Custom AI/ML solutions for financial services industry
AI in financial services for banks, asset managers, fintechs, and credit unions. We build custom AI and ML for fraud detection, credit modeling, compliance automation, and personalized customer experiences, documented for regulator review.
AI in banking and asset management, from fraud to model risk
Six measurable outcomes across fraud, credit, markets and compliance. Your data condition, core-system integrations and model-review process set the schedule.
Real-Time Fraud Scoring
Per-transaction ML scoring designed for sub-200ms latency, tuned to cut false positives so analysts work the alerts that matter.
AML & KYC Automation
Entity resolution, sanctions screening, and SAR/STR narrative-drafting LLMs. With generative AI in banking compliance, the model drafts each narrative and an analyst approves it before filing. Regulator-exam-ready documentation by default.
Credit Decisioning
Alternative-data credit models with explainable scorecards and adverse-action reasoning built to Regulation B as amended by the CFPB final rule effective July 21, 2026.
Markets & Asset Allocation
Signal generation, regime detection, and portfolio optimization, built per strategy and deployed in your own environment.
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 keep research data inside their own environment.
Model Risk Management
Documentation, backtesting, sensitivity testing, and validation packs built to SR 26-2, the interagency model risk guidance that replaced SR 11-7 in April 2026.
Capabilities across the financial services value chain
Lending & Credit Risk
- Alternative-data credit models with explainable scorecards
- Default-probability (PD) and LGD models per segment
- Application-fraud detection at origination
- Early-warning indicators and watchlist scoring
Fraud, AML & Compliance
- Real-time transaction-fraud scoring at sub-200ms latency
- Entity resolution and beneficial-ownership graphs
- AML narrative drafting and SAR/STR support LLMs
- Sanctions screening with reduced false-positive rates
Markets, Trading & Asset Management
- Alpha-signal generation across alternative datasets
- Regime detection and volatility forecasting
- Portfolio optimization under regulatory constraints
- Investment-research and M&A-diligence LLMs over private + public corpora
Customer Experience & Operations
- Voice and chat LLMs for contact-center automation
- Personalized offer and pricing engines
- Complaint-classification and CFPB-pattern detection
- Document-extraction and lending-ops automation
How ML fraud scoring cuts false positives and frees analyst time
We build gradient-boosted fraud scoring pipelines trained on your own dispositioned alerts, enriched with device, geolocation, and behavioral-biometric features. The model ranks every alert so analysts start with the highest-risk cases, and the same scoring engine can feed real-time decline decisions on card-not-present transactions. Each pipeline ships with a documented MRM package built for your model risk team and your regulator exams.
Scope your fraud model in a strategy session →Speak with a financial services AI expert
A 30-minute scoping call. We come prepared with your portfolio mix, your transaction volumes, and a directional read on which workflows are worth automating first.
Book a Strategy Session- 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
AI Agents for Banks and Financial Firms
AI agents in banking and finance prepare the case work behind alerts, reviews and loan files, and your analysts make every decision.
Agentic AI in finance and banking
Agentic AI in finance and banking assigns an AI agent to a case such as an AML alert or a KYC refresh. The agent gathers the customer and transaction records, runs sanctions and adverse-media searches, assembles the case file and drafts the narrative. An analyst reviews the evidence, decides the case and files any report. For broker-dealers, FINRA's AI guidance in its 2026 Annual Regulatory Oversight Report, published December 9, 2025, suggests firms consider where to place human-in-the-loop oversight and how to track agent actions and decisions. Our agentic AI development team builds each agent inside your environment with a log of every step, ready for that review.
The same review pattern runs in the finance back office. Our finance AI agents reconcile accounts, prepare close tasks and chase receivables on your ERP. For payables, AP automation agents capture invoices, match them to purchase orders and route approvals. Both run inside your environment with read-only access by default, and a controller or AP lead approves each write before it reaches the ledger.
Fintech AI and banking chatbots with private data handling
A customer chatbot answers balance, card, payment and loan-status questions from your core and card systems. It hands disputes, complaints and any question that calls for advice to a staff member with a summary of the conversation. Fintech lenders and payment apps use the same pattern for support, and AI for credit unions follows it too, since member account and loan data needs the same controls. Customer names, account numbers and transaction details stay in your environment. The model runs self-hosted in your cloud account, or a PII redaction layer masks those fields before a prompt reaches a hosted model and restores them in the reply. An AI chatbot for banks built this way confirms the customer's identity before it shares any account detail, and it logs each conversation and each masked field for your compliance team.
Frequently Asked Questions
Every model ships with a full MRM package built to the revised 2026 interagency model risk management guidance (SR 26-2 / OCC Bulletin 2026-13, which rescinded SR 11-7 and OCC Bulletin 2011-12 in April 2026): documentation, sensitivity tests, backtesting against holdout periods, monitoring dashboards, and sign-off-ready validation packs. Each package is built for CCAR/DFAST-style scrutiny, state regulator review, and CFPB fair-lending examinations.
Yes. We build 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, Feedzai, Visa 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.
Every credit model ships with reason codes that map to the specific factors driving each decision, so adverse-action notices cite what actually moved the score rather than a generic proxy. The CFPB's final rule amending Regulation B, issued April 22, 2026 and effective July 21, 2026, narrows the Bureau's reliance on disparate-impact theories untethered to demonstrable causation, which raises the value of models whose factor attribution you can show line by line. We deliver the scorecard documentation, the fair-lending testing results, and the monitoring your compliance team presents at exam.
Yes. The underlying ML platform (feature store, model registry, monitoring, governance, MRM workflow) is reusable across all three, so AI for banks and AI in wealth management run under the same model governance. Domain models differ by line: retail focuses on fraud and credit, commercial on entity-resolution and trade-finance ML, and wealth on signal generation. Most universal banks running their own platform run one shared stack with multiple model families.
Explore AI/ML solutions for financial services
Air-Gapped AI for Regulated Industries
Air-gapped AI and offline LLMs on your own GPUs: Onyx, LibreChat and vLLM on an isolated network with no outbound connections, for regulated industries.
Learn more →Private AI for Business
Private AI for business on infrastructure you control: on-premise AI, private LLMs, chat, search and RAG deployed on your servers or in your cloud tenant.
Learn more →Private ChatGPT for Business
Private ChatGPT for your company: LibreChat or Open WebUI in your tenant with BYO-LLM (OpenAI, Anthropic or self-hosted Llama), Slack, RAG and audit logs.
Learn more →Private RAG
A private RAG chatbot for your documents: tuned hybrid retrieval, BYO embeddings and LLM, cited answers and per-corpus access control, on-prem or air-gapped.
Learn more →Secure On-Premise AI Compliance Software & Regulatory Monitoring
Compliance AI for banks, insurers and health systems: secure on-premise AI for regulatory monitoring, with evidence and audit trails in your own tenant.
Learn more →Self-Hosted AI for Business
Self-hosted AI for business: LibreChat, Onyx and vLLM deployed in your VPC, on-premise or air-gapped, with BYO-LLM, SSO and audit logs your team controls.
Learn more →We Build for 20 Industries
Ready to talk financial services AI?
Start with a 30-minute strategy session. We come prepared with a directional read on your operation and a scoped proposal.