mind let’s examine how AI can improve workflows in investment banking.
Other financial services verticals – like investment management or retail banking – have been quick to adopt artificial intelligence. For example, J.P. Morgan has established itself as an early adopter of AI by launching its institutional investment product called “Quest IndexGPT” which uses GPT-4 to enhance the thematic index construction process for institutional investors. Financial services companies have also already used AI to automate tasks like trade settlement, SEC marketing compliance, and investment analysis. However, tools like Quest IndexGPT are only scratching the surface of what AI is capable of. Thanks to advancing technology, financial services companies can now automate much higher-level workflows than were previously possible – an update that has far-reaching implications in investment banking as well. These new innovations and use cases allow bankers to do more with less, to be more competitive, and to accelerate their timeline for reviewing, planning, winning, and distributing deals.
Major consulting firms are very optimistic about the future of AI in investment banking.
Deloitte estimates that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026 when adjusted for inflation. This improvement stems from enhanced productivity and efficiency from employees, which is a topic that we will discuss in more detail in the coming sections.
According to Deloitte’s study, greater employee productivity could result in additional revenue of $3.5 million per front-office employee by 2026. This enhanced efficiency can also help expedite the deal evaluation process – specifically when it comes to document drafting and regulatory filings – potentially allowing investment banks to close more deals each year. Outpacing their competitor's ability to perform diligence can help banks win more deals.
McKinsey is similarly optimistic and projects that enhanced employee productivity due to AI could increase revenues for investment banks by as much as $200 to $340 billion annually. From a high level, this enhanced revenue stems from automating manual tasks that monopolize bankers' day-to-day schedules, such as due diligence processes. Removing these time-intensive manual tasks helps free up a significant portion of investment bankers’s schedules, allowing them to focus on higher-level tasks.
The investment banking sector is uniquely positioned to experience an outsized benefit from AI, thanks to the volume of both internal and external data sources available in the industry. The financial services sector is perhaps the most data-intensive sector globally. Modern investment banks have access to massive treasure troves of data including market data, corporate news, public financial filings, historical transactions from the dealer’s desk, economic indicators, industry benchmarks, CRM data, alternative data sets, second and third-party data platforms, and more.
This plethora of data creates dozens of strategic ways that investment banks can leverage AI. However, to do so, investment banks first need to access the full potential of their data and staff.
Unlocking Data is Key
The financial services industry is overflowing with data, which is why it’s considered one of the industries most poised to benefit from the rise of generative AI. This is highly advantageous because AI solutions are only as good as the data that powers them. Data plays such a critical role when it comes to implementing AI that McKinsey listed it as a key ingredient for success when implementing generative AI at scale in investment banking. McKinsey stated:
“Most commercial and investment banks are already working to consolidate and optimize market, reference, and client data. But, they don’t always have a strategy for unstructured data such as text, images, client documents, and so on. All of these can be vital for successful gen AI applications; firms may need to establish the infrastructure for people across the firm to access unstructured data from anywhere on their platforms.”
Access to unstructured data plays a critical role in onboarding an AI solution to automate manual tasks. However, one area where we disagree with McKinsey is the implication that investment banks need to establish an infrastructure of people across the firm. We created Gradient’s Finance Reasoning Platform to help make it easier for investment banks to leverage the power of AI without allocating resources to a team of data scientists or ML engineers. This is highly advantageous when it comes to time-consuming tasks such as valuing private companies. Valuing private companies has grown more complicated in recent years, with companies staying private for longer. However, investment bankers can leverage data reasoning to review the unstructured data sources of private companies, build parallels to valuations of other companies, and include market transactions of private companies to generate up-to-date valuations. All of this can be automated and completed in just a fraction of the time – without bringing on additional team members – by using a solution like Gradient’s Finance Reasoning Platform.
Artificial Intelligence technology is evolving rapidly. While the AI technology from five years ago likely couldn’t assist with many of the complex functions required in investment banking, today’s AI solutions are capable of automating much more complex workflows. With that in mind let’s examine how AI can improve workflows in investment banking.
Other financial services verticals – like investment management or retail banking – have been quick to adopt artificial intelligence. For example, J.P. Morgan has established itself as an early adopter of AI by launching its institutional investment product called “Quest IndexGPT” which uses GPT-4 to enhance the thematic index construction process for institutional investors. Financial services companies have also already used AI to automate tasks like trade settlement, SEC marketing compliance, and investment analysis. However, tools like Quest IndexGPT are only scratching the surface of what AI is capable of. Thanks to advancing technology, financial services companies can now automate much higher-level workflows than were previously possible – an update that has far-reaching implications in investment banking as well. These new innovations and use cases allow bankers to do more with less, to be more competitive, and to accelerate their timeline for reviewing, planning, winning, and distributing deals.
Major consulting firms are very optimistic about the future of AI in investment banking.
Deloitte estimates that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026 when adjusted for inflation. This improvement stems from enhanced productivity and efficiency from employees, which is a topic that we will discuss in more detail in the coming sections.
