According to the 2024 State of Automation in Financial Services report by SMA Technologies, 100% of financial institutions and insurance companies have adopted some level of investment banking automation, with 41–50% of operations now automated on average. Additionally, 53% of financial services executives highlight improving processes across the business as a key innovation goal.
A survey by Farsight AI shows that 23% of professionals prioritize AI technologies in the investment banking sector alone, and many banks are increasing their budgets for automation by 6–10%. The combination of big data with AI and ML development services transforms investment platforms, as these technologies offer smarter and faster decision-making.
I’m Oleh Komenchuk, an ML Engineer at Uptech. In this article, I’ll break down how AI-powered data automation affects investment banking. We’ll explore the challenges it solves, what it can and can’t do, and real-world examples of its impact. Let’s get started!
What is Data Automation?
Data automation handles the heavy lifting of data management – data collection, organization, and analysis – using various technologies, including advanced technology like Artificial Intelligence.
These processes would otherwise take significant time and manual effort. For businesses, especially in sectors like investment banking, data automation is an irreplaceable element, especially if they want their data management to be efficient, scalable, and accurate.
So, let’s examine the technologies and infrastructure that enable automation in investment banking.
4 core technologies behind data automation in investment banking
Data automation in investment banking relies on a wide array of technologies that work together like the gears of a finely tuned timepiece. Each gear, from the simplest to the most intricate, plays a vital role in the transformation of raw data into actionable insights.
These technologies mainly include, but are not limited to:
- Optical Character Recognition (OCR)
- Robotic Process Automation (RPA)
- Natural Language Processing (NLP)
- Artificial Intelligence (AI) with Machine Learning (ML)
1. Optical Character Recognition (OCR): The data extractor
OCR is the entry point for automation: Its main task is to convert physical or scanned documents into machine-readable formats. It recognizes printed or handwritten text within PDFs or images for further use.
In investment banking, OCR enables data extraction from contracts, invoices, and financial statements. For example, OCR can identify account numbers, dates, and amounts from client agreements. As such, it prepares unstructured data for downstream automation processes.
2. Robotic Process Automation (RPA): The task handler
Robotic Process Automation (RPA) refers to software designed to replicate human actions for performing repetitive tasks. RPA automates things like data entry, system navigation, and report generation.
In investment banking, RPA helps process client onboarding forms, reconcile trades, or prepare regulatory reports. For instance, an RPA bot could collect transaction data, validate it, and transfer it into the required format for reporting systems – saving hours of manual effort.
3. Natural Language Processing (NLP): The language analyst
NLP allows systems to process and understand human language, whether written or spoken. It analyzes unstructured data like legal contracts, earnings reports, and even market news.
In investment banking, NLP can interpret lengthy compliance documents, extract clauses, or analyze real-time sentiment from market news. For example, it could summarize an earnings report's highlights, enabling analysts to identify trends more quickly.
4. Artificial Intelligence and Machine Learning (AI/ML): The strategist and predictor
AI and ML take automation to the highest level as they allow systems to analyze vast datasets, identify patterns, and make predictions. Using advanced algorithms like neural networks (e.g., Transformer models), decision trees, clustering algorithms, Statsmodels, or systems like Prophet, AI/ML provide deeper insights and smarter automation.
In investment banking, these technologies aid with fraud detection, credit risk assessment, and portfolio optimization, among other things.
Infrastructure for data automation
Cloud computing. Providers like AWS, Google Cloud, and Microsoft Azure offer the flexibility to store, process, and scale data pipelines without significant upfront investment in hardware.
Data lakes and warehouses. You need to store all the information somewhere, right? That’s when cloud data repositories like data warehouses and data lakes come into play. Cloud data warehouses like Snowflake, AWS S3, GCP Storage, and Azure Data Lake centralize structured and unstructured data, which makes it easier to access and automate analytics.
ETL/ELT pipelines. The ETL/ELT abbreviations stand for “extract,” “transform,” and “load,” but they are in different positions based on the pipeline type. When done manually, this process requires a specialist to find a document, extract the necessary information from it, transform it into a required format, and then load it into a final destination.
Tools like Apache Airflow, Talend, or Informatica automate the process of extracting data from various sources, transforming it for analysis, and loading it into storage systems.
Big Data tools. Platforms like Apache Hadoop and Apache Spark can process large datasets and make automation at scale happen.
Real-time data streaming. Platforms like Apache Kafka or AWS Kinesis enable real-time data ingestion and processing, which is crucial for providing actionable insights on the fly. Also, NoSQL databases like Redis ensure fast access to data, which is critical for time-sensitive tasks like trading and risk assessment.
