Automated Suitability & Reg BI Review

AI agents review trade and account recommendations against suitability standards, Reg BI obligations, and firm policies - flagging risk before the trade.

Your current team stays - this is about the roles you haven't posted yet.

Target: 100%

structured review at sample-review headcount

Reg BI care-obligation documentation

Pattern detection across advisor books

Working system inside the first 100 days

What You Need to Know

What Is suitability review in Financial Services?

Suitability and Reg BI review automation is an AI system that evaluates trade, account, rollover, and product recommendations against client profile, regulatory standards (FINRA 2111, Reg BI), and firm policy - producing structured suitability assessments and care-obligation documentation that regulators expect. It scales supervisory review without scaling supervisory headcount.

Signs You Have This Problem

5 Ways Manual Processes Are Costing Your Financial Services Firm

Compliance supervisors review a thin sample of recommendations in depth - the rest get cursory review at best

Reg BI care-obligation documentation is a gap examiners routinely probe

Problematic patterns across advisor books surface only after customer complaints - too late for proactive supervision

Variable annuity, rollover, and complex-product reviews are inconsistent because they depend on which supervisor sees them

Documentation gaps in examinations create deficiency findings that take months to remediate

01The Problem

Suitability review is an obligation that doesn't scale linearly with headcount. Every recommendation requires evaluating client profile, product features, alternatives reasonably available, and the firm's own suitability standards, and the documentation has to demonstrate that the analysis happened, not just that the recommendation was approved. Reg BI raised the bar substantially: the care obligation now requires structured comparison against alternatives, and that documentation is a gap examiners routinely probe. The practical reality at most firms is uneven. Compliance supervisors review a sample of recommendations in depth and stamp the rest based on aggregate risk indicators. Variable annuity exchanges, rollover recommendations, and complex products get the attention they deserve. Routine recommendations get cursory review at best. Patterns across an advisor's book - the kind of patterns that drive enforcement actions when they eventually surface as customer complaints - are visible only when someone goes looking for them, which usually happens after a complaint has already been filed. Meanwhile, examiners want to see consistent application of the firm's stated policies, structured documentation of care-obligation analysis, and evidence of ongoing supervision. The gap between what firms can produce manually and what examiners increasingly expect is widening every year.

02How We Solve It

Revenue Institute's Suitability & Reg BI Review Agent evaluates every trade, account, rollover, and product recommendation against the client's documented profile, regulatory standards, and your firm's own policies. It produces a structured suitability assessment with citations to the underlying client data, product disclosures, and policy provisions - the documentation regulators expect to see. For Reg BI's care obligation, the agent maintains your current product universe, evaluates each recommendation against reasonably available alternatives at the moment of recommendation, and documents the comparison. Routine recommendations within the client's documented profile flow through with documentation. Recommendations outside the profile, involving complex products, or triggering elevated-review criteria escalate to a supervisor with full context attached. The agent surfaces patterns across advisor books that warrant supervisor attention - concentration, unusual frequency, age-inappropriate risk, conflict patterns. We build the connection to whatever custodial, trading, and CRM platforms your team already runs, scoped and reviewed with your compliance team before anything touches a live supervisory workflow. Compliance supervisors handle judgment cases and exception escalations; the agent handles volume and produces documentation your compliance team can defend in an examination.

The Business Case

Expected ROI for Financial Services Firms

The design target for suitability automation: structured review of every recommendation, at the supervisory headcount that used to cover a sample. Compliance supervisor capacity shifts from sampling to genuine judgment cases, exception handling, and pattern investigation across advisor books - the work that actually needs a human. The documentation gain shows up at examination time. Structured care-obligation memos and pattern-monitoring evidence are what examiners ask for and what sample-based review cannot produce on demand. Avoiding a single significant deficiency finding can pay for the system many times over. For a firm with 50-500 employees running an active advisory book, the payback assumption we scope against is 6-12 months from compliance productivity alone. The risk-avoidance value - a documented defense against enforcement actions, customer complaints, and arbitration - is the larger long-term return.

These figures are modeled expectations - based on how our deployments are architected, stated as assumptions rather than client results, not a published industry benchmark. We build the math on your numbers during the strategy call.