According to Deloitte’s study, greater employee productivity could result in additional revenue of $3.5 million per front-office employee by 2026. This enhanced efficiency can also help expedite the deal evaluation process – specifically when it comes to document drafting and regulatory filings – potentially allowing investment banks to close more deals each year. Outpacing their competitor's ability to perform diligence can help banks win more deals.
McKinsey is similarly optimistic and projects that enhanced employee productivity due to AI could increase revenues for investment banks by as much as $200 to $340 billion annually. From a high level, this enhanced revenue stems from automating manual tasks that monopolize bankers' day-to-day schedules, such as due diligence processes. Removing these time-intensive manual tasks helps free up a significant portion of investment bankers’s schedules, allowing them to focus on higher-level tasks.
The investment banking sector is uniquely positioned to experience an outsized benefit from AI, thanks to the volume of both internal and external data sources available in the industry. The financial services sector is perhaps the most data-intensive sector globally. Modern investment banks have access to massive treasure troves of data including market data, corporate news, public financial filings, historical transactions from the dealer’s desk, economic indicators, industry benchmarks, CRM data, alternative data sets, second and third-party data platforms, and more.
This plethora of data creates dozens of strategic ways that investment banks can leverage AI. However, to do so, investment banks first need to access the full potential of their data and staff.
Unlocking Data is Key
The financial services industry is overflowing with data, which is why it’s considered one of the industries most poised to benefit from the rise of generative AI. This is highly advantageous because AI solutions are only as good as the data that powers them. Data plays such a critical role when it comes to implementing AI that McKinsey listed it as a key ingredient for success when implementing generative AI at scale in investment banking. McKinsey stated:
“Most commercial and investment banks are already working to consolidate and optimize market, reference, and client data. But, they don’t always have a strategy for unstructured data such as text, images, client documents, and so on. All of these can be vital for successful gen AI applications; firms may need to establish the infrastructure for people across the firm to access unstructured data from anywhere on their platforms.”
Access to unstructured data plays a critical role in onboarding an AI solution to automate manual tasks. However, one area where we disagree with McKinsey is the implication that investment banks need to establish an infrastructure of people across the firm. We created Gradient’s Finance Reasoning Platform to help make it easier for investment banks to leverage the power of AI without allocating resources to a team of data scientists or ML engineers. This is highly advantageous when it comes to time-consuming tasks such as valuing private companies. Valuing private companies has grown more complicated in recent years, with companies staying private for longer. However, investment bankers can leverage data reasoning to review the unstructured data sources of private companies, build parallels to valuations of other companies, and include market transactions of private companies to generate up-to-date valuations. All of this can be automated and completed in just a fraction of the time – without bringing on additional team members – by using a solution like Gradient’s Finance Reasoning Platform.
Artificial Intelligence technology is evolving rapidly. While the AI technology from five years ago likely couldn’t assist with many of the complex functions required in investment banking, today’s AI solutions are capable of automating much more complex workflows. With that in mind let’s examine how AI can improve workflows in investment banking.
Other financial services verticals – like investment management or retail banking – have been quick to adopt artificial intelligence. For example, J.P. Morgan has established itself as an early adopter of AI by launching its institutional investment product called “Quest IndexGPT” which uses GPT-4 to enhance the thematic index construction process for institutional investors. Financial services companies have also already used AI to automate tasks like trade settlement, SEC marketing compliance, and investment analysis. However, tools like Quest IndexGPT are only scratching the surface of what AI is capable of. Thanks to advancing technology, financial services companies can now automate much higher-level workflows than were previously possible – an update that has far-reaching implications in investment banking as well. These new innovations and use cases allow bankers to do more with less, to be more competitive, and to accelerate their timeline for reviewing, planning, winning, and distributing deals.
Major consulting firms are very optimistic about the future of AI in investment banking.
Deloitte estimates that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026 when adjusted for inflation. This improvement stems from enhanced productivity and efficiency from employees, which is a topic that we will discuss in more detail in the coming sections.
According to Deloitte’s study, greater employee productivity could result in additional revenue of $3.5 million per front-office employee by 2026. This enhanced efficiency can also help expedite the deal evaluation process – specifically when it comes to document drafting and regulatory filings – potentially allowing investment banks to close more deals each year. Outpacing their competitor's ability to perform diligence can help banks win more deals.
McKinsey is similarly optimistic and projects that enhanced employee productivity due to AI could increase revenues for investment banks by as much as $200 to $340 billion annually. From a high level, this enhanced revenue stems from automating manual tasks that monopolize bankers' day-to-day schedules, such as due diligence processes. Removing these time-intensive manual tasks helps free up a significant portion of investment bankers’s schedules, allowing them to focus on higher-level tasks.
The investment banking sector is uniquely positioned to experience an outsized benefit from AI, thanks to the volume of both internal and external data sources available in the industry. The financial services sector is perhaps the most data-intensive sector globally. Modern investment banks have access to massive treasure troves of data including market data, corporate news, public financial filings, historical transactions from the dealer’s desk, economic indicators, industry benchmarks, CRM data, alternative data sets, second and third-party data platforms, and more.
This plethora of data creates dozens of strategic ways that investment banks can leverage AI. However, to do so, investment banks first need to access the full potential of their data and staff.
