Case Study — AstraFi
Automating Treasury Operations for a Fintech With 40+ Banking Relationships
From 2-day cash forecasting in spreadsheets to real-time treasury intelligence
Industry
Fintech / Payments
Timeline
14 weeks
Team
5 engineers
Tech
Open Banking + PostgreSQL + React
The Challenge
A high-growth fintech processing $800M monthly was managing liquidity across 40+ banking relationships through spreadsheets. Cash forecasting took 2 days, required 8 finance team members, and was frequently wrong by 15-30%. Idle cash balances were being under-deployed because no one had a real-time view of aggregate positions.
Our Approach
How We Solved It
Banking API Integration Layer
Built a unified banking integration layer connecting to 40+ banking partners via Open Banking APIs, direct bank data feeds, and SFTP-based statement imports — normalizing all formats into a canonical account structure.
Real-Time Cash Position Engine
Intraday cash positions update as transactions settle across all 40+ accounts, giving the treasury team a live aggregate view of available liquidity within 30 seconds of any transaction.
ML Cash Flow Forecasting
Trained an ensemble forecasting model on 36 months of transaction history, external payment schedules, and seasonal patterns to predict 30, 60, and 90-day cash flows with 94% accuracy.
Sweep & Deployment Automation
Automated overnight cash sweeps that move idle balances above target thresholds into high-yield instruments, executing transfers programmatically within policy-defined parameters without manual intervention.
Engineering Process
How We Built It
Idempotent Transaction Processing
All banking transaction ingestion is idempotent — duplicate transactions from bank APIs are detected and deduplicated using composite transaction fingerprints before affecting position calculations.
Policy Engine for Transfer Authorization
Automated sweep transfers execute only within a configurable policy engine (per-account limits, counterparty restrictions, time windows) with all exceptions requiring manual treasury approval.
Reconciliation-First Architecture
Every position calculation is reconcilable back to raw bank statements line-by-line. The treasury team can always verify system positions against bank statements without relying on the system as the single source of truth.
Architecture Decisions
Key Technical Choices
Hub-and-Spoke Over Direct P2P Integration
A central integration hub normalizes all 40+ bank feeds into one canonical format, rather than custom integrations per bank — reducing integration maintenance from O(n) to O(1).
Probabilistic Forecasting
Cash flow forecasts include confidence intervals at the 80th and 95th percentile, enabling the treasury team to manage liquidity buffers appropriate to their risk tolerance rather than a single point estimate.
Position Snapshots for Regulatory Reporting
Daily end-of-day position snapshots are archived immutably for regulatory reporting (daily liquidity coverage ratio calculations) without requiring the live system to retain historical state.
Results
What We Delivered
Solution Blueprint
How It All Fits Together
- Open Banking APIs
- Direct bank data feeds
- SFTP statement parsing
- Real-time position engine
- ML cash flow forecasting
- Idle cash identification
- Policy-governed sweep automation
- Reconciliation engine
- Regulatory reporting
Lessons Learned
What We Improved
Bank API Reliability Is Highly Variable
Of the 40+ banking integrations, 8 banks had APIs with >1% error rates. We built automatic fallback to SFTP statement parsing for unreliable bank APIs to maintain position accuracy.
Forecast Accuracy Requires Account-Level Models
A single aggregate forecasting model was 20% less accurate than per-account models. The payment profiles, seasonality, and volatility of each banking relationship are distinct enough to warrant separate models.
The First Week Was Reconciliation
Onboarding required reconciling 3 years of bank statements to establish a verified starting position. We scoped this into the project timeline — it's not optional if the treasury team is to trust system positions.
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