AI in finance: A complete guide for 2026

Why AI matters for financial advisors

Client expectations have changed faster than most advisory tech has. People want answers quickly and to feel like their advisor really knows them. But that kind of service is hard to deliver at scale. The pressure shows up in a few specific places:

• Competitive pressure: Clients increasingly expect the kind of proactive, personalized service that used to be reserved for ultra-high-net-worth relationships. And the window to get ahead of this is narrowing as AI is quickly moving from differentiator to baseline across much of the financial services industry. (Some estimates suggest that 90% of finance teams globally are expected to use at least one AI-enabled tool by the end of this year.)
• Operational demands: The administrative layer of running a practice is substantial—compliance monitoring, performance reporting, meeting documentation—and it grows alongside your book.
• Scalability: There’s a ceiling to how many clients one advisor can serve well without sacrificing quality, which means you traditionally couldn’t serve more clients without adding headcount.

AI helps by automating the operational layer, helping you streamline and deliver more personalized service at scale, and raising the ceiling on how many clients you can serve well without a proportional increase in overhead. 

How AI is used in finance today

AI shows up differently depending on what problem you’re solving. Three core technologies are worth understanding, as knowing what each is built to do makes it easier to spot where they’re most useful.

Machine learning and predictive analytics

Machine learning (ML) is what allows AI to get better at predictions over time without being manually updated. It works by finding patterns in historical data—and in finance, those patterns do a lot of useful work: risk scoring, identifying clients who might be disengaging, triggering rebalancing reviews, forecasting market trends. The more data these models see, the sharper their predictions get. (Whether that’s your firm’s data or broader financial datasets depends on the platform and your data permissions.)

Natural language processing and sentiment analysis

Natural language processing (NLP) is how AI reads and makes sense of text, including earnings calls, regulatory filings, news articles, and client emails. Instead of a human analyst spending days combing through documents, NLP can extract the relevant figures, flag what changed, and surface patterns across thousands of sources at once.

Sentiment analysis is a specific application of this—e.g., reading the emotional tone of news articles to gauge how markets are reacting, or scanning client emails and meeting transcripts to spot referral opportunities or satisfaction signals before your next touchpoint.

Generative AI and large language models

Unlike ML, which analyzes existing data, generative AI (GenAI) produces new content from it. In financial services, that means drafting client communications, summarizing complex documents like estate plans or tax returns, generating scenario models, and producing first drafts of compliance documentation. It’s the newest of the three AI technologies and is still maturing, but it can cut meaningful time out of day-to-day knowledge work.

AI applications for your practice

The technology is only as useful as what it actually does inside a firm. Here’s where advisors are seeing the most meaningful impact.

Portfolio management and investment optimization

Portfolio tools now use AI to monitor drift, flag rebalancing triggers, and stress-test allocations under different scenarios. Many can also generate proposed portfolios based on a client’s goals and risk profile. These are the same mechanics that powered early robo-advisors, but they’re now showing up in software designed for human advisors.

Document processing and workflow automation

A large share of operational work still revolves around paperwork. AI can pull information from incoming documents (e.g., account transfer forms, loan applications, regulatory filings), validate the data, and move it to the right systems. Tasks like reconciliation or onboarding paperwork that used to require manual entry can instead move through a lot of the process automatically.

Client service and conversational AI

Some firms are starting to use AI assistants to handle routine client questions—checking balances, pulling transaction history, or retrieving documents. When something requires an advisor’s input, the system logs the interaction and routes it to the appropriate person, maintaining the right level of oversight.

Fraud detection and cybersecurity

AI monitors transactions, login patterns, and device and location signals continuously. When something looks unusual, AI systems can flag it immediately and trigger verification steps or temporary blocks before fraudulent activity has a chance to progress or spread.

This is also the most established AI use case in financial services: Roughly 60% of financial institutions use AI for fraud detection, and in the U.S., that number climbs to about 91% of banks that are using it.