Unlocking Data is Key
The financial services industry is overflowing with data, which is why it’s considered one of the industries most poised to benefit from the rise of generative AI. This is highly advantageous because AI solutions are only as good as the data that powers them. Data plays such a critical role when it comes to implementing AI that McKinsey listed it as a key ingredient for success when implementing generative AI at scale in investment banking. McKinsey stated:
“Most commercial and investment banks are already working to consolidate and optimize market, reference, and client data. But, they don’t always have a strategy for unstructured data such as text, images, client documents, and so on. All of these can be vital for successful gen AI applications; firms may need to establish the infrastructure for people across the firm to access unstructured data from anywhere on their platforms.”
Access to unstructured data plays a critical role in onboarding an AI solution to automate manual tasks. However, one area where we disagree with McKinsey is the implication that investment banks need to establish an infrastructure of people across the firm. We created Gradient’s Finance Reasoning Platform to help make it easier for investment banks to leverage the power of AI without allocating resources to a team of data scientists or ML engineers. This is highly advantageous when it comes to time-consuming tasks such as valuing private companies. Valuing private companies has grown more complicated in recent years, with companies staying private for longer. However, investment bankers can leverage data reasoning to review the unstructured data sources of private companies, build parallels to valuations of other companies, and include market transactions of private companies to generate up-to-date valuations. All of this can be automated and completed in just a fraction of the time – without bringing on additional team members – by using a solution like Gradient’s Finance Reasoning Platform.
Artificial Intelligence technology is evolving rapidly. While the AI technology from five years ago likely couldn’t assist with many of the complex functions required in investment banking, today’s AI solutions are capable of automating much more complex workflows. With that in mind let’s examine how AI can improve workflows in investment banking.
How AI Can Improve Investment Banking Workflows
How AI Can Improve Investment Banking Workflows
How AI Can Improve Investment Banking Workflows
How AI Can Improve Investment Banking Workflows
There are many steps in the investment banking process. But, for the sake of simplicity, we’ve broken the following section into three main use cases:
Step #1: Compiling Buyer’s Lists & Relationship Intelligence
Step #2: Conducting Due Diligence
Step #3: Negotiation and Closing
AI can assist in varying capacities when it comes to the many workflows that persist throughout investment banking. However, the challenge here is the intricacies and logic that are required to navigate these complex processes.
There are many steps in the investment banking process. But, for the sake of simplicity, we’ve broken the following section into three main use cases:
Step #1: Compiling Buyer’s Lists & Relationship Intelligence
Step #2: Conducting Due Diligence
Step #3: Negotiation and Closing
AI can assist in varying capacities when it comes to the many workflows that persist throughout investment banking. However, the challenge here is the intricacies and logic that are required to navigate these complex processes.
There are many steps in the investment banking process. But, for the sake of simplicity, we’ve broken the following section into three main use cases:
Step #1: Compiling Buyer’s Lists & Relationship Intelligence
Step #2: Conducting Due Diligence
Step #3: Negotiation and Closing
AI can assist in varying capacities when it comes to the many workflows that persist throughout investment banking. However, the challenge here is the intricacies and logic that are required to navigate these complex processes.
There are many steps in the investment banking process. But, for the sake of simplicity, we’ve broken the following section into three main use cases:
Step #1: Compiling Buyer’s Lists & Relationship Intelligence
Step #2: Conducting Due Diligence
Step #3: Negotiation and Closing
AI can assist in varying capacities when it comes to the many workflows that persist throughout investment banking. However, the challenge here is the intricacies and logic that are required to navigate these complex processes.
Step #1: Compiling Buyer's Lists & Relationship Intelligence
Compiling Buyer’s List
One of the most common uses for AI in investment banking is to compile buyers lists to help move deals along quickly.
AI solutions are best at analyzing large data sets and quickly drawing insights. Investment bankers can use this to their advantage by creating custom AI tools that examine data sets (such as publicly-available 13Fs or databases like Pitchbook) to make inferences on public or private investments and gain an understanding of which companies may be interested in investing in a given asset.
Solutions like Gradient’s Finance Reasoning Platform can automatically create detailed profiles of potential customers who might be interested in investing in an opportunity. This automation could save hundreds of hours of collective hours researching because, when done manually, this process could day a team of analysts weeks to complete. Investment banks also could leverage this AI solution to highlight trading opportunities, providing an even greater return on the investment of launching a custom AI tool.
In addition to compiling a buyer list, generative AI can also help automate outreach and solicit interest from potential suitors. A few examples of this include:
Drafting and distributing teasers of the deal to gauge interest
Drafting non-disclosure agreements (NDAs)
Drafting first drafts of pitch books
Relationship Mapping
Savvy investment bankers are already leveraging the power of AI to help gain insights into their existing network, business relationships, and client interactions to find, manage, and close deals – a field known as Relationship Intelligence.
The most powerful weapon in any investment banker’s arsenal is their personal and professional network, often referred to as Relationship Capital. However, the sheer size and fluctuating nature of your team’s network make it difficult to stay up-to-date. This is where Relationship Intelligence comes into play.
