AI automation for financial services.
AI automation built for banks, lenders, wealth managers, and fintechs. Aligned to SOX, PCI-DSS, GLBA, and AML/KYC.
Financial services runs on controls, reconciliation, and paperwork. A single corporate KYC review costs banks roughly USD 2,600 and takes about 95 days, and 90 to 95 percent of AML transaction-monitoring alerts turn out to be false positives that still have to be worked by hand. AI takes over the repeatable parts: KYC document collection and verification, AML alert triage, fraud pattern detection, transaction reconciliation, month-end close, and client onboarding. Every deployment is built to the frameworks you already answer to, SOX, PCI-DSS, and GLBA in the US, plus FINTRAC, OSFI, and PIPEDA in Canada, with encryption, role-based access, segregation of duties, and a full audit trail by default. We are headquartered in Calgary, we ship in 2 to 6 weeks, and we start with one workflow so you can prove the ROI before scaling.
Handled end to end by professionals.
Chad, Jesse, and Camilly lead the team that builds, ships, and maintains your automations.
Sources: MarketsandMarkets, AI in Finance Market, 2024 base, 2025; Industry AML transaction-monitoring benchmarking, 2024 (FluxForce; Facctum)
In short: The Automators builds AI automation for banks, credit unions, lenders, wealth and asset managers, and fintechs: KYC onboarding and verification, AML alert triage, fraud detection, transaction reconciliation, month-end close, and client servicing. Every build is aligned to SOX, PCI-DSS, and GLBA in the US and to FINTRAC (PCMLTFA), OSFI, and PIPEDA in Canada, with Canadian data residency available, encryption, role-based access, segregation of duties, and complete audit logging. Most first projects ship in 2 to 6 weeks. We start with one high-leverage workflow, measure the hours and cost it returns, then scale from there. Model outputs are always reviewed by a person before any regulatory filing, credit decision, or money movement.
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Why financial services is automating now
Financial services is one of the most control-heavy and paperwork-intensive industries in the economy, and the cost of that overhead is measurable. In the US and Canada, financial institutions spent USD 61 billion on financial-crime compliance in 2024, and 99 percent of institutions reported their compliance costs going up, per the LexisNexis Risk Solutions True Cost of Financial Crime Compliance study conducted by Forrester. The market for AI to reduce this load is scaling fast: MarketsandMarkets valued the AI in Finance market at USD 38.36 billion in 2024 and projects it to reach USD 190.33 billion by 2030 at a 30.6 percent compound annual growth rate, with compliance automation platforms cited as the fastest-growing segment.
The burden concentrates in onboarding and financial-crime operations. Fenergo research found that a single corporate KYC review costs a bank about USD 2,598 and takes roughly 95 days end to end, and Statista data for 2024 shows the majority of banks spending between USD 2,001 and USD 2,500 per corporate KYC review. Slow onboarding is not just a cost, it loses business: Fenergo found 67 percent of banks lost clients in 2024 due to inefficient onboarding and KYC, up 19 percent from 2023. On the monitoring side, 90 to 95 percent of AML transaction-monitoring alerts are false positives, and compliance teams spend an estimated 70 to 80 percent of their alert-handling time clearing alarms that are not linked to any crime.
The stakes on the other side are just as large. The Nasdaq Verafin 2024 Global Financial Crime Report estimated that more than USD 3.1 trillion in illicit funds flowed through the global financial system in 2023, with fraud scams and bank-fraud schemes alone accounting for nearly USD 500 billion. Regulators are not lenient about it: global bank fines for financial-crime, consumer-protection, and operating breaches reached about USD 4.5 billion in 2024, with AML and transaction-monitoring failures on their own exceeding USD 3.3 billion. This is precisely the work AI is good at: KYC agents collect and verify documents and screen against sanctions and PEP lists, AML agents enrich and triage alerts so analysts see the risky ones first, fraud models score transactions in real time, and reconciliation agents match transactions across ledgers so month-end close does not consume 120 to 150 manual hours.
Compliance is the gate, and it is non-negotiable. In the US that means SOX for financial-reporting controls, PCI-DSS wherever cardholder data is handled, GLBA for the privacy and safeguarding of customer financial information, and the BSA/AML regime with a documented Customer Identification Program, all under SEC and FINRA oversight for regulated firms. In Canada it means FINTRAC and the PCMLTFA for reporting entities, OSFI for federally regulated institutions, and PIPEDA for personal information, with Canadian data residency where required. We are a Calgary-based agency serving firms across Canada and the US, so we design for both regimes from day one. We start with one high-leverage workflow, prove the ROI in weeks, keep a human in the loop on anything that touches a filing, a credit decision, or money movement, and scale only what works.
What we automate for financial services firms.
The functions where financial services teams spend the most hours on repeatable work, each mapped to the automation we deploy and the outcome it drives.
