AI agent for financial services

ai agent: what they are and why investment firms need them

An AI agent is an autonomous system that reasons, acts and interacts. It takes inputs, applies models, and executes tasks inside defined constraints. For investment firms an AI agent brings three clear benefits: faster research, automation of routine tasks, and scale. For example, an AI agent can summarise an earnings call transcript and flag key changes in guidance. In another example, an AI agent can run automated data pipelines that pull market data, normalise fields and store clean signals for models. These examples show how AI agents reduce manual work and free analysts for higher‑value thinking.

Research shows rapid adoption. About 75% of asset managers reported active AI use in a 2024 survey, underlining why many firms prioritise agent projects (Mercer 2024). Bloomberg has reported on “deep research agents” that run multi‑step analysis and produce draft research notes faster and more consistently (Bloomberg). Because these AI agents handle repetitive tasks, teams scale without hiring proportional headcount.

An AI agent also improves consistency. It applies the same data checks and templates to each report. The result is fewer errors and clearer audit trails. In practice, firms use AI agents to automate data ingestion and to draft client‑facing notes. That clamping of manual steps helps with regulatory reporting and with day‑to‑day operations. For teams that handle high email volumes, no‑code AI email agents like those from virtualworkforce.ai show how domain tuning and connectors cut handling time dramatically; see a related example on automated logistics email drafting for how connectors work in practice (automated email drafting example). In short, AI agents offer practical gains now. Next, we look at the evidence on adoption and ROI.

financial services and ai agents in financial services: adoption, evidence and ROI

Adoption of AI in financial services has moved from pilots to production. Surveys find a high share of firms using agentic tools and generative models. For instance, a ThoughtLab study reported that 68% of firms using AI agents saw measurable gains in portfolio performance and risk management (ThoughtLab 2025). That figure reflects both large asset managers and smaller teams that embed AI into workflows. Financial institutions are testing agents across research, compliance and client reporting.

Adoption differs by firm type. Asset management firms often focus on scale and alpha. Wealth management teams apply agents for client reporting and personalised advice. Startups and smaller teams use agents to accelerate research; Forbes has shown that firms with as few as ten people use agents to speed research creation (Forbes). Return on investment appears early in time savings and in higher‑quality signals. Research speed and accuracy drive direct ROI, and 60% of financial services executives credit generative AI with these benefits (Google Cloud research).

Smaller teams can access advanced AI without heavy development. Cloud vendors and specialist providers offer connectors, prebuilt models and managed platforms. This approach means a startup can use ai agents in financial services to synthesize research quickly. Also, firms can combine agents with human oversight to preserve judgement and control. Overall, the evidence supports a phased adoption model: experiment, show measurable gains, then scale. The pattern reduces risk and increases buy‑in across the organisation. For more on practical rollouts that reconnect agents to business processes, see a use case describing how to scale operations with AI agents (scale with AI agents).

ai agents for investment and use cases: how ai agents work in research and trading

AI agents for investment support many use cases. They automate research, generate trading signals, run surveillance, produce client reports and assist trade execution. For each use case the process follows a clear pattern: input → agent action → output. For research automation the input is financial documents and market data. The agent ingests PDFs, news feeds and market data, then it applies natural language processing and analytical models to produce a draft research note. The output is a structured report and a set of highlights that a human reviewer edits.

Signal generation works similarly. Inputs include price feeds and factor data. The agent applies machine learning models and then issues ranked ideas or alerts. The output is a signal stream that traders can ingest. Surveillance agents monitor trading patterns and compliance rules. They flag exceptions and produce audit evidence. Client reporting agents aggregate portfolio holdings and performance, and then they generate personalised investment summaries that advisers can review.

Multi‑agent systems increase robustness. Moody’s highlights that “multiple agent voting” can reduce bias by aggregating diverse models and views (Moody’s). In practice several specialised agents can evaluate the same opportunity and then vote or weight their recommendations. The result is improved recommendation reliability and clearer traceability. Bloomberg’s deep research agents show how chained agent steps produce longer, multi‑step research outputs automatically (Bloomberg).

A measurable benefit of these approaches is time saved. Teams report faster report turnaround and more consistent summaries. Firms also see fewer manual errors in data pipelines. Finally, agents can surface potential investment opportunities by analyzing market signals and company filings, giving analysts a richer starting point for judgement. These gains let human experts focus on interpretation and client conversation rather than repetitive data work.

portfolio and portfolio management: agentic approaches to allocation and risk

Agents now touch portfolio workflows from idea generation to monitoring and rebalancing. In portfolio processes an agent starts by scanning market data and research. It then suggests allocations or alerts about concentration risk. An agentic system acts with limited autonomy under human controls. For example an agent might propose a reallocation after a macro shock and include a rationale, scenario analysis and suggested trade sizes. A human portfolio manager reviews the proposal, tweaks sizing and approves execution. This handoff preserves human oversight while gaining speed and scale.

ThoughtLab’s research found that firms using AI agents reported measurable improvements in both portfolio performance and risk management (ThoughtLab 2025). McKinsey projects that AI improvements across distribution and investment processes could unlock significant value for asset management firms (McKinsey). Those gains come from faster decision cycles and from better risk control through continuous monitoring.