Relationship Intelligence is an emerging branch of artificial intelligence and machine learning that can help banking professionals centralize, sort, and interpret contact records. This includes keeping track of your network’s personal and professional moves, updating databases automatically, and helping uncover potentially fruitful relationships. Modern AI solutions can help automate the manual data entry required to keep records up-to-date as well as auto-draft follow-up communication (texts, emails, etc.) to keep a relationship strong.
However, keep in mind that only solutions capable of data reasoning – such as Gradient’s Finance Reasoning Platform – will be able to assist with Relationship Intelligence. Let’s quickly examine why that is.
The Power of Data Reasoning
Data reasoning is the practice of analyzing, interpreting, and applying logic to data, in order to draw insights that can help deliver informed decision-making. It goes beyond the basic data analysis that the majority of artificial intelligence solutions offer.
For example, the Gradient Finance Reasoning Platform can intelligently infer relationships, process data, and handle knowledge-based operations even when dealing with unstructured or incomplete datasets (which are commonly found in the investment banking industry). This means that Gradient’s platform can still draw actionable insights from a dataset even if that dataset hasn’t been properly cleaned or organized. Data reasoning will undoubtedly play a key role in helping the investment banking sector take better advantage of AI technology.
Now, let’s examine a handful of ways that artificial intelligence solutions can help in the due diligence phase of investment banking.
Step #2: Due Diligence
Improving Valuation Quality and Efficiency
Properly assessing the value of an asset and determining a fair selling or buying price is a critical workflow within investment banking.
Artificial intelligence, especially data reasoning systems, can help improve valuation standards to provide a fair valuation of an asset in order to take the deal to market. This includes applying valuation methods like discounted cash flow (DCF), precedent transactions, and comparable company analysis to determine the asset’s or company’s fair market value. Even if you prefer to have your team handle the initial valuation, you can leverage a custom-built AI solution to provide a secondary analysis and ensure that your team does not miss anything. However, some investment banks choose to have data reasoning platforms handle the entire process which can save their teams hours of analysis.
Data reasoning platforms can also help bankers predict the likelihood of success for any given deal, expected returns, risk measures, and other elements of the due diligence process that are manual to date. Again, solutions like Gradient’s Finance Reasoning Platform can take weeks’ worth of manual analysis and deliver it in just a fraction of the time – saving analysts days of work.
Finally, generative AI can help streamline the content creation that comes along with these analyses. This includes tasks like creating industry reports, investment theses, performance summaries, and due diligence reports.
Step #3: Negotiation and Closing
Artificial intelligence systems powered by data reasoning are uniquely positioned to assist in the negotiation and closing phase of the investment banking process. During this phase, artificial intelligence can help automate manual tasks in the following ways:
Automated document review: AI solutions can help proofread contracts and other documents to ensure accuracy for terms, prices, representations & warranties, or any other contingencies. Again, while you might still choose to have your team handle this process, AI analysis can serve as a guardrail to ensure that nothing slips through the cracks.
Ensuring regulatory compliance: Gradient’s Finance Reasoning Platform is one of the only AI solutions capable of ensuring that investment banking deals are compliant with SOC 1, SOC 2, GDPR, or any other relevant compliance frameworks.
Streamlined workflow: AI systems can ensure that all steps are completed efficiently by automating deadlines and follow-ups as well as auto-generating standard deal documents, such as closing binders or settlement letters, saving time and reducing errors.
Since investment banking deals can take months to close, artificial intelligence can also ensure that both the buyer and seller are still receiving a fair deal during the closing process. AI solutions can do this by analyzing fluctuations in market variables (such as interest rates, stock prices, or economic policies) and determining if new factors have meaningfully impacted the price of the asset. If this is the case, then the deal may need to be reevaluated.
Step #1: Compiling Buyer's Lists & Relationship Intelligence
Compiling Buyer’s List
One of the most common uses for AI in investment banking is to compile buyers lists to help move deals along quickly.
AI solutions are best at analyzing large data sets and quickly drawing insights. Investment bankers can use this to their advantage by creating custom AI tools that examine data sets (such as publicly-available 13Fs or databases like Pitchbook) to make inferences on public or private investments and gain an understanding of which companies may be interested in investing in a given asset.
Solutions like Gradient’s Finance Reasoning Platform can automatically create detailed profiles of potential customers who might be interested in investing in an opportunity. This automation could save hundreds of hours of collective hours researching because, when done manually, this process could day a team of analysts weeks to complete. Investment banks also could leverage this AI solution to highlight trading opportunities, providing an even greater return on the investment of launching a custom AI tool.
In addition to compiling a buyer list, generative AI can also help automate outreach and solicit interest from potential suitors. A few examples of this include:
Drafting and distributing teasers of the deal to gauge interest
Drafting non-disclosure agreements (NDAs)
Drafting first drafts of pitch books
Relationship Mapping
Savvy investment bankers are already leveraging the power of AI to help gain insights into their existing network, business relationships, and client interactions to find, manage, and close deals – a field known as Relationship Intelligence.
The most powerful weapon in any investment banker’s arsenal is their personal and professional network, often referred to as Relationship Capital. However, the sheer size and fluctuating nature of your team’s network make it difficult to stay up-to-date. This is where Relationship Intelligence comes into play.
