Banking, Capital Markets & Fintech AI
Bank-grade AI: fraud scoring in 12ms, credit decisions in 200ms, AML triage at $0.03/case
We build inference systems for tier-1 banks, neobanks, and broker-dealers — auditable models that survive SR 11-7 model risk review, run inside your VPC, and integrate with core platforms (Temenos, FIS Profile, Fiserv DNA, Finacle) without breaking nightly batch.
Sub-15ms fraud inference at 8k+ TPS — gradient-boosted models on transaction velocity, device fingerprint, and graph-walk mule-network features
GDPR Article 22-compliant lending — SHAP-based reason codes attached to every automated credit decision, fair-lending disparate-impact tests pre-deployment
AML/KYC automation at ~$0.03 per case — entity resolution against OFAC, UN, EU consolidated lists; document NER for passports, utility bills, articles of incorporation
Air-gap deployable — full model lifecycle (training, registry, drift monitoring) inside your PCI-DSS cardholder data environment, no internet egress required
Regulatory Coverage
Compliance frameworks we build to
Security & data protection
SOC 2 Type IIPCI-DSS Level 1ISO 27001GDPR Art. 22
Banking & markets regulation
SR 11-7 (model risk)AML / BSAKYC / CDDFATCAMiFID IISEC 17a-4 (retention)ECOA / Reg B
Track record
50+
Projects delivered
96%
Client satisfaction
2019
Founded
Six AI systems we build for banks & broker-dealers
Each one is shipped as an inference service plus an MRM artefact pack — model card, validation report, drift dashboards, challenger registry — so model risk and second-line review pass on the first cycle.
Real-time fraud & scam detection
Sub-15ms scoring at 8k+ TPS
- Feature pipeline computing 200+ signals per swipe: transaction velocity (1m / 1h / 24h), device fingerprint, geo-velocity, BIN-MCC mismatch, behavioural biometrics
- Graph neural networks over the account-counterparty graph to surface mule rings and synthetic-identity clusters before they cash out
- Two-stage architecture: a lightweight tree model for the hot path, an LLM-assisted reviewer for the queue — keeps human analyst load below 5% of flagged volume
- Drift monitoring on every feature with PSI thresholds, auto-shadow deploy of challenger models, KS-test gating on champion-promotion
Credit risk & explainable underwriting
GDPR Art. 22 / ECOA-ready decisioning
- Hybrid scorecard + gradient-boosted model — scorecard owns the explainable backbone, ML owns the residual lift; both go through MRM challenger review
- SHAP-based adverse-action reason codes generated at decision time, mapped to FCRA/Reg B disclosure language
- Fair-lending tests pre-deployment: disparate impact across protected classes, AUC parity, calibration drift by segment
- Champion/challenger A/B in production with bandit allocation, lift measured on 90-day vintage default rates not just dev-set AUC
AML, KYC & sanctions screening
~$0.03 per case end-to-end
- Entity resolution across OFAC SDN, UN Consolidated, EU Sanctions, UK HMT, PEP and adverse-media corpora — phonetic + transliteration matching for non-Latin scripts
- Document NER on passports, drivers' licences, articles of incorporation, beneficial-ownership filings — extracts UBO chains for CDD/EDD tiering
- Transaction monitoring with typology-aware rules (structuring, layering, trade-based ML) + unsupervised anomaly detection on peer-group behaviour
- Auto-drafted SAR/STR narratives for analyst review — never auto-filed; humans always sign
Quantitative & algorithmic execution
VWAP / TWAP / IS strategies with risk gates
- Backtesting framework with proper survivorship-bias correction, point-in-time fundamentals, and realistic slippage models (square-root, almgren-chriss)
- Risk-aware execution: pre-trade TCA, parent-child order slicing, venue selection across lit, dark and RFQ pools
- Real-time market-data ingestion over FIX 4.4 / 5.0 SP2 and binary feeds (ITCH, OUCH) — nanosecond-grained event store
- Kill switches wired to position limits, P&L drawdown, and venue connectivity heartbeats — MiFID II RTS 6 algo-trading governance baked in
Conversational banking with audit trail
Core-banking integrated, regulator-defensible
- Multi-turn dialog grounded in customer's actual ledger state via read-only adapters to Temenos T24, FIS Profile, Fiserv DNA, Finacle, Mambu
- Every model call logged with prompt, retrieved context, tool calls, response, and customer ID — 7-year retention to satisfy SEC Rule 17a-4 / FINRA 4511
- Hard guardrails on dispensable actions (transfers, card freezes) — model proposes, deterministic policy engine and step-up auth dispose
- Disclosure injection: investment-advice queries get the Reg BI / MiFID II appropriateness disclaimer; credit queries get the FCRA notice
Churn, LTV & next-best-action
Survival analysis, not just classification
- Cox proportional-hazards and DeepSurv models — predicts time-to-attrition, not just a static churn flag, so retention teams know when to intervene
- Customer-level LTV combining product holdings, balance trajectories, and channel engagement — feeds capital-allocation decisions on acquisition spend
- Uplift modelling for retention offers — measures incremental effect, avoids paying customers who would have stayed anyway
- Trigger orchestration into Salesforce Financial Services Cloud, Adobe Campaign, or in-app messaging via Braze/Iterable
Financial-grade data infrastructure
Financial data is not generic event data. Money moves through ISO 20022, FIX and SWIFT — not JSON. Trades have to be reconstructable seven years later. A "late-arriving fact" in a fraud table is a chargeback in court. Here is what we actually build.
