Biztras implemented an AI-powered AR Aging Dashboard to provide real-time visibility into receivables, payment delays, and collections performance.
Biztras delivered a Financial Analytics solutionproviding end-to-end visibility into Accounts Receivable aging. Built on SAPfinancial data using a medallion architecture, the solution monitors overduebalances, payment delays, and credit risk, and enriches data with AI/ML-readyfeatures for payment behavior analysis — improving collections performance,cash-flow forecasting, and working-capital optimization.
• Limited visibility into AR aging,overdue balances, and payment delays.
• Difficulty identifying high-riskand chronically overdue customers.
• Inconsistent posting dates, duedates, and document statuses across SAP financial data.
• Weak cash-flow forecasting due tounreliable receivables data.
• No predictive capability toprioritize collections effort.
• Ingested raw AR open items,customer master, and accounting document data from SAP into a Bronze layer,retaining complete financial transaction history.
• Cleansed and standardized postingdates, due dates, document statuses, customer mappings, and payment attributesin the Silver layer to construct accurate AR aging views.
• Curated enriched Gold layerdatasets with business KPIs and AI/ML-ready features for risk and paymentbehavior analysis.
• Delivered key metrics includingexcess days beyond due date and cost savings.
• Enabled AR aging and collectionsdashboards, high-risk customer identification, and predictive payment-delayinsights.
• End-to-end visibility intoreceivables aging and collections performance.
• Early identification of high-riskand overdue customers for targeted follow-up.
• Improved cash-flow forecasting andworking-capital optimization.
• Prioritized collections effortthrough predictive payment delay risk scoring.
• Measurable cost savings throughreduced payment delays and better credit risk management.
• Limited visibility into AR aging,overdue balances, and payment delays.
• Difficulty identifying high-riskand chronically overdue customers.
• Inconsistent posting dates, duedates, and document statuses across SAP financial data.
• Weak cash-flow forecasting due tounreliable receivables data.
• No predictive capability toprioritize collections effort.
• Ingested raw AR open items,customer master, and accounting document data from SAP into a Bronze layer,retaining complete financial transaction history.
• Cleansed and standardized postingdates, due dates, document statuses, customer mappings, and payment attributesin the Silver layer to construct accurate AR aging views.
• Curated enriched Gold layerdatasets with business KPIs and AI/ML-ready features for risk and paymentbehavior analysis.
• Delivered key metrics includingexcess days beyond due date and cost savings.
• Enabled AR aging and collectionsdashboards, high-risk customer identification, and predictive payment-delayinsights.
• End-to-end visibility intoreceivables aging and collections performance.
• Early identification of high-riskand overdue customers for targeted follow-up.
• Improved cash-flow forecasting andworking-capital optimization.
• Prioritized collections effortthrough predictive payment delay risk scoring.
• Measurable cost savings throughreduced payment delays and better credit risk management.
The solution improved cash-flow visibility, strengthened collections strategies, and enabled smarter, data-driven financial decision-making.
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