Relationship Intelligence is an emerging branch of artificial intelligence and machine learning that can help banking professionals centralize, sort, and interpret contact records. This includes keeping track of your network’s personal and professional moves, updating databases automatically, and helping uncover potentially fruitful relationships. Modern AI solutions can help automate the manual data entry required to keep records up-to-date as well as auto-draft follow-up communication (texts, emails, etc.) to keep a relationship strong.
However, keep in mind that only solutions capable of data reasoning – such as Gradient’s Finance Reasoning Platform – will be able to assist with Relationship Intelligence. Let’s quickly examine why that is.
The Power of Data Reasoning
Data reasoning is the practice of analyzing, interpreting, and applying logic to data, in order to draw insights that can help deliver informed decision-making. It goes beyond the basic data analysis that the majority of artificial intelligence solutions offer.
For example, the Gradient Finance Reasoning Platform can intelligently infer relationships, process data, and handle knowledge-based operations even when dealing with unstructured or incomplete datasets (which are commonly found in the investment banking industry). This means that Gradient’s platform can still draw actionable insights from a dataset even if that dataset hasn’t been properly cleaned or organized. Data reasoning will undoubtedly play a key role in helping the investment banking sector take better advantage of AI technology.
Now, let’s examine a handful of ways that artificial intelligence solutions can help in the due diligence phase of investment banking.
Step #2: Due Diligence
Improving Valuation Quality and Efficiency
Properly assessing the value of an asset and determining a fair selling or buying price is a critical workflow within investment banking.
Artificial intelligence, especially data reasoning systems, can help improve valuation standards to provide a fair valuation of an asset in order to take the deal to market. This includes applying valuation methods like discounted cash flow (DCF), precedent transactions, and comparable company analysis to determine the asset’s or company’s fair market value. Even if you prefer to have your team handle the initial valuation, you can leverage a custom-built AI solution to provide a secondary analysis and ensure that your team does not miss anything. However, some investment banks choose to have data reasoning platforms handle the entire process which can save their teams hours of analysis.
Data reasoning platforms can also help bankers predict the likelihood of success for any given deal, expected returns, risk measures, and other elements of the due diligence process that are manual to date. Again, solutions like Gradient’s Finance Reasoning Platform can take weeks’ worth of manual analysis and deliver it in just a fraction of the time – saving analysts days of work.
Finally, generative AI can help streamline the content creation that comes along with these analyses. This includes tasks like creating industry reports, investment theses, performance summaries, and due diligence reports.
Step #3: Negotiation and Closing
Artificial intelligence systems powered by data reasoning are uniquely positioned to assist in the negotiation and closing phase of the investment banking process. During this phase, artificial intelligence can help automate manual tasks in the following ways:
Automated document review: AI solutions can help proofread contracts and other documents to ensure accuracy for terms, prices, representations & warranties, or any other contingencies. Again, while you might still choose to have your team handle this process, AI analysis can serve as a guardrail to ensure that nothing slips through the cracks.
Ensuring regulatory compliance: Gradient’s Finance Reasoning Platform is one of the only AI solutions capable of ensuring that investment banking deals are compliant with SOC 1, SOC 2, GDPR, or any other relevant compliance frameworks.
Streamlined workflow: AI systems can ensure that all steps are completed efficiently by automating deadlines and follow-ups as well as auto-generating standard deal documents, such as closing binders or settlement letters, saving time and reducing errors.
Since investment banking deals can take months to close, artificial intelligence can also ensure that both the buyer and seller are still receiving a fair deal during the closing process. AI solutions can do this by analyzing fluctuations in market variables (such as interest rates, stock prices, or economic policies) and determining if new factors have meaningfully impacted the price of the asset. If this is the case, then the deal may need to be reevaluated.
Step #1: Compiling Buyer's Lists & Relationship Intelligence
Compiling Buyer’s List
One of the most common uses for AI in investment banking is to compile buyers lists to help move deals along quickly.
AI solutions are best at analyzing large data sets and quickly drawing insights. Investment bankers can use this to their advantage by creating custom AI tools that examine data sets (such as publicly-available 13Fs or databases like Pitchbook) to make inferences on public or private investments and gain an understanding of which companies may be interested in investing in a given asset.
Solutions like Gradient’s Finance Reasoning Platform can automatically create detailed profiles of potential customers who might be interested in investing in an opportunity. This automation could save hundreds of hours of collective hours researching because, when done manually, this process could day a team of analysts weeks to complete. Investment banks also could leverage this AI solution to highlight trading opportunities, providing an even greater return on the investment of launching a custom AI tool.
In addition to compiling a buyer list, generative AI can also help automate outreach and solicit interest from potential suitors. A few examples of this include:
Drafting and distributing teasers of the deal to gauge interest
Drafting non-disclosure agreements (NDAs)
Drafting first drafts of pitch books
Relationship Mapping
Savvy investment bankers are already leveraging the power of AI to help gain insights into their existing network, business relationships, and client interactions to find, manage, and close deals – a field known as Relationship Intelligence.
