The Financial Services Industry Can’t Afford Not To Automate in 2025
Dec 23, 2024
Gradient Team
We are in the midst of a technological revolution that rivals the invention of the internet. Over the coming years, companies that do not adopt AI will likely find themselves in a similar situation to companies that did not develop an internet presence in the 1990s. Namely, falling behind their competitors and struggling to catch up.
The impact of AI adoption will be especially strong in the financial services industry, which is uniquely positioned to benefit from automation through AI. This is largely due to the number of opportunities for automation as well as the sheer amount of data collected by the industry – something that consulting firm Deloitte referred to as the “Explosion of Data”.
In addition to data, modern financial companies also have access to cost-efficient artificial intelligence solutions capable of automating higher-level operations for the first time in human history. Solutions like Gradient’s Finance Data Reasoning Platform can help financial institutions automate complex workflows in just three simple steps.
This article will explore why the financial services industry can’t afford not to automate in 2025.
How Much Manpower Are You Wasting?
The financial services industry is rife with bottlenecks, much more so than other industries. Strict reporting, compliance, and professionalism requirements ensure that nearly every single task gets put under a microscope, leading to tedious processes that require extensive manual effort on behalf of financial services professionals.
One study by a leading automation provider shows that nearly 70% of employees in the financial services industry waste up to 8 hours per week on document processing alone. This highlights just one of the hundreds of tasks that can – and should – be automated by finance data reasoning platforms. Other common bottlenecks in the financial services industry include:
Creating, Closing, or Maintaining Customer Accounts: Every new customer requires extensive ongoing documentation and attention.
Maintaining Compliance: Reports with regulatory bodies like the SEC and FINRA must be filed consistently and accurately.
Uploading and Manipulating Data: Data like client information, financial statements, or transaction records must constantly be uploaded to spreadsheets or internal systems.
Creating Reports: Customer and internal performance reports need to be compiled and distributed on a consistent basis.
Monitoring Transactions for Fraud: Companies must review and flag suspicious-looking transactions for potential fraud, a task that is often the responsibility of employees.
At nearly every turn, financial services professionals find themselves bogged down by monotonous tasks that should have been automated by artificial intelligence long ago. Depending on the size of the company, this lack of efficiency can result in an immense amount of time, money, and manpower wasted as time goes on. But, it doesn’t have to be this way.
Modern finance reasoning AI solutions are often capable of reducing the time spent on manual data tasks by 70% or more. This begs the question: why aren’t finance companies clamoring to create custom AI solutions?
Why Don’t More Financial Institutions Automate Manual Processes?
Many finance companies are using RPA (robotic process automation), but its limitations often prevent them from achieving more advanced automation. For those who are currently using AI, they are typically only exploring tools like ChatGPT - remaining cautious about implementing a more custom AI solutions for higher-level operational tasks. Of the financial institutions we've had the opportunity to speak to, most hesitate to automate more workflows for three key reasons:
Compliance Requirements: Finance companies are required to meet strict compliance requirements like SOC 1, SOC 2, or GDPR. These requirements can be complex and leadership teams often believe that it’s best to leave compliance to a financial services professional…not a computer system.
Upfront Investment: Many finance professionals believe that implementing an AI solution always requires a massive amount of time and money. For example, a 2023 McKinsey study estimated that onboarding a general-purpose customer service chatbot would cost approximately $2 million and require a team of 8 employees working for 9 months. According to the study, bringing on a more complex model could cost up to $200 million. However, depending on the workflows, modern AI solutions can often be customized at a much lower price point.
Unstructured Data Sources: The bulk of internal data sources in the finance industry are unstructured, which means that they are not in a format that can be interpreted by a typical large language model. Cleaning and organizing data is often an expensive and resource-intensive process and, due to this roadblock, many financial institutions believe that they are not able to leverage their internal data sources. However, solutions like Gradient’s Finance Reasoning Platform can now apply reasoning to large data sets and draw insights without the need to clean the data.
Due to these three obstacles, many financial institutions simply assume that it’s not possible to automate their workflows. But, this is usually not the case and Gradient’s team has successfully helped financial institutions automate a wide range of tasks.
Read the following article to learn more about how Gradient’s Finance Reasoning Platform can help you leverage the power of AI in 3 simple steps.
Otherwise, let’s discuss why the financial services industry is perfectly positioned to benefit from automation.
Why Financial Services Firms Need to Automate
1.) They Have An Overwhelming Amount of Data
If data is the new oil in today’s economy then the financial services industry is OPEC.
The financial services industry is experiencing an explosion of big data and modern companies are capable of collecting a mind-numbing amount of data related to their customers, stakeholders, and the overall market. According to the Corporate Finance Institute, the most common forms of big data in finance are:
Real-time stock market insights (a data source that will only get bigger as the industry inches toward 24/7 stock market trading)
Big data analytics in financial models
Customer analytics
Risk management and fraud detection
Like finding crude oil deposits on a plot of land, the prevalence of data represents an immense opportunity for companies that can harness it to their advantage. All of this data is ammunition that can – and should – be leveraged. It’s just a matter of finding the right solution that can help unlock the value behind this data, without breaking the bank.
