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Large Language Models (LLMs) for Investment
ScienceSoft applies 35 years of experience in AI and 19 years in investment software engineering to deliver robust LLM solutions tailored to the unique operations of investment firms.
Why the Investment Industry Is Adopting LLMs
Large language models (LLMs) for investment are used to automatically extract actionable insights from structured and unstructured data in natural language format, including portfolio transactions, investor interaction records, capital market media publications, and regulatory guidelines.
LLMs can consolidate multi-source data, retrieve case-relevant information, and produce intelligent suggestions in seconds, contributing to quick and informed investment decisions. By using LLMs to automate data processing, investment firms can reduce back-office costs by up to 40%. LLMs’ ability to recognize semantic and contextual nuances of human language with 95%+ precision lets investment pros quickly spot emerging yield opportunities and timely hedge against risks.
Another advantage of LLMs is that they can have conversational interfaces, allowing users to chat with the model and ask questions using regular human language. Investment firms often deploy LLMs in the form of virtual copilots for employees or user support chatbots for their clients. The latter brings, on average, a 7% increase in customer satisfaction (CSAT) and up to a 2x decrease in the servicing team’s workload.
Large Language Models in Investments: Market Overview
The market of generative artificial intelligence (AI) in financial services was valued at $1.09 billion in 2023. It is expected to reach $12.14 billion by 2033, growing at a CAGR of 28.1%. The segment of LLM-powered natural language processing is anticipated to witness the corresponding increase and retain the GenAI market’s biggest share throughout the next decade.
The investment industry has relied on AI for years to automate areas like stock analytics and high-frequency trading. Emerging LLM solutions are gaining popularity among investment firms due to their ability to efficiently handle data-intensive tasks across market research, sentiment analysis, portfolio management, and risk mitigation. According to a 2024 survey by ThoughtLab, 58% of investment banks and wealth management firms project GenAI and LLMs to be their biggest tech investment over the next three years.
Wealth management companies are increasingly adopting LLM-based chatbots to automate front-line operations, seeing LLMs as a pivotal solution to the imbalance between service demand (61.9M clients) and capacity (15K investment advisors). As 80% of individual investors are willing to receive AI advice, leverage smart search, and use AI for investment planning, many incumbents and fintech players are launching client-side LLM assistants to boost customer satisfaction.
How LLMs for Investment Work
Main use cases for LLMs in investment
Capital market research
LLMs can instantly capture and summarize massive volumes of web data on capital market dynamics, investor behaviors, and insider opinions. This lets investment pros easily access up-to-date information, quickly spot high-yield opportunities, and avoid risky deals.
Investment planning
LLMs streamline investment planning by suggesting the optimal portfolio structures, investment strategies, and trade order timings. Augmented data sets produced by LLMs can inform fundamental and technical analysis, contributing to more profitable investment decisions.
Client data management
With an LLM tool, investment firms can auto-extract data from client documents for streamlined AML/CFT and OFAC verification. LLMs can generate analytical summaries, memos, and investment performance snapshots to expedite managers’ communication with customers.
Investment reporting
LLMs can consolidate customer, portfolio, and business data for faster investment disclosure reporting. LLM-powered engines can review the produced reports against internal and regulatory standards to ensure report accuracy and compliance and minimize reviewer efforts.
Fraud and compliance controls
By applying LLMs for automated detection of fraud, money laundering, and employee non-compliance, investment firms can timely address financial risks. LLMs can also monitor and report sectoral legislative changes to help companies promptly adhere to changing regulatory boundaries.
Customer service and support
LLM-based chatbots incorporated in investor apps let users easily navigate investment products and self-service features and solve operational issues 24/7 without involving service specialists. This helps enhance CSAT and free the servicing team from low-value manual routines.
Avoiding million-dollar costs of custom investment LLMs
To bring LLM benefits to your investment workflows, you don’t need to build your own GPT from scratch. Market-available LLMs are smart enough to reason on general topics, and they can become a stable foundation for your LLM solution. It is easier and cheaper to “educate” an out-of-the-box LLM about investment specifics than to build and train a completely new model. In most cases, applying cost-effective techniques like prompt engineering and retrieval augmented generation (RAG) is enough for model enhancement. If deeper customization is needed, we selectively update or modify specific LLM settings using parameter-efficient fine-tuning (PEFT).
LLM solution architecture
ScienceSoft's solution architects recommend RAG as the optimal LLM enhancement technique for most cases (market research, portfolio rebalancing, investor reporting, etc.). RAG-enabled investment LLM solutions ensure smooth access to the most recent proprietary data needed to automate investment tasks. Here’s an example of how such solution may look architecturally:
- A user submits a textual or voice inquiry (prompt) about a particular investment aspect via the role-specific LLM app (for investors, financial advisors, portfolio managers, etc.).
