Finance & Accounting
Integrated Systems

Scalable data infrastructure, real-time ML detection, and automated agentic compliance for modern financial institutions.

1. Real-Time Payment Reconciliation

Complexity: HIGH

The Problem:

Financial teams spend days manually cross-referencing CSVs from Stripe, PayPal, and core ERPs to find mismatched records and adjust journal entries.

Business Outcome:

Reduced manual reconciliation from 3 days to under 10 minutes per month.

Customer Benefit:

Faster month-end closes, zero manual error rates, and crystal clear daily cash flow visibility.

graph LR A[Payment Gateways] --> B(Kafka Data Pipeline) B --> C{ML Anomaly Engine} C -->|Match| D(NetSuite ERP) C -->|Mismatch| E[Agentic Review]
graph LR A[Live Transactions] --> B(Real-time Feature Store) B --> C(ML Fraud Model) C -->|High Risk| D(Block & Alert) C -->|Low Risk| E(Approve)

2. Scalable Fraud Detection

Complexity: VERY HIGH

The Problem:

Legacy rule-based fraud detection triggers too many false positives, blocking legitimate enterprise transactions while letting sophisticated fraud slip through.

Business Outcome:

40% drop in chargeback rates with a <10ms inference latency constraint.

Customer Benefit:

Protects bottom-line revenue and significantly improves the checkout experience for legitimate, high-value customers.

3. Dynamic Regulatory Reporting

Complexity: MEDIUM

The Problem:

Generating compliance reports for SEC/FINRA requires tedious SQL queries across fragmented data silos, risking compliance fines if delayed.

Business Outcome:

100% automated regulatory reporting generated instantly from a centralized semantic layer.

Customer Benefit:

Peace of mind for the Chief Risk Officer and zero exposure to non-compliance penalties.

graph LR A[Cloud Data Lakehouse] --> B(Semantic Data Layer) B --> C(Compliance Engine) C --> D(Dashboard / Export)

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