AI Automation for Finance Analytics AI / Machine Learning
Budget: €12 – €18 EUR
I want to replace several manual reporting routines with an end-to-end AI workflow that ingests data from our internal finance databases and live web sources, then produces clear, timely analytics for management. Reporting and analytics are the sole focus—no transaction execution—so the system must excel at pulling, cleaning, and interpreting numbers rather than booking them.
We also want to compare legal documents vs term sheets and excel spreadsheets
Data sources
• Company databases (SQL, flat files, Excel exports)
- Dropbox all our files are in drop box
• Extensive web scraping for competitor benchmarks and investment-market signals
If you have ideas for safely adding external financial APIs later, let me know, but the two feeds above are mandatory.
- There will
Tech freedom
I haven’t settled on Claude, OpenAI, or any other foundation model yet. I’m open to your guidance on which stack best balances accuracy, cost, and future scalability, provided everything runs on a maintainable cloud or container platform.
Core deliverables
• Architecture and tooling proposal with rationale
• Data-ingestion pipelines that screen-scrape at scale and connect to our databases
• Machine-learning layer that turns raw inputs into trends, forecasts, and anomaly flags
• Automated report generator (dashboards, PDFs, or both) running on a schedule
• Deployment scripts and hand-off documentation
Acceptance criteria
1. Daily analytics complete in under 30 minutes without manual intervention.
2. Forecast error ≤5 % against historical back-tests.
3. Codebase fully containerised or otherwise reproducible on our AWS account.
Proven work in finance automation or similar regulated domains will weigh heavily in the selection. When you reply, please share one or two live examples of AI systems you built that involved both database integration and large-scale scraping, noting the model family you chose and why.
We also want to compare legal documents vs term sheets and excel spreadsheets
Data sources
• Company databases (SQL, flat files, Excel exports)
- Dropbox all our files are in drop box
• Extensive web scraping for competitor benchmarks and investment-market signals
If you have ideas for safely adding external financial APIs later, let me know, but the two feeds above are mandatory.
- There will
Tech freedom
I haven’t settled on Claude, OpenAI, or any other foundation model yet. I’m open to your guidance on which stack best balances accuracy, cost, and future scalability, provided everything runs on a maintainable cloud or container platform.
Core deliverables
• Architecture and tooling proposal with rationale
• Data-ingestion pipelines that screen-scrape at scale and connect to our databases
• Machine-learning layer that turns raw inputs into trends, forecasts, and anomaly flags
• Automated report generator (dashboards, PDFs, or both) running on a schedule
• Deployment scripts and hand-off documentation
Acceptance criteria
1. Daily analytics complete in under 30 minutes without manual intervention.
2. Forecast error ≤5 % against historical back-tests.
3. Codebase fully containerised or otherwise reproducible on our AWS account.
Proven work in finance automation or similar regulated domains will weigh heavily in the selection. When you reply, please share one or two live examples of AI systems you built that involved both database integration and large-scale scraping, noting the model family you chose and why.