ML frameworks and libraries. Machine learning frameworks provide the foundation for building and deploying AI models.
- Deep learning tools include PyTorch and TensorFlow used for tasks like predictive analytics, fraud detection, and sentiment analysis.
- Time-series analysis requires libraries like Statsmodels and Prophet to analyze time-based data and make more accurate forecasts of market trends.
MLOps tools. Platforms like MLflow, Weight & Biases, and Neptune.ai are inevitable for the deployment, monitoring, and versioning of machine learning models. They track experiments, version models, and monitor performance.
From my perspective as an ML Engineer, the abovementioned blend of technology and infrastructure allows businesses to go beyond just saving time. It empowers them to
- unlock insights that couldn’t otherwise be detected,
- make smarter decisions,
- adapt to growing data demands, among other things.
When it comes to investment banking automation, these systems drive risk management, customer personalization, and faster trade executions, giving institutions a competitive edge.
How Investment Banking Automation Works: Manual vs. AI-Powered
To show how data automation can impact investment banking, we first look at how things are done manually and then compare the two. We’ll take the credit risk assessment process as an example.
As you know, risk assessment is when banks evaluate the likelihood that a borrower will default on a loan or fail to meet financial obligations. Traditionally, there’s a lot of manual effort and time-consuming tasks here. So, let’s compare how it works manually and how AI can transform it.
Manual credit risk assessment
The traditional process relies heavily on human intervention, which increases the risk of errors and slows down lending operations. This is particularly true for large volumes of loan applications.
So, this is what credit risk assessment looks like when done manually:
- Data collection. Loan applicants provide financial statements, credit reports, and other documentation.
- Document review. Bank staff manually analyze the documents to assess the applicant’s creditworthiness.
- Input and processing. Analysts input key data into spreadsheets or internal systems to run rule-based financial models.
- Risk scoring. Staff use predefined formulas to calculate risk scores, often missing subtle patterns that require deeper analysis.
- Decision-making. Final approval involves manual checks and lengthy review meetings, which is why it takes much more time to make a final decision.
As you can see, such a process requires many different specialists to do tasks manually, resulting in a delay in decision-making.
AI-powered credit risk assessment
With AI, credit risk assessment becomes faster, smarter, and more reliable. Here’s how automation transforms the process:
Automated document analysis. Many AI systems used in investment baking platforms leverage the OCR technology that we described previously. For instance, AI-powered tools like ABBYY or DocuSign analyze documents instantly and identify inconsistencies or missing data.
Integration with external data sources. AI connects to external databases – credit bureaus, government records, or market reports – to cross-check applicant information. This eliminates the need for manual lookups and ensures accurate, up-to-date insights.
Real-time risk scoring. Machine learning models analyze multiple data points, including credit history, spending behavior, and market conditions, to calculate risk scores dynamically. These models can detect subtle patterns that traditional calculations often miss, enabling a deeper understanding of risk.
Predictive analytics. AI systems predict the likelihood of default by comparing borrower profiles with historical data and industry benchmarks.
Tools like FICO’s Falcon Platform use machine learning to improve credit risk models, allowing banks to make faster and more informed lending decisions.
Automated decision workflows. AI integrates with existing banking systems to automate workflows. For example, applications that meet certain criteria can receive instant approval, while higher-risk cases are flagged for manual review. This reduces delays and ensures smoother operations.
The difference between the two processes is noticeable. At the same time, it’s important to understand what automated systems can do for investment platforms and what they can’t.
What Can and Can’t Intelligent Automation Do for Investment Platforms?
As someone who has worked extensively with AI and data automation, I often get asked questions like, “Can AI predict the stock market?” or “Can it decide the best investment strategy for me?” The short answer is that AI is powerful, but it’s not magic. Let’s break this down.
What AI and automation CAN do
Analyze data at scale. AI can process and analyze vast amounts of financial data in seconds. These can be
- historical trends
- real-time market updates
- transactions
- sentiment from news or social media, etc.
This speed and accuracy allow investors to identify patterns and opportunities they might otherwise miss.
For example, platforms like Bloomberg Terminal use AI to provide real-time analytics and actionable insights.
Enhance decision-making. Intelligent automation can rank investment options based on risk, historical performance, market conditions, and other factors. Of course, it doesn’t decide for you, but it gives you a solid basis to make more informed decisions.
Robo-advisors like Betterment and Wealthfront are good examples, as they use algorithms to recommend diversified portfolios based on your goals and risk tolerance.
Automate routine tasks. Tasks like portfolio rebalancing, risk monitoring, and even regulatory compliance checks can be automated. And as a result, you save time and reduce human error.
