Cash flow forecasting¶

This Cash flow forecasting application, part of the Finance AI App Suite, outlines a basic development and prediction workflow for a late-payment predictive model in DataRobot. It leverages data stored in SAP Datasphere, SAP S4/HANA, and SAP Analytics Cloud to develop a predictive model and enhance financial planning with AI-driven forecasts and automated insights.

This application is useful for managing cash flows, credit risks, and collections. It targets industries that deal with a large volume of invoices, delayed payments, and extended payment cycles. The application provides real-time insights into cash flow forecasts and payment timing predictions, which improves decisions around optimizing working capital and meeting quarterly financial targets.

Benefits include:

• Mitigating late or early payment risks.
• Identifing risky invoices and cutting costly borrowing.
• Eliminating billing blind spots.
• Optimizing inflows.
• Getting real-time cash forecasts.
• Spotting early and late payments.

主な機能¶

• AI-Powered cash flow forecasting—Predict when and how much customers will pay, improving liquidity planning and financial decision-making.
• Proactive late payment mitigation—Identify potential late payments and trigger automated actions to reduce Days Sales Outstanding (DSO).
• Seamless SAP integration—Directly integrate with SAP Datasphere and SAP Analytics Cloud for real-time insights and predictive analytics.
• Reduced manual effort—Automate data collection, analysis, and reporting, freeing up finance teams to focus on strategic initiatives.
• End-to-end governance and monitoring—Ensure transparency and reliability with built-in AI monitoring, reducing forecasting errors by 50% compared to SAP Cloud for Cash Management.

アーキテクチャ¶

各テンプレートは、元データの入力からデプロイされたアプリケーションまで、エンドツーエンドのAIアーキテクチャを提供する一方で、特定のビジネス要件に合わせて高度なカスタマイズが可能です。

Application templates contain three families of complementary logic. このテンプレートでは、完全にカスタムのAIロジックとフロントエンドを選択するか、DataRobotの既存の機能を利用することができます。

• AIロジック：AIリクエストへの対応、予測の生成、予測モデルの管理に必要です。

• アプリロジック：ホストされたフロントエンドを経由する場合でも、外部の消費レイヤーに統合する場合でも、ユーザーの利用に必要です。

• 運用ロジック：すべてのDataRobotアセットを有効にするために必要です。