The most powerful weapon in any investment banker’s arsenal is their personal and professional network, often referred to as Relationship Capital. However, the sheer size and fluctuating nature of your team’s network make it difficult to stay up-to-date. This is where Relationship Intelligence comes into play.
Relationship Intelligence is an emerging branch of artificial intelligence and machine learning that can help banking professionals centralize, sort, and interpret contact records. This includes keeping track of your network’s personal and professional moves, updating databases automatically, and helping uncover potentially fruitful relationships. Modern AI solutions can help automate the manual data entry required to keep records up-to-date as well as auto-draft follow-up communication (texts, emails, etc.) to keep a relationship strong.
However, keep in mind that only solutions capable of data reasoning – such as Gradient’s Finance Reasoning Platform – will be able to assist with Relationship Intelligence. Let’s quickly examine why that is.
The Power of Data Reasoning
Data reasoning is the practice of analyzing, interpreting, and applying logic to data, in order to draw insights that can help deliver informed decision-making. It goes beyond the basic data analysis that the majority of artificial intelligence solutions offer.
For example, the Gradient Finance Reasoning Platform can intelligently infer relationships, process data, and handle knowledge-based operations even when dealing with unstructured or incomplete datasets (which are commonly found in the investment banking industry). This means that Gradient’s platform can still draw actionable insights from a dataset even if that dataset hasn’t been properly cleaned or organized. Data reasoning will undoubtedly play a key role in helping the investment banking sector take better advantage of AI technology.
Now, let’s examine a handful of ways that artificial intelligence solutions can help in the due diligence phase of investment banking.
Step #2: Due Diligence
Improving Valuation Quality and Efficiency
Properly assessing the value of an asset and determining a fair selling or buying price is a critical workflow within investment banking.
Artificial intelligence, especially data reasoning systems, can help improve valuation standards to provide a fair valuation of an asset in order to take the deal to market. This includes applying valuation methods like discounted cash flow (DCF), precedent transactions, and comparable company analysis to determine the asset’s or company’s fair market value. Even if you prefer to have your team handle the initial valuation, you can leverage a custom-built AI solution to provide a secondary analysis and ensure that your team does not miss anything. However, some investment banks choose to have data reasoning platforms handle the entire process which can save their teams hours of analysis.
Data reasoning platforms can also help bankers predict the likelihood of success for any given deal, expected returns, risk measures, and other elements of the due diligence process that are manual to date. Again, solutions like Gradient’s Finance Reasoning Platform can take weeks’ worth of manual analysis and deliver it in just a fraction of the time – saving analysts days of work.
Finally, generative AI can help streamline the content creation that comes along with these analyses. This includes tasks like creating industry reports, investment theses, performance summaries, and due diligence reports.
Step #3: Negotiation and Closing
Artificial intelligence systems powered by data reasoning are uniquely positioned to assist in the negotiation and closing phase of the investment banking process. During this phase, artificial intelligence can help automate manual tasks in the following ways:
Automated document review: AI solutions can help proofread contracts and other documents to ensure accuracy for terms, prices, representations & warranties, or any other contingencies. Again, while you might still choose to have your team handle this process, AI analysis can serve as a guardrail to ensure that nothing slips through the cracks.
Ensuring regulatory compliance: Gradient’s Finance Reasoning Platform is one of the only AI solutions capable of ensuring that investment banking deals are compliant with SOC 1, SOC 2, GDPR, or any other relevant compliance frameworks.
Streamlined workflow: AI systems can ensure that all steps are completed efficiently by automating deadlines and follow-ups as well as auto-generating standard deal documents, such as closing binders or settlement letters, saving time and reducing errors.
Since investment banking deals can take months to close, artificial intelligence can also ensure that both the buyer and seller are still receiving a fair deal during the closing process. AI solutions can do this by analyzing fluctuations in market variables (such as interest rates, stock prices, or economic policies) and determining if new factors have meaningfully impacted the price of the asset. If this is the case, then the deal may need to be reevaluated.
Step #1: Compiling Buyer's Lists & Relationship Intelligence
Compiling Buyer’s List
One of the most common uses for AI in investment banking is to compile buyers lists to help move deals along quickly.
AI solutions are best at analyzing large data sets and quickly drawing insights. Investment bankers can use this to their advantage by creating custom AI tools that examine data sets (such as publicly-available 13Fs or databases like Pitchbook) to make inferences on public or private investments and gain an understanding of which companies may be interested in investing in a given asset.
Solutions like Gradient’s Finance Reasoning Platform can automatically create detailed profiles of potential customers who might be interested in investing in an opportunity. This automation could save hundreds of hours of collective hours researching because, when done manually, this process could day a team of analysts weeks to complete. Investment banks also could leverage this AI solution to highlight trading opportunities, providing an even greater return on the investment of launching a custom AI tool.
In addition to compiling a buyer list, generative AI can also help automate outreach and solicit interest from potential suitors. A few examples of this include:
Drafting and distributing teasers of the deal to gauge interest
Drafting non-disclosure agreements (NDAs)
Drafting first drafts of pitch books
Relationship Mapping
Savvy investment bankers are already leveraging the power of AI to help gain insights into their existing network, business relationships, and client interactions to find, manage, and close deals – a field known as Relationship Intelligence.
