Ruby Shanker Krishnam is a practitioner at the intersection of finance, cloud engineering, and applied AI, the kind of leader who translates noisy finance operations into predictable, auditable, and highly automated systems. By combining cloud-native architectures with AI-driven automation, Ruby focuses on four high-impact areas for finance teams: transforming the general ledger, optimizing accounts receivable (AR), automating accounts payable (AP), and streamlining tax management. The result is faster close cycles, healthier cash flow, and materially lower operational risk.
Large finance, technology, logistics, and accounting organizations run on brittle spreadsheets, manual reconciliations, and point-to-point integrations. Specific pain points include:
- Slow, error-prone month-end close because the general ledger (GL) is fragmented.
- Long Days Sales Outstanding (DSO) caused by unclear invoicing, poor collections prioritization, and delayed dispute resolution.
- High AP processing costs and supplier friction from manual invoice capture, approval bottlenecks, and duplicate payments.
- Tax compliance headaches driven by siloed data, inconsistent tax treatment, and late or incorrect reporting.
Ruby’s approach addresses these problems end-to-end with a combination of cloud consolidation, intelligent automation, and data-first governance.
A Practical Architecture: Cloud Foundations + AI Services
Ruby typically starts with a modern, layered architecture:
Unified Data Layer in the Cloud
A central data lake/warehouse (managed services on AWS/Azure/GCP) aggregates transactional GL entries, subledger records, billing, payments, and tax codes. This single source of truth enables consistent reporting and auditability.
Microservices & API-First Integrations
Rather than brittle point-to-point connectors, Ruby designs API-led integration (MuleSoft/Cloud integration patterns or cloud-native event streaming) so source systems can feed and consume ledger data in real time.
AI/ML Services for Finance Workflows
- Natural language processing (NLP) for invoice OCR, statement understanding, and unstructured-comment extraction.
- Machine learning models for customer payment behavior, credit-risk scoring, and predictive collections.
- Anomaly detection for reconciliation mismatches, duplicate invoices, and unusual ledger postings.
Automation and Orchestration
RPA and workflow engines automate approvals, exception routing, and posting to the GL. MLOps ensures ML models are versioned and monitored in production.
Security, Compliance, and Observability
End-to-end encryption, role-based access controls, immutable audit trails, and automated evidence capture for tax and regulatory audits.
How Ruby Transforms the General Ledger
Ruby treats the GL as a real-time, reconciled data product rather than a static monthly report.
- Automated ingestion & mapping. Source system transactions are mapped to a canonical chart of accounts automatically, using rule-based and ML-assisted mapping that learns exceptions over time.
- Continuous reconciliation. Bank, subledger, and GL reconciliation become near-real-time with automated matching and flagged exceptions.
- Faster close. By eliminating manual reconciliation and enabling automated journal entry suggestions, month-end close time shortens and close quality improves—freeing finance to focus on analysis rather than data-fixing.
Optimizing AR with Intelligence
For accounts receivable, Ruby focuses on cash predictability and dispute resolution.
- Predictive collections. ML models predict which customers are likely to pay late and how much, allowing prioritized outreach and tailored payment terms.
- Smart dunning and self-service. Automated, personalized communications and secure self-serve payment portals reduce friction and lower DSO.
- Dispute automation. NLP classifies disputes from emails and remittance notes; bots gather supporting docs and create case files, accelerating resolution.
Automating AP End-to-End
AP is an ideal target for automation and cost reduction.
- Invoice ingestion and validation. OCR + NLP extracts invoice data; ML validates vendor details and flags suspicious invoices or potential duplicates.
- Auto-matching and exception handling. Three-way matching (PO, receipt, invoice) is automated, and only exceptions are routed to humans.
- Supplier experience & early-pay discounts. Automated workflows can enable dynamic discounting and faster supplier onboarding, improving relationships and capturing cost savings.
Outcomes and Business Impact
When properly designed and executed, projects of this type typically deliver:
- Shorter month-end close cycles and fewer manual journal adjustments.
- Lower DSO via prioritized collections and self-service payments.
- Reduced AP processing cost per invoice and fewer duplicate payments.
- Faster, more accurate tax filings and reduced audit time.
Ruby’s implementations emphasize measurable KPIs: reduction in close time, percent of invoices auto-processed, DSO improvement, cost-per-invoice, and audit-cycle time.
The Near Future
Ruby sees the next wave as finance teams becoming proactively predictive partners to the business: cash forecasting that links to procurement, tax-aware pricing engines, and AR/AP automation that integrates with customer and supplier experience platforms. With AI and cloud as enablers, finance transformation using ERP solutions can move from bookkeeping to business stewardship.
The post Ruby Shanker Krishnam: Modernizing Finance with AI + Cloud — from General Ledger to Tax appeared first on International Business Times.