Risk assessment and credit scoring

AI evaluates creditworthiness using traditional data—credit history, income, collateral—alongside non-traditional signals like utility payments and cash flow patterns. The result is a more complete picture of an applicant’s financial health than standard scoring models would typically capture.

Algorithmic trading

Many market participants now use AI-driven systems to analyze real-time data and execute trades based on predefined strategies.

Around 70% of all global trading volume is actually now executed by these algorithms. Independent advisors usually aren’t running these models themselves, but their clients’ portfolios are operating in markets where algorithmic activity plays a significant role.

Regulatory compliance and anti-money laundering

AI-driven RegTech tools handle much of the ongoing monitoring—scanning transactions and client records against regulatory rules and watchlists, then surfacing anything that needs review. They also track regulatory changes as they happen, rather than catching up after the fact.

Benefits of AI
in finance

Where AI proves itself is in the day-to-day economics of running a firm:

• Operational efficiency and time savings: Preparation, documentation, and follow-through work—drafting agendas and recaps, updating CRM records, extracting tasks from conversations—gets handled automatically, freeing up more of an advisor’s week for actual client work.
• Enhanced client experience and personalization: Advisors arrive at every interaction with a clear view of recent activity, open issues, and past commitments—enough context to tailor recommendations and follow-ups to a client’s real circumstances rather than working from memory or partial notes. The economics back it up: BCG research finds that firms using AI to personalize client interactions have seen 10–15% revenue increases and up to 30% reductions in churn.
• Improved risk management: Continuous monitoring surfaces early signals—portfolio drift, concentration risk, unusual account activity, disengaging clients—and supports more consistent documentation, reducing the quiet gaps that tend to become compliance or suitability problems later.
• Cost reduction and scalability: As the practice grows, more of the added administrative work—notes, forms, updates, record-keeping—gets handled automatically. Firms can absorb higher client volumes without proportionally increasing headcount.
• Data-driven decision making: Consolidating portfolio data, tax-lot details, cash flow needs, and client communication into a single view means advisors spend less time assembling information and more time evaluating the tradeoffs that actually require their judgment.
• Tax planning: AI can ingest tax returns, account statements, and client financial data to surface optimization opportunities (e.g., Roth conversions, timing moves, deductions) and model scenarios in real time. For example, Hazel’s AI tax planning tool can turn a client’s tax return into a personalized strategy—with scenario modeling and client-ready deliverables—in minutes. This helps save advisors approximately $5,900 per client in taxes and 5 hours per week, on average.

Challenges of AI in finance 


The benefits of AI in finance are meaningful—but they’re also not without tradeoffs. Understanding where AI adoption gets complicated is part of making a good decision about where and how to start.

• Data privacy and security: AI tools need data to work—and in an advisory context, that means account numbers, tax IDs, client statements, and communications. The practical concerns are specific: Data leaving the firm’s environment to third-party vendors, staff pasting sensitive information into unmanaged tools, and limited visibility into how long data is retained or who can access it. Any platform you evaluate should have clear answers on things like role-based access controls, encryption, audit trails, and clear limits on what data the tool can access.
• Algorithmic bias: AI models learn from historical data—and historical data often reflects historical inequities. That can look like lending decisions that disadvantage certain zip codes, risk scores that penalize gaps in work history, or service models that route some segments to slower responses. A related challenge is transparency: When a model is complex or vendor-hosted, it can be genuinely difficult for an advisor or compliance officer to answer “why did the model recommend this?”—which is a real problem in a fiduciary context.
• Legacy system integration: Most firms are already operating across a mix of custodial platforms, portfolio accounting systems, CRM tools, and document management—plus whatever spreadsheets and macros have accumulated over the years. The common pain points are duplicated data, delays between when data updates and when you can act on it, and APIs that are limited or outdated. Integration work also tends to take longer and cost more than the AI pilot itself, which is worth factoring in before you’re already committed.
• Implementation costs and expertise gaps: The cost of adopting AI goes beyond the vendor contract. It includes data preparation, integration, and ongoing monitoring. Some firms are showing early ROI, but it’s not universal yet: 1 in 5 finance teams currently report returns above 20% on their AI initiatives. Most firms can experiment with low-code tools, like Hazel, which is built to handle the integration and workflow complexity, so firms don’t have to build that capability themselves.