The most powerful weapon in any investment banker’s arsenal is their personal and professional network, often referred to as Relationship Capital. However, the sheer size and fluctuating nature of your team’s network make it difficult to stay up-to-date. This is where Relationship Intelligence comes into play.
Relationship Intelligence is an emerging branch of artificial intelligence and machine learning that can help banking professionals centralize, sort, and interpret contact records. This includes keeping track of your network’s personal and professional moves, updating databases automatically, and helping uncover potentially fruitful relationships. Modern AI solutions can help automate the manual data entry required to keep records up-to-date as well as auto-draft follow-up communication (texts, emails, etc.) to keep a relationship strong.
However, keep in mind that only solutions capable of data reasoning – such as Gradient’s Finance Reasoning Platform – will be able to assist with Relationship Intelligence. Let’s quickly examine why that is.
The Power of Data Reasoning
Data reasoning is the practice of analyzing, interpreting, and applying logic to data, in order to draw insights that can help deliver informed decision-making. It goes beyond the basic data analysis that the majority of artificial intelligence solutions offer.
For example, the Gradient Finance Reasoning Platform can intelligently infer relationships, process data, and handle knowledge-based operations even when dealing with unstructured or incomplete datasets (which are commonly found in the investment banking industry). This means that Gradient’s platform can still draw actionable insights from a dataset even if that dataset hasn’t been properly cleaned or organized. Data reasoning will undoubtedly play a key role in helping the investment banking sector take better advantage of AI technology.
Now, let’s examine a handful of ways that artificial intelligence solutions can help in the due diligence phase of investment banking.
Step #2: Due Diligence
Improving Valuation Quality and Efficiency
Properly assessing the value of an asset and determining a fair selling or buying price is a critical workflow within investment banking.
Artificial intelligence, especially data reasoning systems, can help improve valuation standards to provide a fair valuation of an asset in order to take the deal to market. This includes applying valuation methods like discounted cash flow (DCF), precedent transactions, and comparable company analysis to determine the asset’s or company’s fair market value. Even if you prefer to have your team handle the initial valuation, you can leverage a custom-built AI solution to provide a secondary analysis and ensure that your team does not miss anything. However, some investment banks choose to have data reasoning platforms handle the entire process which can save their teams hours of analysis.
Data reasoning platforms can also help bankers predict the likelihood of success for any given deal, expected returns, risk measures, and other elements of the due diligence process that are manual to date. Again, solutions like Gradient’s Finance Reasoning Platform can take weeks’ worth of manual analysis and deliver it in just a fraction of the time – saving analysts days of work.
Finally, generative AI can help streamline the content creation that comes along with these analyses. This includes tasks like creating industry reports, investment theses, performance summaries, and due diligence reports.
Step #3: Negotiation and Closing
Artificial intelligence systems powered by data reasoning are uniquely positioned to assist in the negotiation and closing phase of the investment banking process. During this phase, artificial intelligence can help automate manual tasks in the following ways:
Automated document review: AI solutions can help proofread contracts and other documents to ensure accuracy for terms, prices, representations & warranties, or any other contingencies. Again, while you might still choose to have your team handle this process, AI analysis can serve as a guardrail to ensure that nothing slips through the cracks.
Ensuring regulatory compliance: Gradient’s Finance Reasoning Platform is one of the only AI solutions capable of ensuring that investment banking deals are compliant with SOC 1, SOC 2, GDPR, or any other relevant compliance frameworks.
Streamlined workflow: AI systems can ensure that all steps are completed efficiently by automating deadlines and follow-ups as well as auto-generating standard deal documents, such as closing binders or settlement letters, saving time and reducing errors.
Since investment banking deals can take months to close, artificial intelligence can also ensure that both the buyer and seller are still receiving a fair deal during the closing process. AI solutions can do this by analyzing fluctuations in market variables (such as interest rates, stock prices, or economic policies) and determining if new factors have meaningfully impacted the price of the asset. If this is the case, then the deal may need to be reevaluated.
Final Thoughts: AI in Investment Banking
Final Thoughts: AI in Investment Banking
Final Thoughts: AI in Investment Banking
Final Thoughts: AI in Investment Banking
There are a plethora of ways that investment banks can leverage AI to improve workflows in investment banking including compiling buyer’s lists, optimizing Relationship Intelligence, automating due diligence, and creating documents during the final stages of a deal. The strategies discussed in this article also don’t take into account the ways that AI can streamline operational and administrative tasks.
For investment bankers, the emergence of data reasoning creates a significant opportunity to automate higher-order workflows that currently take up hours, if not days, of your team’s valuable time. The early adopters who leverage data reasoning will undoubtedly gain a significant competitive advantage over others in the industry and enjoy enhanced productivity, lower overhead, and greater overall profitability.
We hope that you’ve found this article valuable when it comes to learning how AI data reasoning solutions like Gradient can help investment bankers improve their workflows.
Interested in learning how a high-performing, cost-effective custom AI system could benefit your business? Contact the Gradient team today to learn more.
