LiquidityLens is a custom AI-powered platform built for corporate treasury teams managing cash across multiple entities, currencies, and banking relationships. The platform pulls bank, ERP, and entity-level cash data into one connected view, giving finance leadership a clear read on usable liquidity instead of a delayed, fragmented picture. It was built specifically for a finance team whose treasury operations had outgrown what spreadsheets and standalone reporting tools could support.
The client is a multinational industrial holdings group operating treasury functions across dozens of entities, currencies, and bank accounts. Before LiquidityLens, their finance team pieced together cash positions manually from separate ERP systems and bank portals, with no single source showing how much cash was actually available to use at any given time. They needed a platform built around their specific entity structure and treasury workflow, not a generic finance dashboard adapted after the fact.
The client is a multinational industrial holdings group operating treasury functions across dozens of entities, currencies, and bank accounts. Before LiquidityLens, their finance team pieced together cash positions manually from separate ERP systems and bank portals, with no single source showing how much cash was actually available to use at any given time. They needed a platform built around their specific entity structure and treasury workflow, not a generic finance dashboard adapted after the fact.
Treasury teams operating across multiple entities and currencies face a recurring set of constraints
The development approach combined treasury expertise, applied machine learning, and enterprise data engineering to build a scalable liquidity intelligence platform.
LiquidityLens was built to analyze entity-level cash positions, including bank balances, ERP records, and currency exposure, classifying each balance by liquidity state in real time. Machine learning models were incorporated to forecast short-term cash needs and flag liquidity risk using entity-specific transaction patterns. The platform was designed using a consistent data architecture across entities to enable smooth integration regardless of how each system exports its data, with enterprise security controls applied throughout.
Many treasury tools provide basic cash position reporting. LiquidityLens expands liquidity intelligence through real-time cash classification, predictive forecasting by entity and currency, automated risk detection, and reporting built for direct CFO and leadership review.
Consolidates bank, ERP, and entity-level data into one continuously updated view, with every balance classified as available, restricted, or in transit.
Applies machine learning to entity-level transaction history to project short-term liquidity needs by currency, refreshing automatically as new data arrives.
Identifies emerging liquidity pressure across entities and currencies and notifies treasury teams before a risk becomes a funding gap.
Converts live platform data into a decision-ready liquidity summary, removing the manual work of assembling numbers for leadership review.
Supports cash data across multiple entities and currencies within one consistent interface, with new entities added through configuration rather than custom integration.
Matches bank and ERP data automatically, surfacing only the exceptions that require treasury staff attention.
Maintains response time and reliability as transaction volume and entity count grow.
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