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
02How We Solve It
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.
Built for Financial Services
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 studiesHow Deployment Works
The C.O.R.E. Method - from kickoff to production inside the first 100 days.
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.
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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.
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.