Skip to content
LeapWise
Menu
中文
All solutions

Finance

Designed for financial compliance: isolated permissions, end-to-end audit, figures checked by code. AI advises but never decides — it doesn't touch the core lending system, doesn't trade, and doesn't replace sign-off.

thresholdBreach → human reviewAudit log✓Call logged✓Figures checked!Alert sent✓Human review✓Case archived

Post-loan monitoring and review

Deliverable

Turns unusual changes in the loan book into explainable, assignable and reviewable check tasks.

The problem
Post-loan data is scattered, anomalies surface late, and task assignment and review are chased by hand.
Who it's for
Bank risk management and post-loan teams
Delivery
Private deployment, delivered in WeCom, Feishu, DingTalk or on the web
What it does
  • Reads new data on schedule; deterministic rules flag arrears, metric changes, missing documents and external events
  • De-duplicated and tiered by borrower and event, dispatched after risk staff confirm
  • Relationship managers respond; risk managers review and close
  • Never changes the lending system or acts automatically

Bond credit-event monitoring

Deliverable

Maps issuer credit events to bonds and portfolio holdings, producing time-stamped, evidence-backed risk briefs.

The problem
Credit news is noisy, matching it to issuers is hard, and portfolio impact is worked out by hand.
Who it's for
Securities firms, funds, bank asset management and investment teams
Delivery
Private deployment, delivered in WeCom, Feishu, DingTalk or on the web
What it does
  • Master data for issuers, bonds, guarantees and related parties
  • News and filings matched to entities; ambiguous matches go to a person
  • Holdings impact mapping and risk briefs
  • No rebalancing, no trading, no investment advice

Research report consistency pre-check

Deliverable

Before formal QC, finds verifiable issues in a research report's numbers, formulas, citations and disclosures.

The problem
Figures, formulas and citations are checked line by line by hand, and QC is overloaded.
Who it's for
Brokerage research, investment research and QC teams
Delivery
Private deployment, delivered in WeCom, Feishu, DingTalk or on the web
What it does
  • Submitted versions locked; every version retained
  • Deterministic checks of figures and formulas, with issues pinned in place
  • Grouped as hard errors, items to verify and wording suggestions
  • Analysts resolve each item; QC reviews

Evidence-chain agent platform for finance

Deliverable

An internal agent workspace for financial institutions: document parsing, Q&A, review and export, all recorded.

The problem
Finance demands strict permissions, isolation and audit; general AI tools don't pass compliance.
Who it's for
Banks, securities firms, insurers and other financial institutions
Delivery
Private deployment, delivered in WeCom, Feishu, DingTalk or on the web
What it does
  • Identity and permissions, project isolation, hash-chained audit
  • PDF and Excel parsing into the knowledge base
  • Orchestrated Q&A, review and export
  • Works with Feishu, WeCom and DingTalk

Financial analysis agent

Deliverable

Start a financial analysis with one sentence: data collection, cleaning, health scoring, cash-flow simulation and peer benchmarking, ending in a report.

The problem
Financial analysis takes too long, and definitions and conclusions are hard to verify.
Who it's for
Corporate finance, group controlling, corporate banking
Delivery
Private deployment, delivered in WeCom, Feishu, DingTalk or on the web
What it does
  • Multi-agent pipeline: collect, clean, score, simulate, benchmark, recommend
  • Every figure comes from a deterministic engine with an evidence ID
  • Reports in Word, PDF and Excel, distributed automatically
  • Same input, same result; deployed privately

Start with a half-day workshop

Your business owner and IT lead join us to pin down 1–3 candidate use cases and their success metrics.

Book a use-case workshop

  1. 1

    Use-case workshop

    Half a day, on site or online

    • Business owner and IT lead attend
    • Map 1–3 candidate use cases and the state of the data
    • Output: use-case list and draft success metrics

  2. 2

    PoC proposal

    Within a week of the workshop

    • Data and system boundaries, evaluation-set design
    • Milestones, commitments on both sides, acceptance bar
    • Output: a PoC proposal ready to sign

  3. 3

    Run the PoC

    2–4 weeks

    • Your real material, in a private or demo environment
    • Weekly check-ins with visible metrics
    • Output: evaluation report and launch plan