Payments & messaging protocol fluency
Native ingestion of the wire formats that actually carry money.
- ISO 20022 (pacs.008, pacs.009, camt.053) — full XSD validation, structured remittance preserved end-to-end for ISO migration deadlines (Fed, T2, CHAPS)
- FIX 4.2 / 4.4 / 5.0 SP2 for order flow; binary feeds (NASDAQ ITCH / OUCH, CME MDP 3.0) for tick data
- SWIFT MT-to-MX transformation with field-level lineage so reconciliation teams can trace any cent back to its original message
- Open Banking: UK CMA v3.1, Berlin Group NextGenPSD2, FDX 6.0 — consent management, JWKS rotation, AISP/PISP scopes
Trade reconstruction & WORM retention
Built for SEC 17a-4(f), FINRA 4511, MiFID II RTS 6.
- 5–7 year immutable retention on S3 Object Lock / Azure Immutable Blob — auditor-ready notarised manifests
- Time-synchronised event log across order, market data, voice, chat, and email — UTC-aligned to 100µs
- Trade reconstruction playback: any executed order rebuildable from raw events on demand
- Hash-chained audit log so any tampering with the historical record is detectable on read
Ledger CDC & exactly-once event streams
No lost debits. No double credits. Ever.
- Change data capture from core banking (Temenos T24, FIS Profile, Fiserv DNA, Finacle, Mambu) via Debezium or vendor adapters — log-based, never query-based
- Kafka topics partitioned by account key with idempotent producers and transactional consumers — exactly-once semantics from ledger to lake
- Schema Registry with Avro/Protobuf forward+backward compatibility enforcement so downstream consumers never break on a release
- Saga-pattern outbox tables in the core for cross-system writes (e.g. transfer + notification + ledger entry) without distributed-transaction lockup
Market data & time-series at low query latency
Tick stores that don't fall over on backfill.
- ClickHouse and kdb+ for OHLCV and tick — sub-second range queries over billions of rows for risk and quant teams
- Bitemporal modelling: valid-time vs transaction-time so corrections to historical prints don't poison prior risk calcs
- Materialised views for L1 quotes, NBBO reconstruction, volume profile aggregates
- Cold tier on Parquet + Iceberg with z-order on (symbol, ts) — cheap historical, fast recent
Regulatory reporting & risk modelling
From raw ledger to FR Y-14Q, CCAR, FRTB, IFRS 9.
- dbt projects with tested transformations — every regulatory metric (PD, LGD, EAD, ECL stages) has a tested model, not a spreadsheet
- Snowflake or Databricks for stress-testing scenarios — Monte Carlo for VaR/ES, full revaluation under historical and hypothetical shocks
- Lineage capture (OpenLineage, Spline) so the regulator's 'where did this number come from' has a literal graph answer
- FRTB IMA-ready risk-factor data quality framework: real-price observability, modellability tests, non-modellable risk factor capitalisation
Deployment topology for cardholder data
Your VPC, your KMS, your audit logs.
- Reference deploys on AWS (VPC + PrivateLink + KMS + Macie), Azure (Private Link + Key Vault HSM), GCP (VPC-SC + CMEK) — no public egress
- Tokenisation at the perimeter so PANs never enter analytics — keeps the data lake out of PCI-DSS scope
- Air-gap mode: full MLOps stack (MLflow registry, Argo workflows, monitoring) shipped as a self-hosted bundle for banks with no internet egress
- Customer-managed keys for at-rest encryption, mTLS internally, FIPS 140-2 validated modules where required
Reference stack we have shipped to production
Kafka (ledger event bus)Debezium (CDC)ClickHouse (tick + analytics)kdb+ (HFT timeseries)Snowflake (risk modelling)Databricks (feature store + MLflow)dbt (regulatory transforms)Iceberg + Parquet (cold tier)Argo + MLflow (MLOps)OpenLineage (audit graph)HashiCorp Vault (secrets, HSM)AWS PrivateLink / Azure Private Link
What banks & broker-dealers actually ask us
Technical answers to the regulatory, integration, and engineering questions that come up in the first scoping call.