Risk engines, fraud scoring, and digital banking. Not another fintech pitch deck.
We build the decision systems behind lending, payments, and compliance.
Banks and fintechs need AI that scores risk in milliseconds, catches fraud without blocking good customers, and meets regulatory requirements without a 12-month compliance project. We build those systems.
Why AI in FinTech is different.
Financial services runs on decisions. Approve or decline. Flag or clear. Price or pass. Every one of those decisions was built on rules written a decade ago. Those rules can't keep up.
The banks deploying ML for credit decisioning see 30-40% more approvals with lower default rates. The ones still running FICO-only models lose $2B in viable loans they never see. Neobanks ship features in weeks. Legacy banks file change requests.
The gap is not about technology. Everyone has access to the same models. The gap is execution: who can take a model from notebook to production in 6 weeks instead of 18 months. Who can meet PCI-DSS and SOX without a separate 12-month compliance project. That's what we do.
The Problems That Slow You Down
Legacy core banking is a 20-year anchor
Every new feature routes through mainframe-era systems. Integration takes months, not weeks. Your competitors ship while you wait for IT.
Fraud models that block real customers
High false positive rates cost more than the fraud they catch. Customers leave after the third declined transaction.
Regulatory compliance as a moving target
PCI-DSS, SOX, AML, KYC: every quarter brings new requirements. Your compliance team is always catching up, never ahead.
Customer experience stuck in 2015
Branch-era UX ported to mobile. Digital-native competitors are 5 years ahead on onboarding, self-service, and personalization.
Systems for FinTech
Risk Scoring Engines
ML models that score credit, lending, and counterparty risk in milliseconds. Real-time decisions, not overnight batch.
View service →Fraud Detection AI
Multi-signal fraud models that catch 95%+ of fraud while keeping false positives under 2%. Customers stay, fraudsters don't.
View service →Compliance Automation
SOC 2, PCI-DSS, AML/KYC: controls, evidence collection, and audit management that keeps you ahead of regulators.
View service →Digital Banking Platforms
API-first architecture for account opening, payments, lending, built for speed, multi-tenancy, and white-label.
View service →Financial Data Infrastructure
Real-time pipelines from transaction systems, market feeds, and third-party data into a unified analytics layer.
View service →These aren't pitch deck scenarios. Every use case maps to a system pattern we've built and deployed in production.
What We Build in FinTech
Transaction Fraud Detection
Head of Fraud Prevention1M+ daily card transactions overwhelm rules engines. 95% false positives, $5-10/investigation.
XGBoost anomaly detection on transaction graphs (amount, velocity, geolocation, device) + isolation forest for novel patterns, retrained weekly.
KYC Document Verification
Director of Compliance Operations10K+ onboarding docs/week reviewed manually. 3-5 day delays, 15% error rates, $50M/year compliance fines.
CNN for ID forgery + BERT NLP for PII extraction from passports/statements, fuzzy-matched against sanctions lists.
Credit Risk Scoring
VP of Risk ManagementLegacy FICO rejects 30% viable SME loans, missing alternative data, $2B untapped revenue.
Gradient boosting on alternative data (bank transactions, utility payments, social proofs) via core banking APIs, SHAP explainability.
AML Transaction Monitoring
Chief Compliance OfficerRules-based systems flood SAR teams with 90% false alerts; 48-hour review delays risk $1M+ fines.
Graph neural networks on transaction networks (entity resolution, flow patterns) + supervised classification on historical SARs.
Trade Surveillance
Director of Market Compliance50K daily equity trades manually triaged; 4-6 hours per high-risk alert.
LLM+RAG summarizes trade patterns, generates resolution narratives on enriched data, with risk prioritization.
Loan Origination Automation
Head of Lending OperationsEnd-to-end loan apps take 7-10 days manually; 25% abandonment, $20K/provider/month staff costs.
OCR+NLP parsing bank statements/ITRs, LLM agent validates compliance, feeds LOS via API.
Sanctions Screening
VP of Financial Crime ComplianceFuzzy matching on 100K+ payments/day. 92% false positives from name variations, $5M lost revenue.
Transformer-based entity matching with explainable scoring, real-time API to payments gateway.
Collections Recovery Agent
Director of CollectionsCall centers handle only 20% of 100K delinquent accounts/month. 15% recovery rate, $50M uncollected/quarter.
LLM agentic workflow for conversational outreach (SMS/voice), personalized plans from 100+ risk variables, TCPA compliant.
Building the system is half the job. Growing the business around it is the other half.
How We Grow FinTech Brands
AEO for Banking Product Queries
Head of Growth MarketingFintechs lose 60% of "best business loan" queries to ChatGPT/Perplexity; zero-click rate 40%.
GEO-optimized guides with structured comparisons (rates, fees, eligibility), schema, Perplexity monitoring to application forms.
LinkedIn Ads for B2B Fintech Pipeline
Director of Demand GenerationLinkedIn campaigns average 1.45% engagement but under 10% MQL rate; CAC $658 without nurture.
LinkedIn video ads to gated webinars to HubSpot forms, ICP targeting (CFOs in finance), retargeting sequences.
HubSpot Lead Scoring for SMB Banking
VP Marketing Operations5K leads/month, 65% unqualified; no behavioral scoring delays handoff 2 weeks.
HubSpot scoring on visits/downloads/demo requests, compliant nurture, Salesforce sync.
AI Ads for Loan Products
Performance Marketing DirectorGoogle CPC $8-15 for "personal loan" with 2-3% conversion; no AI engine presence.
Gemini sponsored answers + Google PMax, A/B creatives with application CTAs, UTM attribution.
SEO + AEO for Fintech Comparison Pages
SEO ManagerAffiliate traffic drops 25% from AI overviews; rank #3-5 on "crypto wallet comparison."
Structured tables (fees, security), schema reviews, finance site backlinks, AEO audits.
How We Work With FinTech
Machine Learning Solutions
Prediction systems that run 24/7. Not proof-of-concepts gathering dust.
Learn more →AI Systems & Agents
LLMs, agent chains, and intelligent workflows, wired into your ops, not sitting in a sandbox.
Learn more →Security & Compliance
SOC 2, ISO 27001, HIPAA, AI governance: audits that close enterprise revenue, not just check boxes.
Learn more →Platform Engineering
SaaS architecture, internal tools, API ecosystems: built for what comes after the MVP.
Learn more →Frequently asked questions
Yes. We build with explainability from the start. Model decisions include feature attribution, audit trails, and documentation regulators expect.
We architect payment systems within PCI scope boundaries from day one. Tokenization, encryption, access controls, and evidence collection built into the CI/CD pipeline.
Working prototype in 4-6 weeks with your historical data. Production deployment with monitoring and retraining pipelines: 2-3 months.
AI moves fast. Stay ahead.
No spam. One actionable email per week on AI systems and growth.
Related Answers
Should we build a custom AI system or buy an off-the-shelf tool?
If a SaaS tool does 80% of what you need, buy it. If your competitive advantage depends on the other 20%, build it. Here's the decision framework.
5 min readanswerHow do I know if my data is ready for AI?
If you can't answer "where is our customer data?" in under 30 seconds, it's not ready. Here are the 5 signs your data needs work before AI can help.
3 min readYour competitors ship features weekly. How long does it take you?
Start with a technical assessment. We'll identify the highest-impact opportunities and build a roadmap that moves at fintech speed.