Financial Services AI Governance
Wealth Management AI on Databricks: Supervision, Lineage, and Advisor Review
Databricks gives a wealth firm one place to build AI on client and portfolio data. It does not decide who supervises the output. Kriv AI helps wealth managers set that governance before advisors rely on it.
Wealth management AI on Databricks is governable when the platform is the data and model layer and the firm builds supervision on top: named owners, advisor review of client-facing output, lineage from source data to output, and retained records. FINRA says its rules apply to AI like any other tool. Kriv AI designs this, starting at $200 per hour.
context
What Governing Wealth Management AI on Databricks Means
Wealth firms use a lakehouse to combine custodial, CRM, portfolio, and market data, then build models and assistants on it: meeting prep, client summaries, next-best-action suggestions, and draft communications. The governance question is who supervises those outputs once they reach an advisor or a client.
The platform is not the control
A shared data platform makes it easier to build quickly, and that is also the risk. A model that summarizes a household for an advisor can pull from many sources, and nobody may own the question of whether the summary is complete, current, or appropriate to show. Platform features such as access controls and lineage help you answer questions later, but they do not assign accountability.
A practical split: the platform records what data and which model version produced an output, and the firm decides who must review it, under what policy, and what happens when the review disagrees with the model.
What FINRA has said
FINRA Regulatory Notice 24-09, published June 27, 2024, says: "FINRA intends for its rules and guidance to be technologically neutral and to function dynamically with evolutions in technology and member firms' processes." It adds that the rules apply when firms use AI "just as they apply when member firms use any other technology or tool."
On supervision, the notice says "pursuant to Rule 3110 (Supervision), a member firm must have a reasonably designed supervisory system tailored to its business." It also says that "FINRA rules apply whether member firms are directly developing Gen AI tools for their proprietary use or when leveraging the technology of a third party, including through embedded features in existing third-party products." Whether a given firm is a FINRA member, and which other regulators apply, depends on its registration, so confirm that with compliance counsel.
ai gap
Where Wealth AI Governance Breaks Down
The common gap is that the build team and the supervision team never meet. Data engineers ship a client-summary assistant, and compliance learns about it when an advisor mentions it. The assistant may draw on notes, emails, or account data that were never classified for this use.
Other gaps recur. Nobody records which model or prompt version produced a given client-facing draft. Advisors edit drafts but the edits are not retained. Embedded AI features in vendor tools turn on without a review. And a change to a data source or a prompt reaches production without anyone asking whether it changes what clients see.
capabilities
What a Governed Wealth AI Design Includes
Use-case inventory and tiering
List every AI use on the platform and in vendor tools, then tier it by consequence. Internal summarization of public research sits differently from drafting a client communication or suggesting a recommendation.
Lineage from source data to output
For each tiered use, be able to show which datasets, which model, and which prompt or configuration produced an output. Use the platform's lineage and access-control features for this, and test that the record is actually complete for a sample of outputs.
Advisor review and retained records
Define what an advisor must review before client-facing content is used, what they can see about its basis, and what gets retained, including edits and rejections. Your compliance team determines which records fall under your retention obligations.
Monitoring, vendor oversight, and change control
Monitor for drift and for patterns in advisor overrides. Evaluate vendor and embedded AI features before they are enabled, and require review before model, prompt, or data-source changes go live.
framework
How FINRA Notice 24-09 and NIST AI RMF Fit Together
Notice 24-09 says that if a firm uses Gen AI tools as part of its supervisory system, "its policies and procedures should address technology governance, including model risk management, data privacy and integrity, reliability and accuracy of the AI model." It also says "a member firm should evaluate Gen AI tools prior to deploying them." Those are the points a wealth AI program on Databricks has to be able to show.
NIST describes its AI Risk Management Framework as "intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems." It is not a FINRA requirement, but it gives a neutral structure for the same work: govern accountability, map where AI touches client outputs, measure behavior, and manage the risk. Your regulators and your own policies determine what must be retained and reported.
differentiation
How This Differs From Our Related Pages
This page is about governing wealth management AI built on Databricks. Our Databricks AI for financial services page covers the platform broadly, our AI governance consulting for banks and financial services page covers the practice, and our model risk management pages cover validation for banks. Use this page when the question is how a wealth firm supervises AI outputs that advisors and clients see.
engagement
How an Engagement Works
We start by inventorying the AI uses on your Databricks workspace and in vendor tools, including the informal ones. We then tier them by consequence, define supervision and advisor review, map lineage and retained records, and align the design to your compliance team's obligations. Your team keeps the operating model; we do not supervise your advisors or manage client accounts.
tiers
What You Get at Each Tier
1. Enterprise / regulated organizations
An AI use-case inventory and tiering for your wealth workflows, a supervision and advisor-review design, a lineage and records design, and change-control criteria for model, prompt, and data-source updates.
2. Fractional CTO / AI governance lead
Ongoing oversight of the supervision program as use cases, vendors, and platform features change, including working with compliance, technology, and advisor leadership.
3. Specialized advisory
A single session or second opinion on a client-facing AI design, a lineage review, or a vendor's supervision claims.
rate card
Kriv AI's Rates for This Work
These are Kriv AI's own published rate floors, not an industry average.
| Track | Kriv hourly rate | Typical engagement model | Minimum engagement |
|---|---|---|---|
| Enterprise / regulated (banks, broker-dealers, payment processors) | From $200/hr | Fixed-scope project or retainer | $8,000 |
| Fractional CTO / AI governance lead | $300 to $400/hr | Part-time, ongoing (monthly) | $8,000 |
| Specialized advisory (vendor evaluation, second opinion) | $400 to $700/hr | Hourly, per-session | Varies by engagement |
| Small business | $150/hr | Referred to Kriv AI's partner network | n/a |
get a quote
How to Get a Real Quote
The rates above are floors, not a quote. Actual price depends on the number of AI use cases in scope, how many vendors and data sources are involved, how much documentation exists, and which regulators apply. Book a discovery call and we will scope it honestly.
Sources
Cited sources
Straight answers
Frequently asked questions about Wealth Management AI on Databricks: Supervision, Lineage, and Advisor Review
Does FINRA have separate rules for AI in wealth management?
Regulatory Notice 24-09 says FINRA intends its rules to be technologically neutral and that they apply to AI as they do to any other technology. Existing obligations such as supervision under Rule 3110 apply.
Does using Databricks make our AI compliant?
No. The platform can provide access controls and lineage that help you evidence decisions, but supervision, advisor review, and retention policy are firm decisions.
Do embedded AI features in vendor tools need review?
Notice 24-09 says FINRA rules apply when leveraging a third party's technology, including embedded features in existing third-party products, and that firms should evaluate Gen AI tools before deploying them.
Is the NIST AI RMF required?
NIST describes it as voluntary. Many firms use it as a reference structure alongside their regulatory obligations.
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