01
+37%
fraud catch rate
Real-time fraud detection
Streaming ML pipelines for card-not-present, ACH, wire, and synthetic-identity fraud. Two-tier models on the hot path, full audit logging, and analyst-facing case management.
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Industry
Financial Services.
PCI-DSS, SOX, and regional banking compliance engineered in from day one. Senior engineers shipping fraud detection, trading platforms, compliance engines, and core banking modernization for U.S. and international banks, lenders, and fintechs.
- PCI-DSS Level 1
- Aligned
- SOX (S-OX 404)
- Aligned
- FFIEC
- Supported
- SOC 2 Type II
- In flight
The landscape
Financial services engineering is a regulated, audited, real-time discipline — not a SaaS playbook.
The reality across banks, lenders, and fintechs: every release crosses PCI-DSS or SOX scope; every model decision lands in a regulator's eye; every integration touches a core banking system that nobody can take down for a maintenance window; every audit asks for evidence collected two quarters earlier; and every vendor risk review takes longer than the build phase. Buyers don't want a generic SaaS shop with a finance slide — they want engineers who know that real-time fraud detection, audit-defensible decisioning, and core-banking integration are first-class concerns.
Prosigns has shipped to U.S. regional banks, top-50 lenders, capital markets firms, and growth-stage fintechs across North America and the Middle East. We design for evidence collection, audit-defensibility, and real-time operating reality on day one. CITADEL (security/compliance) co-pilots every engagement; CORTEX (AI/ML) and FORGE (custom software) ship the workload; FOUNDATION (data) handles the substrate. The bench in the proposal is the bench in production.
Where we ship
6 workloads, grounded in this sector.
Specific applications we’ve built and operated for financial services buyers. Every example below is grounded in a real shipped engagement.
- 02
−47%
onboarding time
KYC and onboarding orchestration
Multi-agent KYC orchestration with identity verification, document classification, sanctions screening, and risk scoring. Human-in-the-loop checkpoints, BSA / AML examination-ready audit trail.
- 03
Trading and capital markets platforms
Order management, execution algorithms, post-trade reconciliation, and market data infrastructure with sub-millisecond latency where the workload demands it.
- 04
0
data loss across cutover
Core banking modernization
Strangler-fig migration off mainframe and legacy core banking with dual-running windows, replay infrastructure, and zero-data-loss cutover discipline.
- 05
Compliance and risk engines
Real-time SAR/STR generation, model risk management (SR 11-7), CECL provisioning, and the regulatory reporting infrastructure examiners actually pull from.
- 06
Open banking and integration
FDX-aligned APIs, account aggregation, payment initiation, and the consent-management infrastructure FCRA / GLBA compliance requires.
How we engage
4 phases, named in the SOW.
Each phase has a deliverable, an owner, and an acceptance criterion specific to financial services delivery.
- 01
Risk model and regulatory frame
Discovery starts with the regulatory frame: PCI-DSS scope, SOX controls, model risk management, and the examiner expectations specific to the workload. Architecture decisions land against the regulatory frame, not against generic best practices.
- 02
Compliance-driven architecture
Audit logging granularity defined before the first commit, encryption boundaries and key management settled up front, BAA / DPA chain documented, and evidence-collection pipelines designed in. Compliance is engineered, not bolted on.
- 03
Real-time engineering discipline
Streaming-first architecture for fraud, trading, and decisioning workloads. Latency budgets calibrated against user impact and counterparty SLAs, not engineering convenience. Failover and replay infrastructure tested under hostile conditions.
- 04
Operating cadence with examiners in mind
Continuous evidence collection, quarterly model risk reviews, monthly access reviews, and IR plan rehearsed quarterly. Examiners pull what they need in days; nothing assembled in panic the week before.
Practices in financial services
How each practice plays in this sector.
The capabilities below are scoped to the constraints financial services procurement actually enforces — compliance, audit, data residency, and vendor risk.
