AI + Finance
Around the core flows of banks, brokers, insurers and trusts — multi-agent, federated learning and knowledge graphs braided into one rope. Risk, trading, marketing, audit and compliance collection — every step measurable, traceable and supervisable.
Start with the business problem
Financial services
Reporting, compliance documentation and service absorb specialist time.
Anonymous scenario illustration · not verified client results
Before
Manual collation and source checking
Pilot target
Source-linked drafts with specialist review
Suggested acceptance metrics: Report hours, citation accuracy, cost per report
Legacy figures and anonymous cases on this page lack complete source evidence. They are scenario references, not verified results or return promises; verify with client permission, baselines and test reports.
Key Capabilities
Four key capabilities required to put this solution into production.
Smart Risk & AML
GNN graph mining plus multi-source fusion lifts suspicious-transaction recall and response time.
Bond-Trading Agent
Quote parsing, matching, pricing and compliance — fully automated, 10× trader productivity.
Cross-Institution Federation
Federated risk and anti-fraud models keep data in domain and meet financial regulations.
Smart Audit & Collection
Compliance rule engine plus LLM evidence collection cuts audit cycles and labor cost.
Representative Scenarios
Trust risk control · bond-trading agents · bank precision marketing · insurance smart underwriting · cross-institution AML modeling.
Download Solution Playbook
Generate a printable white paper for this module
Opens a playbook page with key capabilities, scenarios, outcomes, specs and case digest — use the browser's Save as PDF to distribute.
Case Studies
Filter anonymous scenario illustrations by industry, scenario and scale. Metrics and inferred customer, duration, stack and deployment metadata are unverified, not established customer outcomes.
Finance
5 casesIllustration · metrics unverified
Top-trust smart risk control
GNN + federated joint modeling sharply lifts suspicious-path recall.
- Customer
- Top-tier Banks / brokers / insurers
- Duration
- 12–20 weeks
- Tech stack
- Graph neural networks · Federated learning · Speech / ASR-TTS · Predictive modeling
- Deployment
- Private deployment
Illustration · metrics unverified
Broker bond-trading agent
Quote parsing, pricing, matching and compliance — fully automated.
- Customer
- Top-tier Banks / brokers / insurers
- Duration
- 8–12 weeks
- Tech stack
- Multi-agent
- Deployment
- Hybrid cloud
Illustration · metrics unverified
Insurance smart underwriting
Multimodal fusion of underwriting docs lowers manual review.
- Customer
- Top-tier Banks / brokers / insurers
- Duration
- 8–12 weeks
- Tech stack
- Multimodal backbone
- Deployment
- Hybrid cloud
Illustration · metrics unverified
Bank corporate-credit smart assistant
Financials, statements, news and industry research integrated; credit recommendations one-click.
- Customer
- Top-tier Banks / brokers / insurers
- Duration
- 8–12 weeks
- Tech stack
- Multimodal backbone
- Deployment
- Private deployment
Illustration · metrics unverified
Third-party payments anti-fraud
Transaction graphs + behavior sequences + device fingerprints jointly modeled.
- Customer
- Top-tier Banks / brokers / insurers
- Duration
- 8–12 weeks
- Tech stack
- Graph neural networks
- Deployment
- Hybrid cloud
Specs & Fit
Best for | Banks / brokers / insurers / trusts / payments |
Core tech | GNN + FL + multi-agent |
Deployment | Private / regulatory sandbox / consortium |
Compliance | MLPS-3 / banking / personal-finance |
SLA | 99.95% availability |
FAQ
How is data compliance ensured?
Cross-institution federated modeling keeps data in domain — meets CBIRC and sandbox requirements.
Time to launch?
Single scenario: 6–8 weeks; platform projects: 3–6 months.
Is cost predictable?
Lightweight backbone + Skills reuse — average per-inference cost down 40%+.
Quantified Outcomes
We only ship solutions that deliver ROI — here are the measurable metrics after enterprise rollout.
AML response +25%, 10× bond-trading productivity, 60% shorter audit cycles, 100% compliance on cross-institution modeling.
Defined scope. Accountable delivery.
Staged pricing & acceptance
Discovery, pilot and rollout have separate scope and fees. Contracts define baselines, evaluation samples, acceptance metrics and responsibility. Results are not unconditionally guaranteed.
Customer business data stays off-chain
Business records and personal data are not written to a blockchain. Confirm deployment region, model providers, access, retention and deletion in writing before work begins.
Clear contracting & delivery responsibility
Before signing, obtain and verify the legal entity, registration, jurisdiction, delivery owner and dispute terms. DAO collaboration does not replace contractual responsibility.