AI Automation
for Fintech,
San Francisco.
KYC, AML, reconciliation, and regulatory reporting. Built for the compliance requirements of San Francisco.
The workflows that move the needle.
KYC/AML onboarding and ongoing monitoring
Transaction reconciliation and exception handling
Regulatory reporting and audit trail generation
Built to spec.
Every automation we ship in San Francisco is engineered around the compliance frameworks that govern fintech data in United States.
SEC Rule 17a-4, FINRA Rules 3110 and 3120, BSA/AML requirements, and SOX Section 302/404 for publicly listed companies govern all financial AI automation.
We run a data protection impact assessment on every project, document the legal basis for all automated processing, and build human-in-the-loop controls wherever a decision carries legal or material effect. You receive full audit logs and runbook documentation at handover.
Everything the automation says to a customer is both a regulated communication and a preserved record.
In most sectors an automated message is just a message. In financial services it is two regulated artefacts at once. Under the FCA's Consumer Duty, set out in PS22/9, firms must deliver good outcomes for retail customers, which includes communications customers can understand and the support they need when they need it. An automated response that is technically accurate but incomprehensible is a Consumer Duty problem, not a copywriting one.
At the same time it is a record. SEC Rule 17a-4 and FINRA Rule 3110 require covered firms to preserve communications and to supervise them. If a system generates customer communications at volume, the retention and supervisory review architecture has to exist before the system ships, not after somebody asks for it.
Where the automation informs a decision rather than a message, the Prudential Regulation Authority's model risk management principles apply. The expectation is documented ownership, validation, and an understanding of how the model behaves outside its training conditions. Most of the effort in a regulated build goes here rather than into the model itself.
What it has to connect to
- Core banking and ledger
- Usually the constraint: batch windows and read-only access
- KYC and screening providers
- Rate limits and match thresholds shape the workflow
- Archival and supervision
- Retention under 17a-4 and supervisory review under 3110
- Accounting systems
- QuickBooks, Xero, NetSuite in the SME segment
What we will not automate here
- Final credit and risk decisions
- Automation handles extraction and preliminary scoring; the decision on a higher-risk customer stays with a person.
- Suitability and advice
- Regulated advice is not an output we let a system produce unreviewed.
- Unlogged customer communications
- A communication that is not preserved is a supervision failure regardless of its content.
Sector sources
- 01PS22/9: A new Consumer Duty, Financial Conduct Authority
- 02Model risk management principles for banks (SS1/23), Bank of England, Prudential Regulation Authority
- 0317 CFR 240.17a-4, Records to be preserved, Electronic Code of Federal Regulations
- 04FINRA Rule 3110, Supervision, Financial Industry Regulatory Authority
California's automated decision-making rules came into force on 1 January 2026, and they apply to ordinary business workflows, not just models.
The California Privacy Protection Agency's regulations covering automated decision-making technology took effect on 1 January 2026, with the obligations attaching to significant decisions phasing in through 2027. This is the substantive difference between building in California and building in most other states: there is an operative rule about automated decisions rather than a general privacy statute applied after the fact.
Two further statutes landed on the same date. AB 2013 requires documentation of the data used to train generative AI systems, and SB 53 imposes transparency and safety obligations on frontier models. Most of our clients are not training frontier models, but the training-data documentation requirement reaches anyone who fine-tunes or ships a generative system, and it is easier to satisfy by recording provenance during the build than to reconstruct afterwards.
All of this sits on top of CCPA and CPRA, which already give Californians deletion, access and opt-out rights that an automation has to be able to honour. A workflow that cannot locate and delete one person's data across every system it touches is not compliant, regardless of how well the model performs.
The full San Francisco briefing sets out the rest of the local picture.
Who you answer to here
- California Privacy Protection Agency
- ADMT regulations in force since 1 January 2026
- California Attorney General
- CCPA and CPRA enforcement
- FINRA and SEC
- For the financial services and fintech cluster
Sources
- 01CCPA regulations, including automated decision-making technology, California Privacy Protection Agency
- 02California Consumer Privacy Act (CCPA), California Attorney General
Common questions.
- Is there an AI automation agency for fintech in San Francisco?
- Yes. Axonari engineers AI automation systems for fintech businesses in San Francisco, working remotely from our engineering base in Jaipur. We have built systems covering kyc/aml onboarding and ongoing monitoring and transaction reconciliation and exception handling for organisations across San Francisco, CA. Projects start within 2–3 weeks of the initial brief.
- Is AI automation compliant with HIPAA in San Francisco?
- Compliance is engineered into every project we ship in San Francisco. SEC Rule 17a-4, FINRA Rules 3110 and 3120, BSA/AML requirements, and SOX Section 302/404 for publicly listed companies govern all financial AI automation. All automations that process personal or regulated data include a data protection impact assessment, human-in-the-loop controls for decisions with legal or material effect, and full audit logging.
- How much does fintech AI automation cost in San Francisco?
- Cost in San Francisco depends on complexity and scope. A focused single-workflow automation — for example, kyc/aml onboarding and ongoing monitoring — typically runs $10,000–$35,000. Multi-workflow builds with integrations and compliance scaffolding run $40,000–$100,000. All projects are fixed-price with agreed deliverables — no hourly billing.
- How long does a fintech AI automation project take in San Francisco?
- A single-workflow automation for a San Francisco-based fintech business takes 6–10 weeks from brief to go-live: 1–2 weeks for discovery and data mapping, 3–5 weeks for engineering and integration, and 1–2 weeks for testing, compliance review, and handover. Multi-workflow builds run 12–20 weeks. Timelines are fixed at the brief stage.