For instance, AI-powered compliance systems flag unusual transactions in real-time to meet legal requirements without constant manual oversight.
Predict market trends (to some extent). AI works quite well with short-term predictions based on patterns in the data.
For example, tools like Kensho Technologies use ML to predict how events (like earnings reports or interest rate changes) might impact stock prices. However, these predictions are based on probabilities, not certainties.
What AI and automation CAN’T do
Guarantee stock market predictions. While AI can spot patterns, the stock market is influenced by countless unpredictable factors: geopolitical events, sudden market sentiment shifts, or even weather. No AI system can fully account for such randomness. As an ML engineer, I always remind clients that predictions are probabilities, not guarantees.
Replace human expertise. AI doesn’t always understand the “why” behind its recommendations. It analyzes data but lacks the intuition and judgment that seasoned investors bring to the table. Complex strategies often need a human touch to account for nuances and soft factors.
Make ethical or strategic choices. AI doesn’t consider personal values or long-term goals unless explicitly programmed to. For instance, if you prioritize ESG (Environmental, Social, and Governance) investments, you still need to ensure the AI is aligned with your ethical criteria.
Adapt perfectly to market shocks. During events like the COVID-19 pandemic, even the most advanced AI systems struggled to adapt quickly because such disruptions weren’t in their training data. This highlights the limits of relying solely on automation during crises.
So, can AI choose your investing method?
Not really. AI can analyze your risk tolerance, predict market scenarios, and even automate tasks like diversifying your portfolio. But the ultimate decision still rests with you. Intelligent automation isn’t a replacement for critical thinking – it’s a tool to make it better and more informed.
In my experience, the real value lies in combining human expertise with AI’s processing power. Automation handles the heavy lifting, while investors focus on strategy and decision-making.
AI/ML Automation in Investment Banking: 6 Encouraging Uses Cases
From investment research to robo advisors, the examples of using AI automation in investment banking are endless. Frankly, a single article won’t be enough to examine all of them. Below, I have rounded up 6 real-life use cases of intelligent automation within the investment sector.
Trade execution and portfolio management
AI has been widely used in trade execution and portfolio management to automate complex tasks, analyze large datasets, and execute trades based on predefined strategies. In addition to human error reduction, this approach mitigates emotional biases and enhances overall investment performance.
For example, AI systems can be explicitly programmed to:
- Buy stocks automatically when they meet specific conditions.
- Manage exit strategies using conditional orders, ensuring trades align with your goals.
- Execute stop-loss or take-profit levels based on real-time market conditions.
AI-driven systems adhere to preset rules and criteria and, in this way, help investors avoid impulsive decisions — especially during volatile market conditions.
Real-life example: Tradeweb Markets' AiEX Tool
Tradeweb Markets, a leader in electronic trading, developed the Automated Intelligent Execution (AiEX) tool for their trading activities. Initially designed for routine trades, AiEX now handles complex transactions across various asset classes.
Among its key features, there are:
- Trade execution automation with predefined parameters and real-time market data.
- Processing of large volumes of data to identify optimal trading opportunities.
- Minimization of market impact and execution costs through more optimized order management.
In Q3 2024, Tradeweb reported a 36% increase in earnings and a 37% rise in revenue, with $448.9 million in sales, underscoring the success of AI-powered tools like AiEX in optimizing trade execution and portfolio management.
Risk management and compliance
In terms of risk management and compliance, AI can be leveraged to analyze vast amounts of data to detect anomalies, identify risks, and ensure adherence to regulatory requirements.
It automates processes, such as
- monitoring transactions
- flagging suspicious activities
- generating detailed audit trails, among other things.
AI-driven compliance tools utilize advanced technologies like generative AI and large language models to detect and mitigate risks in real-time.
Real-life example: BNY Mellon and Behavox Quantum
BNY Mellon, a global financial services leader, partnered with Behavox to integrate its AI-powered compliance solution, Behavox Quantum, into their operations. Behavox Quantum uses advanced AI risk policies to transform compliance and risk detection processes.
Among its key features, there are:
- AI-powered surveillance to monitor and detect compliance breaches or high-risk behaviors within the organization.
- Large language models and generative AI analyze communications, transactions, and other internal data for signs of potential violations or risks.
- Generation of actionable insights to allow compliance teams to address issues before they escalate proactively.
Wealth management
AI in wealth management automates tasks like note-taking, action tracking, and communication, enhancing productivity and client engagement. It can also analyze client data to offer personalized investment recommendations.
Real-life example: Morgan Stanley’s AI @ Morgan Stanley Debrief
Morgan Stanley Wealth Management introduced AI @ Morgan Stanley Debrief, an OpenAI-powered tool that automates meeting notes, action items, and follow-ups.