Controls are essential. Implement limits on position size, require human approval for material shifts, and keep robust backtesting for model changes. Maintain audit trails so regulators and internal reviewers can see why an agent suggested an action. For governance, use role‑based permissions and daily exception reports. A short scenario illustrates the flow: an agent detects rising credit spreads, runs a stress test, proposes trimming exposure by 2–3%, and then a portfolio manager approves the trade. This model blends speed and safety. Firms that adopt agentic approaches should document guardrails, maintain rigorous backtests and keep a human in the loop for material decisions.

ai platform and advisor: integrating agents across financial services

An AI platform must combine data, models, orchestration, audit trail and UI. That stack allows agents to act as digital advisors for clients and for advisers. Domain models such as BloombergGPT show the benefit of finance‑specific training and structured connectors to market data and financial documents (Bloomberg). Firms need connectors to market data, accounting systems and document stores so agents have reliable inputs. For example, virtualworkforce.ai demonstrates how deep data fusion and thread‑aware context reduce time spent on repetitive email workflows; the technical pattern is similar when integrating agents with ERPs and reporting systems (ERP email automation example).

As advisors, agents can personalise outputs and streamline client interactions. They can produce personalised investment reports and can adapt language to client preferences. Regulation will expect explainability and auditability. Provide clear provenance for each output, and keep logs for every decision path. Forbes has documented startups using agents to speed research and client engagement, which shows the accessibility of these platforms for smaller firms (Forbes).

Technology leads should follow a checklist: validate data quality, build connectors and APIs, select models or vendors, implement model governance and calibrate UI for advisers. Decide vendor vs in‑house based on domain needs and control requirements. For those evaluating ROI, consider time saved in report production, improved client satisfaction and reduced error rates. If your ops teams struggle with repetitive, data‑dependent emails, a no‑code AI advisor that integrates ERP and email history can be a practical first step; see a case that compares virtualworkforce.ai ROI approaches (ROI case). In short, a robust ai platform turns agents into reliable, auditable digital advisors across financial services.

ai agents work: governance, limitations and the next steps for firms

AI agents work best under strong governance. Firms must manage bias, overreliance and model drift. A Citi executive warned that moving from operational efficiency to investment‑centric AI needs rigorous governance to align outputs with human judgement and regulatory standards (Citi). Moody’s and other industry briefings recommend oversight that includes testing, monitoring and clear escalation paths (Moody’s). These measures keep systems reliable and defensible.

Start with a pragmatic rollout plan. Phase one: pilot agents on non‑critical workflows to measure accuracy and time savings. Phase two: expand to higher‑value processes with human‑in‑the‑loop controls. Phase three: scale and automate, while keeping strong audit trails. Track metrics such as accuracy, time saved, and alpha or cost reduction. Also track compliance metrics and incident rates. This roadmap makes it easier to show returns and to remediate issues quickly.

Limitations remain. Agents can inherit bias from training data, and they can drift as markets change. Firms must retrain models, update data connectors and perform continuous validation. Keep an audit of model versions and decisions so you can explain outputs to regulators and clients. Responsible AI practices include documented data lineage, redaction where needed, and user controls over agent behaviour. For teams that handle customer interactions, integrating thread memory and permissions reduces risk and improves client outcomes; see a related resource on improving logistics customer service with AI for techniques that apply equally to client emails in finance (improving customer service).

Takeaway: begin with controlled pilots, invest in data and governance, and measure impact. Then scale the parts that make a measurable difference. Firms that follow this path position themselves to leverage agentic AI safely and to realise the speed and precision that advanced AI can offer.

FAQ

What is an AI agent in finance?

An AI agent in finance is an autonomous system that reasons, acts and interacts with data and users. It ingests market data and financial documents, runs models and produces outputs such as research notes, alerts or trade signals, while operating under defined controls.

How widely are AI agents used in investment firms?

Adoption is broad and growing. Surveys report around 75% of asset managers use AI technologies and many are piloting or running AI agents in production (Mercer 2024). Usage varies by firm size and function.

What use cases suit AI agents best?

Use cases include research automation, signal generation, surveillance, client reporting and trade execution. Each use case follows the pattern input → agent action → output and often delivers measurable time savings.

Can AI agents improve portfolio management?

Yes. Agents assist idea generation, sizing, monitoring and automated rebalancing under human oversight. Studies show improved risk management and performance where agents feed consistent signals into decision-making (ThoughtLab 2025).

What governance is needed for agents?

Governance should include model validation, human‑in‑the‑loop approvals, audit trails and continuous monitoring. Regulators and internal compliance teams will expect explainability and versioned records of decisions.

How do platforms support AI agents?

An ai platform provides data connectors, models, orchestration and a UI with audit logs. Platforms trained on domain data, such as BloombergGPT examples, make agents practical for financial workflows (Bloomberg).

Are AI agents safe for client interaction?

With proper controls they can be. Agents must cite sources, record provenance and require human sign‑off for material client communications. Responsible AI practices reduce risk and improve trust.

How should firms start with agents?

Begin with pilots on non‑critical workflows, measure accuracy and time saved, then expand. Invest early in data quality and governance to scale successfully.

What limitations should firms expect?

Expect model bias, drift and occasional inaccuracies. Continuous testing, retraining and clear escalation paths will mitigate these issues. Keep humans in the loop for material decisions.

Where can I see practical examples?

Look at case studies and vendor materials that show connector patterns and ROI. For an example of connector-driven automation in practice, review virtualworkforce.ai’s ERP email automation and ROI case pages (ERP automation) and (ROI case).