A single corporate KYC review costs a bank roughly USD 2,598 and takes about 95 days, with staff collecting documents, rekeying data across systems, and screening names against sanctions and PEP lists by hand, and slow onboarding cost 67 percent of banks clients in 2024.
A KYC agent collects and verifies identity documents, extracts and structures the data, screens against sanctions, PEP, and adverse-media lists, flags mismatches for review, and assembles a complete onboarding file with the supporting evidence attached.
Faster onboarding and a clean, screened client file is the typical benchmark KYC automation targets against a roughly USD 2,598 and 95-day per-review baseline.90 to 95 percent of AML transaction-monitoring alerts are false positives, yet each one must be worked, and compliance teams spend an estimated 70 to 80 percent of their alert-handling time clearing alarms that are not linked to any crime.
An AML agent enriches each alert with customer, transaction, and network context, scores and ranks it by genuine risk, auto-documents and dispositions clear false positives per policy, and routes the risky minority to an analyst with the evidence already assembled.
More analyst time on genuinely risky alerts and less on noise is the benchmark alert-triage automation targets against a 90 to 95 percent false-positive baseline, with a human deciding every escalation and SAR.Fraud scams and bank-fraud schemes accounted for nearly USD 500 billion globally in 2023, and rules-only detection either misses novel patterns or floods teams with false declines, while disputes and chargebacks pile up in manual queues.
A fraud agent scores transactions in real time against behavioral and network signals, holds or steps up only the suspicious ones, drafts dispute and chargeback documentation, and hands genuine cases to an investigator with the full transaction trail.
Sharper real-time detection with fewer false declines and faster dispute handling is the typical benchmark this automation is built to deliver, with final action reserved for a person.Reconciliation is consistently the most time-consuming close task, taking finance teams roughly 20 to 50 hours a month across 3 to 5 disconnected systems, and unmatched items surface late as restatement and audit risk.
A reconciliation agent ingests statements and ledgers, matches transactions across accounts and systems, categorizes and routes the exceptions, and drafts the adjusting entries for a controller to review and post, all against defined rules.
Fewer unmatched items and hours returned to the finance team is the benchmark reconciliation automation targets against a 20 to 50 hour monthly manual load.Half of finance teams take longer than five business days to close the books, and a traditional close can consume 120 to 150 manual hours across the team, gated by cross-team dependencies and spreadsheet-driven steps.
A close agent orchestrates the checklist, pulls and reconciles source data, drafts accruals and journal entries for review, chases outstanding items, and assembles the close package and variance commentary with a full control trail for SOX evidence.
A shorter, more predictable close with audit evidence captured as it runs is the typical benchmark close automation targets against a 120 to 150 manual-hour baseline, with entries reviewed and posted by a person.Banks, lenders, and wealth firms lose after-hours inquiries, statement and balance requests, document collection, and status updates to voicemail and manual outreach, which hurts client experience and clogs relationship-manager time.
A client-servicing agent answers routine account, statement, and product questions 24/7, collects and chases outstanding documents, sends status and renewal updates, and hands complex advice, complaints, or account changes to a licensed person, all logged for the record.
Around-the-clock coverage of routine requests and consistent document and status follow-up is the typical benchmark this automation is built to deliver, with advice and account actions kept with your staff.Most financial services teams start with one high-leverage automation, prove the ROI in weeks, then scale from there.
Book free consultationWhere automation leverage runs deepest.
Ranked by the breadth of automation opportunity we see across each area's core workflows: the wider the bar, the more of that work our deployments can take over today.
Automation patterns in financial services.
Illustrative examples of the automations we build for financial services firms. See our published case studies for real client work.
