Collections Automation at a Global Technology Giant — 95% Efficiency Gain, 86% TAT Reduction, 73% Value-Add Redirection
Workflow Automator + Anomaly Detector + Workflow Planner deploying an analytics-engine that predicts deduction-codes and categorises transactions for the collections workflow.
MindMap OS · installed inside the customer's perimeter by forward deployed engineers · Managed Cloud · billed on the result
The challenge
The client — a global technology giant with a substantial AR-collections operation across its enterprise-customer base — was running a collections workflow that the head of finance described as structurally tedious. The mechanical workflow required coordination across multiple departments with constant back-and-forth of the information. The collections-team was spending hours understanding the reasons for specific deductions in the cash-applications process, including checking with the vendor, the vendor-master-data, the sales-team and other stakeholders — resulting in hours wasted in completing one transaction.
The structural concerns were specific. The per-transaction processing-time was substantial (7 days on average including the follow-ups with customers and relevant departments); the per-transaction coordination-burden across the multiple departments was creating per-transaction inefficiencies; and the collections-team capacity was structurally absorbed by the per-transaction orchestration work rather than the strategic-collections-management work.
The CFO had aligned on the objective: substantially reduce the per-transaction processing-time, eliminate the per-transaction coordination-burden through structured automation-and-analytics, and redirect the collections-team capacity to the strategic-collections-management work that the per-transaction orchestration had been crowding out.
The approach
MindMap deployed a collections-automation platform composed of Workflow Automator (Wa) for the cross-system workflow execution, Anomaly Detector (Ad) for the per-transaction deduction-code prediction and categorisation, Workflow Planner (Wp) for the per-transaction lifecycle orchestration, and FP&A Forecaster (Ff) for the analytics-and-management-reporting layer.
Phase one was the workflow-mapping-and-process-improvement work. We worked with the accounts-team to not only identify and map out the workflow but also improve on the existing process by deploying an analytics-engine that would help predict the code used for specific transactions. The workflow-mapping identified the structural-bottlenecks in the per-transaction coordination-and-cross-department-engagement pattern.
Phase two was the deduction-code-prediction analytics-engine. The system categorises the transactions based on a lot of pre-fed data which is used for further analytics before crunching the numbers in SAP. The analytics-engine uses the per-transaction characteristics and the historical-transaction-pattern data to predict the appropriate deduction-code for the per-transaction handling.
Phase three was the per-transaction lifecycle automation. Workflow Automator handles the per-transaction execution across the multiple departmental-systems with the structured per-transaction lifecycle-tracking. The execution handles the previously-manual coordination work through the structured-automated workflow.
Phase four was the management-reporting-and-analytics layer. FP&A Insights provides the per-day-and-per-transaction collections-performance dashboards with the per-customer-and-per-segment recovery-pattern visibility. The analytics support the strategic-collections-management decision-making.
The pre-built building blocks
Rather than commission a ground-up build, the engagement leaned on MindMap's pre-built accelerator library — production-tested components that compress what would otherwise be a six-to-nine-month build into weeks.
Workflow Automator →
24/7 cross-system per-transaction execution
Anomaly Detector →
Deduction-code-prediction analytics-engine
Workflow Planner →
Per-transaction lifecycle orchestration with parallel engagement
FP&A Forecaster
Collections-performance dashboards and analytics
The architecture
The platform runs on the technology giant's managed cloud environment with appropriate financial-data-handling controls. The integration spans the AR-system, the SAP-financial-platform, the vendor-master-data system, the sales-team CRM, and the analytics-and-dashboard infrastructure.
Workflow Automator's automated 24/7 round-the-clock workforce handles the per-transaction execution across the multiple departmental-systems. The workforce processes approximately 100 transactions per day with the structured-execution pattern.
Anomaly Detector's deduction-code-prediction analytics-engine uses the supervised-learning model trained on the historical deduction-code-and-transaction-characteristic data. The model produces the per-transaction deduction-code prediction with the appropriate confidence-scoring; the high-confidence predictions flow into the automated workflow, while the low-confidence predictions route to the manual-review workflow.
Workflow Planner's per-transaction lifecycle orchestration handles the end-to-end workflow from the transaction-identification through the deduction-code-assignment, the per-stakeholder-engagement (where required) and the SAP-posting. The orchestration handles the parallel-engagement where the per-stakeholder queries can run in parallel.
FP&A Insights' management-reporting-and-analytics layer provides the per-day operational dashboards with the per-transaction execution-metrics, the per-customer-and-per-segment recovery-pattern visibility, and the per-deduction-code categorisation-trend analysis. The dashboards support the strategic-collections-management decision-making.
The audit trail captures every transaction-lifecycle event with the full context preserved for the financial-and-compliance audit requirements.
The numbers behind the story
Efficiency has increased 95% through the structured-automation-and-analytics workflow. The per-transaction execution-effort has compressed substantially with the structured-automation pattern.
Turn-around-time has reduced 86% across the per-transaction processing. The post-RPA-deployment turn-around-time has reduced to less than 10 minutes per transaction against the previous 7-days-per-transaction baseline.
Per-transaction lead-time has reduced from 7 days to 1 day across the operational period. The structural-acceleration supports the working-capital-recovery cadence and the strategic-customer-relationship-management work.
73% of the work has been redirected to more value-add activities. The collections-team capacity that had been absorbed by the per-transaction orchestration work has been substantially redirected to the strategic-collections-management work.
Team-productivity has increased 48% through the structured-automation-and-analytics workflow. The per-FTE transaction-handling-capacity has materially improved; the volume of transactions processed has increased 23% on the same workforce-base.
ROI has been achieved within 4 months of the platform deployment with the 100% reduction-in-errors. The platform's payback economics are structurally strong, with the ongoing operational benefit continuing to accrue as the transaction-volume continues to grow on the automated foundation.
An unexpected outcome: the structured deduction-code-and-transaction-pattern data has supported the technology giant's strategic-customer-relationship-management work. The per-customer-and-per-segment deduction-pattern visibility has surfaced customer-relationship insights that the sales-team is using for the strategic account-management engagement.
“Our collections workflow had been structurally tedious with hours wasted on per-transaction coordination across multiple departments. MindMap delivered ninety-five per cent efficiency gain, eighty-six per cent TAT reduction and seventy-three per cent of the work redirected to value-add activities — with the analytics-engine-driven deduction-code prediction collapsing our seven-day-per-transaction process into a one-day workflow.”— Chief Financial Officer· Global Technology Giant
Why MindMap was chosen
The technology giant had previously evaluated specialist collections-automation vendors. The vendors proposed proprietary-platform deployments that the CFO concluded would generate the structural-dependency on the vendor's platform for the ongoing operation.
MindMap's accelerator-composition approach — bringing Workflow Automator, Anomaly Detector, Workflow Planner and FP&A Insights around the existing AR-and-SAP-and-CRM estate — was the structural differentiator. The approach delivered the collections-automation without the proprietary-platform dependency.
Our embedded enterprise-collections expertise on the delivery team (two former enterprise-collections directors and a former analytics-specialist with collections-analytics depth) was the third factor. The CFO valued the team's understanding of the enterprise-collections reality and the cross-department coordination patterns.
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