The default fix for this workflow is another hire - $85K-$120K a year loaded, 3-6 months to productivity, also stated as assumptions. A system runs the process work for a fraction of that, once. Your current team stays: your people do the judgment work, the system does the process work.

Why Financial Services Firms Choose Revenue Institute

MSPs sell uptime. Agencies sell deliverables. AI vendors sell hype. Consultants sell slides. We build the technology your business runs on, then we run it. Every engagement starts with your specific workflows, compliance requirements, and business objectives. No generic templates. No off-the-shelf tools forced into your process.

Native Stack Integration

Connects directly with Salesforce, HubSpot, NetSuite, and the tools your financial services team already uses.

Compliance-by-Design

Every system is architected around your regulatory requirements - audit trails, access controls, and data residency included. It runs inside your existing platforms and permissions.

Live Inside the First 100 Days

Deployment follows The C.O.R.E. Method - your highest-ROI workflow ships first, and you see it running before the engagement ends.

Straight answer on proof

We don't have a published financial services firm case study yet, and we won't borrow one from another industry to look like we do. The named engagements on our case studies page show the same system architecture in production - and on a call we'll walk through exactly what we'd build for your firm.

See the named case studies

How Deployment Works

The C.O.R.E. Method - from kickoff to production inside the first 100 days.

Capture - Process Audit & Integration Mapping
Orchestrate - Agent Design & Build
Run - Pilot on Real Data, Then Go-Live
Expand - New Workflows on the Same Foundation

Frequently Asked Questions

What does the agent review?

Trade recommendations, account-type recommendations, rollover recommendations, and product recommendations against the client's risk tolerance, investment objectives, time horizon, financial situation, and any other factors required by FINRA Rule 2111, Reg BI, and your firm's own suitability policy. It produces a structured suitability assessment with citations to the underlying client data and product disclosures.

How does it handle Reg BI's care obligation specifically?

Reg BI requires reasonable diligence, care, and skill in evaluating and comparing reasonably available alternatives. The agent maintains a current product universe, evaluates the recommendation against alternatives at the moment of recommendation, and documents the comparison - which is exactly the documentation firms struggle to produce in examinations. The output is a structured care-obligation memo, not a generic suitability checkbox.

Can it identify problematic patterns across an advisor's book?

Yes. The agent surfaces patterns that supervisors should investigate - concentration in proprietary products, unusual rollover frequency, age-inappropriate risk profiles, or recommendations that consistently produce the highest commission for the advisor. These are the patterns that drive regulatory enforcement; surfacing them proactively is dramatically less painful than discovering them in a customer complaint.

Does it integrate with our trading platform and CRM?

We build the integration to whatever custodial, trading, or CRM platform your team already runs, and confirm exact connector scope - reviewed against your compliance team's requirements - on the strategy call. The system operates inside your existing supervisory workflow rather than asking firms to migrate.

How does it handle high-volume routine recommendations?

Risk-based prioritization. Routine recommendations within the client's documented profile - rebalancing within risk tolerance, standard product categories, no concentration concerns - flow through with documentation. Recommendations that fall outside the profile, involve complex products, or trigger any of the firm's elevated-review criteria escalate to a supervisor with the full context attached. Supervisors handle judgment cases; the agent handles volume.

What about variable annuities and complex product recommendations?

Complex products receive elevated review by default. The agent documents the suitability analysis specific to product features - surrender charges, riders, fee structure, tax treatment, and compares against alternatives the client could reasonably access. This is one of the highest-value applications because complex-product reviews are where documentation gaps tend to run deepest.

How long does deployment take?

You have a working system inside the first 100 days. Weeks 1-3 cover platform integration and policy ingestion. Weeks 4-10 train the agent on your historical supervisory decisions and validate against known cases. Weeks 11-14 deploy, starting with one product category, typically equity and mutual fund recommendations, then expanding to complex products and account types.

Ready to deploy AI for your financial services firm?

Stop staffing this workflow. Start owning the system that runs it - your people do the judgment work, the system does the process work.

In a 30-minute call, our AI architects will identify your top 3 automation opportunities and give you a concrete deployment timeline - no slides, no pitch deck.

30-minute call, no commitment
First system live inside the first 100 days
Runs inside your existing systems and permissions

Straight talk: we're not the right fit if you're under $10M in revenue - the math above won't pencil out yet. We'd rather tell you now than take the deposit.