- The prompt processing query is instantly routed to the app’s server-side orchestrator. The orchestrator hosts LLMOps (i.e., the operations enabling continuous communication between the LLM solution’s components), automatically enriches prompts with contextual investment data, and converts prompts to the LLM-ready format.
- The orchestrator queries the investment company’s data storage to obtain prompt-relevant structured data like order dates, stock indices, and trade volumes. Unstructured data and metadata, such as time series graphs, investor documents, and compliance policies, is harder to search semantically, so it needs to be prepared first. ScienceSoft recommends using a data vectorization pipeline that automatically cleanses investment metadata, breaks it into manageable chunks for efficient processing, converts it to a searchable vector format, and stores it in a vector database. RAG comes into play when unstructured contextual data is needed. The orchestrator queries the RAG embedding model to perform a vector search and retrieve the data pieces (embeddings) that are semantically similar to the prompt.
- The results of the hybrid search (lexicographic search across structured data and semantic search across vectors) are transferred to the reranking model. The model merges and analyzes the outputs, produces the optimal single set of results, and sends it to the orchestrator.
- The orchestrator adds the prompt and contextual data into a pre-engineered prompt template. ScienceSoft designs custom templates for client-specific topical queries, considering the prompt length limits defined by LLM providers. The enhanced prompt is then routed to the chosen LLM.
- The pretrained LLM (GPT-4, LlaMA, Claude, or any other tool of our client’s choice) processes the prompt and returns the response to the orchestrator in real time. The orchestrator runs inference validation and logging and routes accurate responses to the user.
- The user provides feedback on the response's relevance and accuracy. The feedback is used for further LLM fine-tuning, e.g., using reinforcement learning with human feedback.
Key capabilities of LLMs for investments
Natural language conversation
Investment professionals interact with LLMs using textual prompts in natural language. LLMs instantly process user inquiries and communicate the requested information in a manner closely resembling human dialog. Multi-modal LLMs featuring speech synthesis can support voice interaction.
Investment data retrieval
LLMs can scan large volumes of unstructured data like investor documents, technical time series (prices, volumes, trades, etc.), capital market news, and social media posts and extract insights relevant to the user’s query. The models auto-summarize the collected information and present it in a user-defined report format.
Sentiment analysis
LLMs capture investment sentiment from public media, identify explicit (hard facts, spoken investor attitudes) and latent (implied by author persona, tone, post timing) market signals, and report them to portfolio managers. The sentiment can be auto-segmented by asset class (e.g., stocks, real estate, crypto), type (opportunity or risk), longevity (short- or long-term positions), and more.
Portfolio construction
The obtained capital market insights and contextual data about investor capacity and risk appetites are used by LLMs to predict the behavior of particular assets and suggest portfolio structures best aligned with the investor’s goals. LLM recommendations can be auto-converted to mid- and long-term investment plan drafts that will curate further portfolio management.
Advice on portfolio optimization
Upon request, LLMs can revise portfolio structures and performance against the intended returns, available benchmarks, opportunities, and exposure factors. They highlight yield variances, emerging investment options, and risks. LLMs can also advise on portfolio rebalancing and hedging and share the rationale behind each consideration.
Synthetic investment data creation
You can use LLMs to generate realistic financial market datasets. For example, LLMs can simulate stock price trajectories and controversial sentiment observable in the market. The data can be used to train and calibrate the machine learning (ML) algorithms behind trading bots on risk aversion, diversification, and tactical concepts.
Data summarization for investment documents
Wealth managers and reporting specialists can ask LLMs to compile business data necessary for planned documents (e.g., investor account statements, meeting notes, security research reports, trade reports, income statements) and put it into an easily digestible format. LLMs can also auto-populate pre-built document templates with the relevant data.
Document review and compliance checks
LLMs can auto-match the produced documents against the data from corporate sources (e.g., a portfolio management system, an accounting system). The models can point out factual errors, tone inconsistencies, incomplete disclosure, and format issues (e.g., for Form 10-K and 10-Q) and suggest the appropriate fixes. LLMs can also copy compliance-sensitive document parts and submit them for manual review to the responsible employees.
Investor self-service
When launched on an investor portal or an investment platform, LLM assistants can give real-time answers to general user queries and specific questions about portfolio management and trading operations. LLM bots can guide users through investment options, curate portfolio design, provide trade advice, and offer 24/7 support.
Investment fraud detection
LLMs recognize patterns in investor documents, communication, and transactions and instantly spot deviations that may indicate fraud attempts. Suspicious activities are auto-classified by type (e.g., document forgery, compliance violation) and supposed intent (money laundering, identity theft, etc.) and reported to the fraud investigation team.
