FiClaw / Bank economics, financial markets and wealth research departments
AI workflows for bank research departments
Economics, financial markets and wealth research teams have different research objects and output uses. Agree acceptance criteria separately for macro or sector views, testable equity strategies and internal discussion material.
Published by FiClaw 产品团队 · Published · Updated
阅读中文版Where FiClaw fits
Bank research departments can evaluate FiClaw by defining a task's data scope, availability timing, scenario assumptions and review responsibilities. The public product supports strategy specifications, Python, A-share historical backtests and diagnosis. Macro data, bond research and internal research systems require assessment. A data-vintage and handoff checklist can guide the pilot; A-share backtesting does not establish a delivered bank-wide multi-asset platform.
Start with a concrete research task
01
Define macro and sector data conventions
- Input
- Supply authorized samples with units, frequency, observation periods, release dates and versions.
- Research output / evaluation scope
- Pilot a way to organize the data dictionary, vintage differences and open questions.
- Human review
- Distinguish then-available values from later revisions. Macro sources and vintage interfaces need assessment.
02
Record scenarios and assumptions
- Input
- Researchers specify a baseline, changed scenarios, drivers and fixed conditions.
- Research output / evaluation scope
- Agree calculation, evidence and review formats; implementable A-share hypotheses can use the current workflow.
- Human review
- A scenario is an assumption, not an established forecast. Bond valuation and cross-asset models require separate assessment.
03
Hand over internal research material
- Input
- Collect source snapshots, transformations, charts, conclusions and unresolved questions.
- Research output / evaluation scope
- Use a checklist to retain sources, versions, assumptions and human interpretation.
- Human review
- Internal access, archiving and approvals need scoping. Credit, pricing, balance-sheet and trading decisions remain with authorized staff.
A pilot with acceptance criteria
- 01
Define the department and use
Identify economics, financial markets or wealth research. Choose one public or authorized redacted sample task.
- 02
Freeze the data snapshot
Record sources, permission, retrieval time, observation period, version, units and transformations for the agreed comparison.
- 03
Review assumptions and changes
Compare calculations with manual results on the same snapshot. Separate revisions, method changes and scenario changes.
- 04
Assess subsequent integration
Use acceptance evidence to scope macro or bond sources, internal interfaces, permissions, deployment and operations.
Confirm capabilities and delivery scope
- Current public capabilities
- FiClaw's public capabilities focus on quant strategy research: Desktop, cloud services, specifications, Python, A-share historical backtests, diagnosis and parameter comparison. Bank teams can first test the portion relevant to their task.
- Separate assessment required
- Macro databases, historical vintage stores, live bond prices and valuation, econometric forecasts, multi-asset analysis and internal bank systems require separate data, interface and implementation scoping. Private deployment also needs assessment.
- Decisions and accountability
- Bank staff review research outputs. FiClaw does not exercise credit approval, suitability, asset-liability management, product pricing, treasury execution or regulatory reporting responsibilities.
Procurement and pilot questions
Is bank research the same as a bank risk system?
This page addresses support for macro, sector, financial markets and wealth research. Credit approval, risk measurement, asset-liability management and regulatory reporting have separate data, model and process requirements; a research workstation does not replace them.
Are macro databases and bond valuation included?
Do not assume so. Public backtesting primarily covers A-shares. Macro series, historical vintages, live bond data, valuation and forecasting models require separate authorization and interface assessment.
Why record data vintages in a bank research pilot?
A value for the same observation period may later be revised. Today's download may differ from what was available when research was performed. Versions, release timing and retrieval timestamps help explain changes in charts, experiments and conclusions.
Does an initial pilot need customer or account data?
Begin with public or authorized redacted samples and define the question and intended output. Before connecting customer, account or internal business data, confirm permissions, data flows, service dependencies and processing.
Further reading
Data vintages and scenario review in bank AI research ↗
Separate observation periods, availability timing, revisions and scenario changes.
Financial agent data boundaries ↗
Define authorization and processing before connecting internal data.
Securities research workflows ↗
Compare experiment, chart and report evidence review for securities firms.
Bring one research question
Describe the team roles, task, available material and expected output to start a scope discussion. Public or redacted samples are enough for an initial conversation.