AI for Financial Services

AI for fraud detection, credit risk, AML compliance, and customer experience — built for institutions where false positives cost millions and false negatives are existential.

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99.2%

Fraud detection accuracy

10x

Faster risk assessments

67%

Reduction in false positives

$2.4M avg

Cost savings on compliance

Trusted by teams at MatchWise, ServiceCore, QuantFi, Desson Abogados, Mexico Por el Clima, and others across the US and LATAM.

What we build

Anatomy of an AI workflow for Financial Services

Each ships in 8–12 weeks. Pick a workflow to see what goes in and what comes out.

Portfolio research & due diligence

Ingest 10-Ks, earnings transcripts, deal rooms, broker notes, and internal research into a RAG corpus analysts query in natural language. Every answer cites source page and paragraph — no synthesis without provenance.

4–8 hours per memo draft20–40 minutes with citations

Inputs we read

10-K, 10-Q, S-1 filings
Earnings call transcripts and broker notes
Internal IC memos and deal-room documents
Bloomberg, FactSet, S&P Capital IQ exports
Private-company data rooms

Outputs delivered

Draft IC memo with paragraph-level citations
Comparable-company tables sourced from filings
Risk-flag summary tied to source language
MNPI-segregated answer trace per query
Reviewer queue for ambiguous citations

Decide your path

Build, buy, or partner?

Three real options, each with different trade-offs on cost, control, and customization.

Hebbia · AlphaSense · Kensho

Vendor SaaS

Best for: Standard research and surveillance at large institutions

Data control: Vendor-controlled; data routed to vendor LLM
Customization: Low — preset taxonomies
Time to value: Weeks
Cost (3 yr): High recurring per-seat and per-query fees

Recommended

Clearframe partner build

Best for: Mid-size banks, asset managers, and LATAM fintechs with distinctive risk profiles

Data control: Your environment; no third-party training
Customization: High — tuned to your population, books, and channels
Time to value: 10–16 weeks
Cost (3 yr): Predictable; pays back in 6–12 months

DIY

In-house build

Best for: Tier-1 banks with mature ML platforms and model-risk teams

Data control: Full control
Customization: Full
Time to value: 12–24+ months
Cost (3 yr): Highest upfront, lowest recurring at scale

What is AI for financial services?

AI for financial services is the application of machine learning, large language models (LLMs), and graph analytics to the decisions that drive a financial institution's economics — fraud detection, credit risk, AML compliance, customer onboarding, and operational efficiency. Used correctly, it raises true-positive rates and lowers false-positive rates simultaneously, which is the only outcome that matters in this industry.

Financial services runs on volume — millions of transactions per day, thousands of credit decisions per hour, tens of thousands of compliance alerts per week. Rules-based systems break under that volume; analyst teams burn out reviewing 95% false positives; and customers churn when onboarding takes a week. We build AI systems that compress those decision loops from days to seconds without sacrificing the auditability your regulators require.

Glossary

Key terms on this page

RAG (Retrieval-Augmented Generation)

A pattern where an LLM answers questions using documents it retrieves from your firm's own corpus — filings, research, policies — with citations back to source.

LLM (Large Language Model)

A model trained on text that can read, summarize, draft, and reason over financial documents. In production we wrap LLMs in retrieval and validation layers; we don't let them answer ungrounded.

MNPI / MAR

Material non-public information and the EU Market Abuse Regulation. AI systems in capital markets must segregate MNPI from general research so the model never leaks restricted context into a public answer.

Embedded compliance

Compliance checks (disclosures, suitability, MNPI segregation, adverse-action language) wired into the model's response pipeline rather than bolted on as post-hoc review.

Agentic workflow

A workflow where the model plans multi-step tasks — pulling positions, computing exposures, drafting language, routing for review — under explicit human-in-the-loop checkpoints at every irreversible action.

How we work

What the engagement looks like

A typical first engagement runs 10 to 16 weeks and ships a single production-grade model or workflow — usually fraud, AML, credit, or a research copilot for one desk.