How Investment Service Leaders Succeed With LLM Solutions
JPMorgan's IndexGPT Lets Institutional Investors Easily Spot Early Trends
JPMorgan Chase, the United States’ largest bank with $3.5 trillion in assets, launched an LLM-supported investment advisory tool called IndexGPT. The tool relies on OpenAI’s GPT-4 model to generate search keywords relevant to emerging investment options and utilizes a separate NPL model to scan public media and pinpoint companies actively mentioned in the context of those keywords.
Currently targeted toward JPMorgan’s institutional clients with short-to-medium investment horizons, IndexGPT helps investors quickly identify hot, high-return market trends and capitalize on them before they become mainstream knowledge.
AI @ Morgan Stanley Assistant Speeds Up Advisory and Client Support
Morgan Stanley, a US-based multinational investment bank and financial services company with 90 years in business, employed OpenAI’s GPT-4 model to create its own LLM-based investment assistant called AI @ Morgan Stanley Assistant.
The tool gives the institution’s 16,000 financial advisors and client support specialists speedy access to the bank’s database of 100,000+ financial market reports and investment documents. Morgan Stanley’s teams can now address customer questions about markets, asset allocation, and internal processes quicker, allocating the freed-up time to higher-value engagements.
ScienceSoft’s Tech Stack for Investment LLM Solutions
Ways to Mitigate Risks Inherent to LLMs for Investments
Risk #1. Inaccurate LLM responses
If a pretrained LLM lacks knowledge of certain investment concepts or company-specific data, it may produce incorrect or fictitious responses known as hallucinations. The overlooked errors propagate to investment decisions, compromising their accuracy and profitability.
Mitigation steps
Risk #2. Opacity of LLM-produced advice
Investment advisors are legally obliged to act in their clients’ interests. Relying on an LLM solution with opaque logic would complicate the proof of advisor adherence to the fiduciary duty and SEC requirements.
Mitigation steps
Risk #3. Insufficient data security
Reliance on third parties to host the LLM solution’s infrastructure poses risks of unauthorized access to sensitive investment data and unintentional public data exposure. This may result in reputational damage, the loss of competitive edge, and costly compliance breaches.
Mitigation steps
Costs of Implementing an Investment LLM Solution
Based on ScienceSoft’s experience, implementing an LLM solution for investments may cost from $250,000 to $1,000,000+, depending on solution complexity, the chosen approach to LLM enhancement, architectural and tech stack decisions, as well as company-specific security and compliance requirements.
Here are our sample estimates for various scenarios:
$250,000–$350,000
An LLM chatbot that handles investment client communication. RAG is applied to “upskill” pretrained LLMs on specialized investment knowledge and company data.
$300,000–$500,000+
An LLM copilot for investment specialists (advisors, brokers, portfolio managers, etc.). The underlying LLMs are adapted to the investment firm’s specifics using RAG and, if needed, PEFT.
$1,000,000+
An LLM-powered assistant for investment and wealth management professionals, retrained and fine-tuned to reason on highly specific service aspects or new investment models (e.g., crypto investing).
Investment LLM Consulting and Implementation by ScienceSoft
An AI software development company with decades of experience in investment IT, ScienceSoft provides full-cycle LLM services to help investment companies and wealthtech startups implement tailored LLM solutions.
Investment LLM consulting
With your specific requirements in mind, we pick the most feasible approach to LLM implementation and design the optimal features, architecture, and tech stack for your LLM solution. You also get advice on steps to achieve legal compliance and a detailed project plan with cost and time estimates for a predictable LLM solution launch.
Investment LLM implementation
We choose the optimal LLMs and enhancement techniques to accommodate your business-specific data, engineer the LLM solution, and connect it to your internal systems. Our team performs all necessary QA procedures and can provide continuous LLM app maintenance. You get an MVP of your investment LLM solution in 1–4 months.
Our BFSI Clients About ScienceSoft’s Practices
Our collaboration was a true partnership. The team was open, attentive to our requirements, and accurate in addressing them. The delivered solution is exactly what we needed.
What always set ScienceSoft apart is their resistance to mediocre results and their proactive advice on meaningful improvements.
ScienceSoft provided high-quality service and valuable tech insights that aligned well with our vision and specific needs.
We especially appreciate ScienceSoft’s professional approach to security issues, which were among our main concerns due to strict regulations.
We are impressed with ScienceSoft’s pragmatic project management, quality-first mindset, and transparent communication. They are strongly motivated to deliver maximum value with their services.
What makes ScienceSoft different
We deliver high-quality financial solutions no matter what
ScienceSoft delivers financial IT solutions that outperform competitors in logic accuracy, no matter the challenges posed by evolving customer expectations, changing regulations, or legacy system constraints.