There are a plethora of ways that investment banks can leverage AI to improve workflows in investment banking including compiling buyer’s lists, optimizing Relationship Intelligence, automating due diligence, and creating documents during the final stages of a deal. The strategies discussed in this article also don’t take into account the ways that AI can streamline operational and administrative tasks.
For investment bankers, the emergence of data reasoning creates a significant opportunity to automate higher-order workflows that currently take up hours, if not days, of your team’s valuable time. The early adopters who leverage data reasoning will undoubtedly gain a significant competitive advantage over others in the industry and enjoy enhanced productivity, lower overhead, and greater overall profitability.
We hope that you’ve found this article valuable when it comes to learning how AI data reasoning solutions like Gradient can help investment bankers improve their workflows.
Interested in learning how a high-performing, cost-effective custom AI system could benefit your business? Contact the Gradient team today to learn more.
There are a plethora of ways that investment banks can leverage AI to improve workflows in investment banking including compiling buyer’s lists, optimizing Relationship Intelligence, automating due diligence, and creating documents during the final stages of a deal. The strategies discussed in this article also don’t take into account the ways that AI can streamline operational and administrative tasks.
For investment bankers, the emergence of data reasoning creates a significant opportunity to automate higher-order workflows that currently take up hours, if not days, of your team’s valuable time. The early adopters who leverage data reasoning will undoubtedly gain a significant competitive advantage over others in the industry and enjoy enhanced productivity, lower overhead, and greater overall profitability.
We hope that you’ve found this article valuable when it comes to learning how AI data reasoning solutions like Gradient can help investment bankers improve their workflows.
Interested in learning how a high-performing, cost-effective custom AI system could benefit your business? Contact the Gradient team today to learn more.
There are a plethora of ways that investment banks can leverage AI to improve workflows in investment banking including compiling buyer’s lists, optimizing Relationship Intelligence, automating due diligence, and creating documents during the final stages of a deal. The strategies discussed in this article also don’t take into account the ways that AI can streamline operational and administrative tasks.
For investment bankers, the emergence of data reasoning creates a significant opportunity to automate higher-order workflows that currently take up hours, if not days, of your team’s valuable time. The early adopters who leverage data reasoning will undoubtedly gain a significant competitive advantage over others in the industry and enjoy enhanced productivity, lower overhead, and greater overall profitability.
We hope that you’ve found this article valuable when it comes to learning how AI data reasoning solutions like Gradient can help investment bankers improve their workflows.
Interested in learning how a high-performing, cost-effective custom AI system could benefit your business? Contact the Gradient team today to learn more.
FAQ: Data Reasoning in Investment Banking
FAQ: Data Reasoning in Investment Banking
FAQ: Data Reasoning in Investment Banking
FAQ: Data Reasoning in Investment Banking
Can Data Reasoning Improve Investment Banking Workflows?
Yes, data reasoning can help improve workflows in investment banking by assisting with Relationship Intelligence, compiling buyer’s lists, document review and analysis, and more.
Is AI used in Investment Banking?
Industry experts believe that there are widespread uses for AI in investment banking. For example, Deloitte estimates that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026 when adjusted for inflation. But, despite this, AI adoption has been slow in the investment banking sector.
What Are the Top AI Platforms for Investment Banks?
Gradient is widely considered a top AI platform for investment banks due to its ability to automate complex reasoning tasks using unstructured data sources.
Can Data Reasoning Improve Investment Banking Workflows?
Yes, data reasoning can help improve workflows in investment banking by assisting with Relationship Intelligence, compiling buyer’s lists, document review and analysis, and more.
Is AI used in Investment Banking?
Industry experts believe that there are widespread uses for AI in investment banking. For example, Deloitte estimates that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026 when adjusted for inflation. But, despite this, AI adoption has been slow in the investment banking sector.
What Are the Top AI Platforms for Investment Banks?
Gradient is widely considered a top AI platform for investment banks due to its ability to automate complex reasoning tasks using unstructured data sources.
Can Data Reasoning Improve Investment Banking Workflows?
Yes, data reasoning can help improve workflows in investment banking by assisting with Relationship Intelligence, compiling buyer’s lists, document review and analysis, and more.
Is AI used in Investment Banking?
Industry experts believe that there are widespread uses for AI in investment banking. For example, Deloitte estimates that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026 when adjusted for inflation. But, despite this, AI adoption has been slow in the investment banking sector.
What Are the Top AI Platforms for Investment Banks?
Gradient is widely considered a top AI platform for investment banks due to its ability to automate complex reasoning tasks using unstructured data sources.
Can Data Reasoning Improve Investment Banking Workflows?
Yes, data reasoning can help improve workflows in investment banking by assisting with Relationship Intelligence, compiling buyer’s lists, document review and analysis, and more.
Is AI used in Investment Banking?
Industry experts believe that there are widespread uses for AI in investment banking. For example, Deloitte estimates that AI can help boost front-office productivity in the investment banking sector by as much as 35% by 2026 when adjusted for inflation. But, despite this, AI adoption has been slow in the investment banking sector.
What Are the Top AI Platforms for Investment Banks?
Gradient is widely considered a top AI platform for investment banks due to its ability to automate complex reasoning tasks using unstructured data sources.
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© 2025 Gradient. All rights reserved.