AI for financial services

Custom AI/ML solutions for financial services industry

Custom AI and ML for banks, asset managers, fintechs, and credit unions. From fraud detection and credit modeling to compliance automation and personalized customer experiences — built for regulator review.

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40–60%

Alert-volume reduction in fraud and AML with ML scoring before human review.

$1.5B

Documented AI fraud-detection savings at JPMorgan Chase — the public benchmark every bank is being measured against.

30–40%

Less time preparing documentation for regulator exams with automated MRM workflows.

Achieve immediate, organization-wide results

Six measurable outcomes across underwriting, claims, and actuarial functions — deployed in months, not years.

Real-Time Fraud Scoring

Per-transaction ML scoring under 200ms. Cuts false positives 40–60% so analysts work the alerts that matter.

AML & KYC Automation

Entity resolution, sanctions screening, and SAR/STR narrative-drafting LLMs. Regulator-exam-ready documentation by default.

Credit Decisioning

Alternative-data credit models with explainable scorecards and adverse-action reasoning that passes ECOA review.

Markets & Asset Allocation

Signal generation, regime detection, and portfolio optimization — built per-strategy, not as SaaS.

Investment Research & M&A Diligence AI

RAG-grounded research-AI over your private analyst notes + filings + earnings calls + regulatory rulebooks. Custom for sell-side, buy-side, and M&A advisory teams that can't ship data to vendor SaaS.

Model Risk Management

Documentation, backtesting, sensitivity testing, and validation packs that pass CCAR/DFAST and state regulator review.

Capabilities across the financial services value chain

Lending & Credit Risk

Fraud, AML & Compliance

Markets, Trading & Asset Management

Customer Experience & Operations

From the playbook

How a mid-size bank cut fraud false positives 55% and saved $12M in analyst hours

A $25B-asset regional bank was drowning in 40,000 daily fraud alerts, with analysts dispositioning each one in 4–6 minutes. We built a gradient-boosted fraud scoring pipeline trained on 18 months of dispositioned alerts, enriched with device, geolocation, and behavioral-biometric features. Daily alert volume dropped from 40,000 to 18,000 with 55% fewer false positives at the same true-positive rate. Annual analyst hours recovered: $12M. The same scoring engine now feeds real-time decline decisions on card-not-present transactions, with a documented MRM package that passed the bank’s annual OCC exam.

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Speak with a financial services AI expert

A 45-minute scoping call. We’ll come prepared with your appetite, your loss-cost benchmarks, and a directional read on which models move the needle on your line of business.

Ask us about

Real-time fraud detection with sub-200ms latency
AML entity resolution and SAR/STR narrative LLMs
Alternative-data credit and adverse-action modeling
Alpha-signal generation and regime detection
Investment research and M&A diligence LLMs over private corpora
MRM-ready models for OCC/Fed/CFPB review

Frequently asked questions

Every model ships with a full MRM package built to SR 11-7 / OCC Bulletin 2011-12 standards: documentation, sensitivity tests, backtesting against holdout periods, monitoring dashboards, and sign-off-ready validation packs. We've shipped models that have passed CCAR/DFAST-style scrutiny, state regulator review, and CFPB fair-lending examinations.

Yes. We've shipped models that read from FIS, Fiserv, Jack Henry, Temenos, and Mambu cores via standard APIs and event streams. Real-time scoring services typically sit between your fraud middleware (NICE Actimize, SAS, Featurespace) and your decisioning layer. You own the integration code and model artifacts.

Yes — that's the default. Every model gets a tiered classification (high/medium/low risk), validation by an independent test team, ongoing performance monitoring, and challenger-model benchmarking. We deliver the documentation pack your second-line MRM team needs to approve and the third-line audit team needs to validate.

Yes. The underlying ML platform (feature store, model registry, monitoring, governance, MRM workflow) is reusable across all three. Domain models differ — retail focuses on fraud and credit, commercial on entity-resolution and trade-finance ML, wealth on signal generation — but most universal banks running their own platform run one shared stack with multiple model families.