Among its key features, there are:
- Generation of meeting summaries
- Drafting follow-up emails
- Updating Salesforce automatically
As a result, advisors save up to 30 minutes per meeting and can focus more on meaningful conversations. Clients receive detailed summaries, improving transparency and trust.
Part of a broader AI strategy, this tool exemplifies how AI enhances efficiency and elevates wealth management.
Fraud detection and prevention
Fraud detection with AI uses machine learning algorithms to analyze vast amounts of transaction data and identify unusual patterns or anomalies that may indicate fraudulent activities.
Traditional fraud detection systems often generate many false positives, which often overwhelm analysts and delay responses. As one of the major AI trends in banking, ML-powered fraud detection systems address this issue. They learn from historical data, refine their understanding of normal and suspicious behaviors, and improve accuracy over time.
Real-life example: JP Morgan Chase
JP Morgan Chase, one of the largest financial institutions in the world, implemented an AI-driven solution for fraud detection.
Here’s how it works:
- Integration of diverse data sources. The system aggregates and analyzes data from multiple sources, including transactional records, account behaviors, and external datasets.
- ML algorithms. Advanced models analyze the data to detect patterns and anomalies indicative of potential fraud. For instance, the system might flag a sudden large transaction from a location that is inconsistent with the customer’s typical activity.
As a result, the company saw a 50% reduction in false positives. This allowed analysts to focus on genuine threats instead of sifting through erroneous alerts. Also, there was a 30% increase in actual fraud detection rates, enabling the bank to prevent more financial crimes and reduce losses.
Additionally, JP Morgan Chase’s AI system monitors regulatory changes across 120,000 websites to keep compliance teams informed and reduce manual effort.
RPA in investment banking for document digitalization
Investment banks handle massive amounts of unstructured data, such as contracts, legal documents, and reports submitted in various formats (e.g., PDFs, images, and handwritten notes). Needless to say, manually processing and digitizing these documents is time-consuming and prone to errors.
That’s why robotic process automation in investment banking is widely used. Packed with OCR and NLP, cognitive RPA can read and interpret unstructured data. For example:
- It extracts key details, such as client names, terms, and monetary values, from scanned contracts.
- It converts this information into structured data, ready for integration into internal banking systems.
Real-life example: JPMorgan Chase’s COIN Platform
JPMorgan Chase implemented a cognitive RPA tool called COIN (Contract Intelligence). This platform processes thousands of commercial loan agreements in seconds, extracts data from it, and interprets information with near-perfect accuracy.
By automating the labor-intensive process of reviewing legal documents, COIN has significantly reduced errors and freed up employees for more strategic tasks.
Sentiment analysis in earnings calls
Sentiment analysis uses AI to analyze the tone, intent, and context of people's words. In investment banking, this is particularly useful during earnings calls, when company executives share updates on financial performance and answer analyst questions.
Beyond the words spoken, sentiment analysis can reveal underlying emotions, confidence levels, or evasiveness, providing deeper insights into a company’s future prospects. On top of that, sentiment analysis can lay a foundation for your AI personalization strategy.
Real-life example: Goldman Sachs Asset Management (GSAM)
Goldman Sachs Asset Management has integrated advanced AI-powered sentiment analysis into its investment process. Their system analyzes the words spoken during earnings calls and incorporates acoustic features to evaluate tone, emotion, and ambiguity in the speaker’s voice.
For example:
- AI models analyze text from transcripts to detect bullish (positive) or bearish (negative) sentiment in both scripted statements and the less-structured Q&A sessions, where management often reveals more nuanced insights.
- Using acoustic analysis, the system identifies subtle vocal cues, such as hesitation or emphasis, that may indicate a speaker's confidence or uncertainty.
This dual-text and audio approach enables Goldman Sachs to:
- Compare management sentiment against market expectations for security pricing.
- Capture discrepancies between what management says and how they say it.
- Identify potential risks or opportunities earlier than traditional analysis methods.
This use of AI sentiment analysis offers a competitive edge, allowing Goldman Sachs to make data-driven investment decisions based on deeper insights from company communications.
5 Main Challenges of Data Automation in Investment and How AI Solves Them
While modern investment banking operations have improved greatly, they are not without hurdles. Managing data at scale, ensuring accuracy, and integrating complex systems are just a few of the obstacles that banks face. But with intelligent automation, many of these challenges can be tackled head-on. Let’s break it down:
1. Data quality issue
Poor data quality – think of it as missing, inconsistent, corrupted, or inaccurate information – is a common issue in automation. In terms of investment banking, bad data can lead to faulty analyses and poor decision-making, which can be costly.