1–2 weeks

Step 1

Paid scoping sprint

Size the data, define success metrics (precision/recall at fixed thresholds, dollar impact, alert volume), and align with model-risk and compliance leads.

Data sizing memoSuccess metrics with thresholdsModel-risk alignment notes

6–10 weeks

Step 2

Build & shadow

Same senior engineers from kickoff to deploy. Validate against a holdout from your historical data, then run 2–4 weeks in shadow mode against the incumbent system.

Validated model with holdout reportShadow-mode comparison vs. incumbentModel card and validation pack

Week 10–16

Step 3

Production deploy

Graduate to production behind feature flags with the second line of defense signed off. Live monitoring, drift detection, and challenger framework ship with the build.

Feature-flag rolloutLive monitoring and drift detectionChallenger framework

We don't ship demos. Every deployment is measured against fraud loss reduction, false-positive rate, alert-to-SAR conversion, approval rate at fixed risk, and analyst hours saved.

How we handle your data

Financial AI lives or dies on auditability. We keep customer and market data inside your environment — no third-party model training, no leaked PII — with structured documentation on every model decision so second-line and external auditors can sample any decision and trace it to source.

What we do

Your data stays in your environment

No third-party model training

Per-decision audit logs with reason codes

MNPI segregation in retrieval layers

Model cards and validation packs delivered with every build

Architectures designed to meet

SOC 2

PCI DSS

GLBA

FFIEC guidance on model risk

SR 11-7 model risk management

NIST AI Risk Management Framework

We don't carry these certifications ourselves — your firm's compliance posture stays yours to claim.

Case study

How we did it for NovaPay Financial

Real-Time Fraud Detection System for Digital Banking Platform

99.2%

Fraud detection rate

73%

False positive reduction

$18.4M

Fraud losses prevented (annual)

Read the full case study

Frequently asked questions about AI for financial services

How is AI fraud detection different from rules-based systems?

Rules-based systems freeze the moment a fraud ring adapts; AI systems learn from new patterns continuously and weigh hundreds of signals at once. In production, our hybrid stacks (rules plus ML plus graph features) cut false positives by 60–70% versus rules alone while catching more true fraud.

Will AI models meet our model risk management (SR 11-7 / MRM) requirements?

Yes — we ship every production model with documented training data, validation reports, performance metrics, and an explainability layer (SHAP or counterfactuals) so your model risk team can sign off. We also support shadow-mode deployment and challenger models for ongoing validation.

Can AI explain why a customer was denied credit?

Required, not optional. Our credit models pair a high-performance learner (gradient boosting or neural) with an explanation layer that produces ECOA/FCRA-compliant adverse action reasons. Every decision is reproducible and auditable.

How do you keep customer financial data secure?

We deploy inside your VPC, use tokenization or field-level encryption for PII/PCI data, and route through endpoints that contractually exclude training. For the most sensitive workloads we run open-weights models on your own infrastructure so no data ever leaves the perimeter.

Is AI ready for algorithmic and quantitative trading?

For signal generation, execution optimization, and portfolio construction — yes, used by every major hedge fund. We build ML pipelines with rigorous backtesting, walk-forward validation, and risk overlays. For pure prediction of market direction, expectations should be modest; the alpha is in feature engineering and execution, not the model.

How long to deploy AML transaction monitoring?

A first production model on top of your existing transaction data typically goes live in 10–14 weeks, including model risk documentation. The bigger lift is integration with your case management system and tuning thresholds with the compliance team — we plan for that explicitly.

Does this work for LATAM banks and fintechs?

Yes — we deploy bilingual (Spanish/Portuguese/English) stacks and align to CNBV (Mexico), CMF (Chile), CVM (Brazil), and SBS (Peru) requirements alongside global frameworks like Basel III and FATF. Local payment rails (SPEI, PIX, CoDi) are first-class in our fraud and AML models.

Related services

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Most financial services teams we work with ship to production in 90 days.

Worth 30 minutes to see what that would look like for your firm? Book a call with one of our senior engineers — no sales handoff, no deck.

Book a 30-minute call