2.) Increasing Customer Expectations
Rapidly advancing technology has changed customers’ expectations in the financial services industry. Gone are the days when bank customers were satisfied with a monthly account statement in the mail or the occasional trip to a bank branch. Today’s consumer now expects round-the-clock access to their banking tools, easy-to-use apps, and comprehensive digital experiences that rival that of the world’s best technology companies.
Increased customer expectations have led to trends like embedded finance, which is the integration of financial services, like payments, lending, insurance, or banking services, into non-financial offerings. Today’s consumers expect access to financial services products at any point in time. In response, modern finance companies need to operate as efficiently as possible in order to meet rising customer expectations while still prioritizing their bottom line.
3.) A Wide Range of Opportunities
There are dozens of ways that finance institutions can leverage AI to automate manual processes and streamline operations. Here are just a few of the ways that Gradient has helped its customers so far:
Anti-Money Laundering: Gradient helped a large US bank serving 40,000 corporate and institutional clients reduce transaction risk and increase the accuracy of detecting suspicious activity.
Trade Settlement: Gradient helped a prominent asset manager adopt a generative AI-driven solution to automate and enhance their trade settlement process, leading to significant operational improvements and cost savings.
Investment Analysis: Gradient worked directly with a US-based asset management fund with an AUM of ~$40bn to leverage AI to develop an internal tool to help improve investment performance – streamlining the process to analyze data and enhance their decision-making capabilities.
Customer Service Co-Pilots: Gradient was able to implement an AI-based strategy to enhance triaging for customer requests using the vast amount of unstructured data from customer interactions.
SEC Marketing Compliance: Gradient was able to develop a custom workflow that automated the redlining process for marketing materials that are required to go through this process, using marketing compliance guidelines and internal frameworks.
With so many proven use cases available, it’s getting harder and harder for financial professionals to claim that they have no use for AI.
4.) An Existing Tech Stack
For the first time in human history, modern finance companies have everything they need to deploy custom AI solutions at scale. This includes powerful yet affordable computing power, easy access to cloud computing and data storage offerings, as well as AI services providers who can provide high-performing, cost-effective custom AI systems.
Benefits of Automation in Finance
Automating manual processes at scale will allow financial institutions to free up an immense amount of time and resources. These benefits change depending on the sector. For example, EY highlights a few examples of how different sectors could benefit from AI:
Consumer Banking: Elevated service delivery and customer interaction.
Investment Banking: Streamlined research and financial modeling, helping to improve investment performance.
Corporate and SMB Banking: Enhanced business lending and risk management.
Capital Markets: Broad improvements related to trading, risk management, and compliance.
Regardless of the sector, financial services providers stand to benefit from artificial intelligence through the automation of higher-level operations. At scale, this level of automation unlocks immense value in the following ways:
Cost Optimization: Automating back-office processes that traditionally require large teams to manage can help companies reduce costs and improve cash flow.
Revenue Enhancement: Maximizing the full potential of data to deliver investment insights, improve customer experiences, and create innovative financial products can help lead to
Reduced Employee Burnout: Freeing employees from manual tasks gives them more control over how they spend their day-to-day, allowing them to focus on more mission-critical tasks. This also helps financial service professionals (who are historically overworked) achieve a better work-life balance, which should translate to reduced turnover.
Meet Gradient: Automating Manual Processes For Finance Companies
Gradient’s Finance Reasoning Platform leverages artificial intelligence to automate complex reasoning tasks in the financial sector. What separates Gradient’s platform from others in the market is its capacity to transform unstructured data sources without the need to clean or reshape the data. This allows Gradient to leverage data sources that were previously unusable.
Gradient’s platform helps financial services companies automate complex workflows in three easy steps:
Integration: Easily consolidate data from infinite sources without pre-processing techniques that are normally required to structure data.
Reasoning: Gradient’s platform combines your own knowledge and finance expertise to quickly learn how to execute financial processes by analyzing large data sets.
Automation & Continuous Improvement: After deployment, Gradient provides comprehensive monitoring to ensure your AI systems are operating at their highest potential.
Gradient’s data reasoning platform is capable of automating higher-level operations while still maintaining strict compliance requirements that are common throughout the finance industry. Additionally, Gradient’s team works as strategic partners to your company helping you create and maintain your custom AI solution without needing to hire an internal team of computer scientists or developers.
Final Thoughts: AI Automation in Finance
It’s becoming increasingly clear that artificial intelligence is much more than the latest tech trend – it’s a technological revolution with the power to redefine the financial sector. To quote EY’s Financial Services AI Leader, Dr. Kostis Chlouverakis:
“The strategic deployment of GenAI is much more than a trend; it is a comprehensive reimagining of operations, product development, and risk management, allowing banks to deliver personalized services and novel solutions while streamlining mundane tasks.”
We hope that you’ve found this article valuable when it comes to learning how the financial services industry can’t afford not to automate in 2025.
FAQ: Finance Data Reasoning Platform
What is 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. At Gradient, we like to describe data reasoning as moving from basic operational tasks to higher-order operational tasks.
How Can Data Reasoning Help Me?
Data reasoning is powering the future of automation and allowing AI models to move beyond simple tasks. Enterprises that can harness data reasoning will benefit from smarter workflows, better customer experiences, and more agile operations.
Is Data Reasoning Better Than RPA?
While RPA can automate tasks, it often struggles with scalability across complex, enterprise-wide operations. If you’re interested in automating higher-level operations then a data reasoning solution is best for you.