How AI solves it
AI-powered systems use machine learning algorithms to clean and validate data. For example, an automated ETL (Extract, Transform, Load) tool can identify inconsistencies, fill gaps, and flag errors, ensuring high-quality data for downstream operations. This means better analytics, fewer mistakes, and more confidence in decision-making.
2. Complex data integration
Investment banks rely on multiple data sources – client portfolios, market feeds, and legacy systems – each with its own format. Integrating all this information can be a logistical nightmare.
How AI solves it
Advanced integration tools powered by AI can automatically standardize and merge data from different sources. For instance, AI algorithms can map and transform disparate data formats into a unified structure (e.g., data formats DD/MM/YY or MM/DD/YY), making it easier to analyze. Tools like Snowflake and AWS provide scalable cloud-based solutions to simplify this process.
3. Scalability problem
It is critical to manage growing data volumes as the business expands. Traditional systems struggle to keep up with the sheer scale of modern investment banking operations.
How AI solves it
Cloud-based platforms equipped with AI can scale with ease. These systems adapt to increased workloads without requiring extensive manual intervention. For example, investment banks use predictive analytics models to forecast trends and prioritize resources. In this way, they can ensure smooth operations even during market surges.
4. Regulatory compliance
Compliance is a high-stakes game for investment banks. It is a constant challenge to keep up with changing regulations and ensure that data processes are audit-ready.
How AI solves it
AI systems designed for anti-money laundering (AML) or fraud detection can analyze transactions in real-time, flagging potential compliance violations. They also generate detailed audit trails for transparency.
For example, machine learning models can identify suspicious patterns, such as unusual transaction spikes, and alert compliance teams immediately.
5. Delivery of personalized client experiences
Clients expect tailored investment strategies, but manual customization is extremely difficult, not to say time-consuming.
How AI solves it
AI algorithms analyze client data – risk tolerance, financial goals, and market behavior – to create personalized portfolios and investment recommendations. Robo-advisors like Betterment use machine learning to offer curated investment options and give individualized client experience.
What Do You Need to Automate Your Investment Platform with AI and ML?
To sum things up, if you’re in the investment business and looking to test the waters of automation with AI, you need these 3 things:
- a solid foundation
- the right tools
- a clear strategy
As an ML engineer, here are my key pieces of advice to help you get started.
Invest in scalable infrastructure
Cloud platforms like AWS, Google Cloud, and Microsoft Azure are your best friends. They allow you to store, process, and scale data without worrying about the limitations of physical hardware. Data warehouses and lakes like Snowflake can centralize both structured and unstructured data, making it easier to analyze and automate workflows.
Use the right tools to build and manage your AI systems
Frameworks like PyTorch and TensorFlow are perfect for creating smart models that can handle tasks like fraud detection or portfolio optimization. MLOps tools like MLflow make it easier to deploy and monitor these models, keeping them accurate and reliable over time.
And don’t forget real-time streaming platforms like Apache Kafka – they enable your platform to process data continuously and deliver insights on the fly.
Focus on quality and adaptability
Last but not least, keep in mind that data automation isn’t a set-it-and-forget-it process. You need strong monitoring systems to track your models’ performance and regularly update them with fresh data to stay ahead of market changes.
At Uptech, we’ve helped businesses like yours take the leap into AI-driven automation in the financial sector. A few prominent examples are:
- Green investment app. A platform designed for green investors to contribute, manage their portfolios, and measure their environmental impact.
- TiredBanker. A solution that uses GPT-4 to turn years of complex earnings data from S&P 500 companies into easy-to-digest insights, guiding users toward better investment decisions.
These are just a couple of examples from our portfolio. The Uptech team builds tailored AI solutions that meet the unique needs of investment firms. Whether it’s automating trade execution, enhancing risk management, or creating more personalized client experiences, we’re here to help you take the next step toward smarter, AI-driven operations.
Because at the end of the day, AI isn’t just about automating processes – it’s about an ocean of potential for your business. Contact us to bring your idea to life.
FAQs
What are the benefits of data automation in investment banking?
Data automation enhances efficiency, reduces errors, and provides real-time insights. All of this works in favor of faster decisions and improved risk management in investment banking.
Will investment banking be automated?
While certain processes like data analysis, compliance, and trade execution can be automated, human expertise will remain crucial for strategic decision-making and relationship management.
Is it safe to use investing platforms with data automation?
Yes, investing platforms with data automation are safe when they follow strict data security protocols, including encryption, compliance with regulations, and real-time monitoring.