| Segment | Engagement | Outcomes & impact |
|---|---|---|
| CASE 01Regional bank | KYC onboarding automation for a regional bankRegional and commercial banks carry a heavy onboarding load, with a single corporate KYC review costing roughly USD 2,598 and taking about 95 days, and Fenergo found 67 percent of banks lost clients in 2024 to slow onboarding. An AI onboarding agent collects and verifies identity documents, screens each entity against sanctions, PEP, and adverse-media lists, flags mismatches for a compliance officer, and assembles a complete, evidenced onboarding file for sign-off. | VERIFIEDIdentity documents collected, verified, and structured without manual rekeying. SCREENEDSanctions, PEP, and adverse-media screening run on every entity with hits flagged for review. FILE BUILTA complete, evidenced onboarding file assembled for the compliance officer to sign off. AUDIT-READYBuilt to BSA/AML and GLBA, with a full audit trail across the onboarding path. |
| CASE 02Credit union | AML alert triage for a credit union compliance teamWith 90 to 95 percent of AML transaction-monitoring alerts turning out to be false positives, a credit union compliance team spent most of its time clearing alarms that were not linked to any crime. An AI alert-triage agent enriches each alert with customer, transaction, and network context, scores and ranks it by genuine risk, auto-documents and dispositions the clear false positives per policy, and routes the risky minority to an analyst with the evidence already assembled. Every SAR decision stays with a person. | ENRICHEDEach alert enriched with customer, transaction, and network context up front. RANKEDAlerts scored by genuine risk against a 90 to 95 percent false-positive baseline. AUTO-CLEAREDClear false positives auto-documented and dispositioned per written policy. HUMAN SARRisky alerts and every SAR decision escalated to a human with evidence attached. |
| CASE 03Wealth & asset manager | Reconciliation and close automation for a wealth managerReconciliation is the most time-consuming close task, running roughly 20 to 50 hours a month across 3 to 5 systems, and half of finance teams take longer than five business days to close. An AI reconciliation and close agent ingests statements and ledgers, matches transactions across accounts, routes exceptions, drafts the adjusting and journal entries, and assembles the close package with SOX control evidence, leaving a controller to review and post. | MATCHEDTransactions matched across custodial, bank, and ledger systems automatically. EXCEPTIONS ROUTEDUnmatched items categorized and routed instead of chased across spreadsheets. ENTRIES DRAFTEDAdjusting and journal entries drafted for controller review before posting. SOX EVIDENCEClose package and control evidence assembled as the process runs. |
| CASE 04Fintech / digital lender | Fraud detection and client servicing for a digital lenderA digital lender fielded high transaction volume and after-hours inquiries that rules-only detection and manual queues could not keep up with, against a backdrop where fraud and bank-fraud schemes cost nearly USD 500 billion globally in 2023. An AI fraud agent scores transactions in real time, steps up only the suspicious ones, and drafts dispute documentation, while a client-servicing agent handles routine account and status questions 24/7 and hands genuine issues and credit decisions to licensed staff. | REAL-TIMETransactions scored in real time with only suspicious ones held or stepped up. DISPUTES DRAFTEDDispute and chargeback documentation drafted for an investigator to review. 24/7Routine account, balance, and status questions handled around the clock. HUMAN DECIDESCredit decisions and account changes routed to licensed staff, PCI-DSS aligned. |
Financial Services runs on throughput.
Sources: LexisNexis Risk Solutions, True Cost of Financial Crime Compliance (Forrester), 2024
Compliance & regulators in financial services.
The regulatory framework every financial services deployment meets by default.
For US firms, financial-reporting workflows are built to preserve SOX internal controls: segregation of duties, maker-checker approval, and control evidence captured on every automated action. Customer financial information is handled under the GLBA Safeguards Rule with encryption and least-privilege access, and anywhere cardholder data is involved we build to PCI-DSS. Regulated broker-dealers and advisers also operate under SEC and FINRA rules. Note these are frameworks we design and operate to, not certifications we hold, so we rely on documented controls and, where applicable, your auditors and QSA.
AML and onboarding automation is built around the BSA/AML regime in the US, including a documented Customer Identification Program, sanctions and PEP screening, and human-decided SAR filing. In Canada, deployments are built for FINTRAC and the PCMLTFA obligations that apply to reporting entities, including suspicious-transaction and large-cash-transaction reporting timelines, with OSFI-aligned AML governance for federally regulated institutions. Automation prepares and documents; a qualified person always makes the reporting and escalation decision.
Personal and financial data is encrypted in transit and at rest, access is role-based and least-privilege, and every automated action is written to an immutable audit trail so you can answer who did what, when, and on which record. For Canadian clients we build to PIPEDA with Canadian data residency available where required, and we never train external models on your customer or transaction data. Anything touching a filing, a credit decision, or money movement keeps a human in the loop by design.
Which services fit financial services firms?
KYC and onboarding documents, loan and account applications, sanctions and PEP screening packets, and AML and dispute case files extracted, verified, structured, and routed with a full audit trail for review before any decision.
Learn more →Transaction reconciliation across ledgers and systems, month-end and financial close orchestration, accrual and journal-entry drafting, and variance commentary, with SOX control evidence captured as the process runs and entries posted by a controller.
Learn more →Orchestration across your core banking, CRM, AML/KYC, and reconciliation systems, so onboarding, alert triage, and close move automatically with segregation of duties and human approval built in, instead of waiting in a queue.
Learn more →Resources for financial services firms.
Technologies we work with.
We integrate with the platforms your team is on today. No rip-and-replace.
n8n
Make
Zapier
OpenAI
Anthropic
Supabase
Salesforce
HubSpot
Twilio
Microsoft 365
Google Workspaceand many more…
Financial Services AI, answered.
Is AI automation compliant with financial-services regulations?
What financial-services workflows can be automated?
How much does financial-services AI automation cost?
Will it integrate with our core banking and compliance systems?
How do you handle false positives in AML and fraud without missing real risk?
How fast can we go live?
Is our customer and transaction data safe?
Why work with a Calgary-based agency for financial-services automation?
Related industries we serve.
Tell us what's slowing you down.
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