AI regulations and compliance in financial services

The regulatory framework around AI in financial services is still catching up to the technology. Current SEC fiduciary guidance makes one thing clear: fiduciary responsibility doesn’t transfer to an algorithm. If an AI tool influences a client recommendation that causes harm, the advisor is still accountable for that outcome—meaning that human oversight remains a compliance requirement.

Beyond fiduciary duty, federal and state privacy regulations govern how client information flows through AI systems, and those rules continue to evolve. Understanding what your platform does with client data—and documenting it thoroughly—is increasingly a baseline expectation for running a compliant practice.

And because there isn’t a fully settled regulatory framework at the moment, advisors adopting AI today should be especially careful when choosing the platforms and tools to incorporate into their practice. Understanding third-party policies will be a meaningful risk management decision.

The future of AI in finance

The current state of AI in financial services is genuinely useful—but the more significant changes, shaping the future of finance, are still ahead.

Agentic AI for autonomous financial workflows

The next frontier is AI solutions that can orchestrate entire processes from start to finish—things like tax planning, compliance documentation, report generation—with humans stepping in to review and approve rather than manage every step. These systems are early but moving fast, and they’re likely to reshape how firms operate over the next few years.

Finance leaders are already anticipating the shift, with 75% expecting agentic AI to be a routine part of their operations by 2028, handling complex workflows with humans stepping in to review rather than manage every step.

Hazel’s tax planning tool, for example, is already doing this for advisors, using AI to ingest client data, surface opportunities, and produce client-ready strategies with the advisor in the loop at the right moments.

Hyper-personalization and embedded finance

As AI gets better at modeling individual client behavior and preferences, the quality and timeliness of personalization improve with it. The direction this is heading is financial guidance woven into the platforms clients already use daily—banking apps, payroll portals, commerce platforms—proactive, context-aware, and available well before the quarterly review. For advisors, that raises the bar on what clients will expect from every interaction, even if they’re not the ones building those experiences directly.

AI for financial inclusion

One underappreciated implication of AI in advisory is what it does to the economics of who you can serve. When routine work is automated, the cost of serving smaller accounts drops, making it more viable to extend quality advice to emerging or mass-affluent clients who didn’t previously fit the traditional model.

And the impact extends to lending, as well. For example, credit unions using AI-driven credit scoring have seen a 40% increase in loan approvals for women and people of color, without an increase in default risk.

How financial advisors can start using AI

If you’re ready to put AI to work in your firm, here are some practical steps to get started:

1. Evaluate your current technology and tools: Look for where data has to be entered manually or where information gets lost between systems. Those friction points usually signal where AI can add the most immediate value.
2. Identify your highest-impact use cases: Pick one obvious or persistent bottleneck in your practice—meeting documentation, client onboarding, compliance reporting—and start there. Early, visible wins make it easier to build support for broader adoption.
3. Choose a platform with AI built in: Standalone AI tools often require extra integration work that can offset their benefits. Platforms like Altruist build AI-powered workflows directly into the custodial experience, so the capability is already there when you need it.
4. Train your team on the tools—and their limits: One of the biggest risks with AI is trusting it blindly. Make sure your team knows how to use the features effectively and where outputs need human review before anything reaches a client.
5. Track what actually changes: Before you roll out anything new, set a baseline for key metrics—time spent on specific tasks, onboarding timelines, client response times. Compare before and after so you know which uses of AI are worth expanding.

Building a more efficient advisory practice with AI

The advisors who will get the most out of AI are the ones who are clear on what they’re trying to build and strategic about where technology can help them get there faster. AI works best as an extension of a good advisory practice, where the client relationship, the trust, and the judgment remain yours.

What AI can do is take the operational weight off your plate, so more of your time goes toward the work that actually matters. 

If you’re thinking about what that looks like in practice, talk to the Altruist team about how an AI-integrated platform can fit into how you run your practice today.