- 01CORTEX
AI & Machine Learning
Generative AI, agents, computer vision, predictive analytics, and MLOps — engineered for production.
Open the practiceIn Financial Services
Fraud detection, KYC orchestration, credit risk, AML, and capital-markets ML — with model risk management, audit logs, and SR 11-7-aligned governance.
- 02FORGE
Custom Software
SaaS, enterprise applications, legacy modernization, integrations, and mobile.
Open the practiceIn Financial Services
Trading platforms, core banking modernization, lending origination, and compliance engines — engineered against SOX, PCI-DSS, and examiner expectations.
- 03FOUNDATION + SKYWAY
Platform & Cloud
Cloud architecture, DevOps, SRE, migrations, data engineering.
Open the practiceIn Financial Services
FFIEC-aware AWS / Azure architectures with explicit data-residency, IaC-backed evidence collection, and the operating discipline regulators expect.
- 04GUARDIAN + CITADEL
Quality & Security
Test automation, performance, accessibility, application security, secure SDLC.
Open the practiceIn Financial Services
PCI-DSS Level 1 program build, SOX evidence collection, penetration testing, and the audit-defensible AppSec discipline regulated workloads require.
Selected work
Quantified outcomes, not adjectives.
- 01Financial Services
+37%
fraud catch rateReal-time fraud detection for a US regional bank.
Replaced a rules-based engine with a streaming ML pipeline on AWS. Reduced false positives 42% while raising true catches. SR 11-7-aligned governance frame, regulator-ready audit logs.
9 months
- 02Financial Services
−42%
cloud cost vs prior architectureMulti-account AWS modernization for a US regional bank.
Replaced a single-account hand-managed AWS estate with a multi-account, IaC-backed organization. Centralized identity, transit gateway network, observability stack, SOC 2 evidence collection.
11 months
Common questions
Asked before the first call.
- 01
Are you PCI-DSS Level 1 aligned?
Yes — we engineer to PCI-DSS Level 1 standards on every cardholder-data engagement. CITADEL co-pilots scope definition, network segmentation, encryption boundaries, and continuous-monitoring evidence. We support QSA-led assessments with the same evidence pipeline that runs SOC 2 — one operating discipline, multiple frameworks.
- 02
Can you ship to SR 11-7 model risk management standards?
Yes. Every ML model we deploy in financial services ships with model documentation aligned to SR 11-7: development methodology, performance metrics, validation results, and ongoing monitoring. We co-author with your model risk function rather than handing off a black box.
- 03
What about FFIEC and state-level supervision?
FFIEC IT examination handbook is part of our default operating frame. State-level supervision (NYDFS Part 500, Texas DOB) layers on as the engagement requires. We design with the regulatory frame in mind from architecture, not retrofitted before exam.
- 04
Can you integrate with our core banking system?
Yes — FIS, Jack Henry, Fiserv, Temenos, Finastra, and custom mainframe cores are all in our active engagement portfolio. We design integrations as primary scope (not phase 2), with documented interface contracts, dual-write windows for critical paths, and explicit fallback for core unavailability.
- 05
Do you support BSA / AML examination support?
Yes. Audit logs, alert investigation tooling, SAR / CTR generation, and the examination-ready evidence pipeline are part of every fraud / AML engagement. We've supported clients through OCC, FRB, FDIC, and state-level examinations.
- 06
What does a financial services engagement cost?
Discovery and risk modeling: 4–6 weeks, $80K–$200K. Production builds: 4–9 months, $400K–$2M depending on regulatory scope. Multi-quarter modernization programs: $2M–$8M+. Managed Services for ongoing operations: $40K–$200K monthly retainer. Brackets published honestly so visitors self-qualify before the first call.
Talk to us
Bring a financial services problem. We’ll bring a senior engineer.
A senior engineer plus the relevant department lead joins the first call. No discovery gauntlet, no junior reps.