INTRADAY CASH FLOW OPTIMIZATION

• US 20150081483A1
• Filed: 01/15/2014
• Published: 03/19/2015
• Est. Priority Date: 09/16/2013
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Brereton, JoAnn P., Hampapur, Arun, Li, Hongfei, Lougee, Robin, Qian, Buyue
• Application Number:
  US14/155,760
• Publication Number:
  US 20150081483A1
• Time in Patent Office: Days
• US Class Current: 705/30
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

Embodiments relate to intraday cash flow optimization. Transactions are accessed on a business-to-business integration network from a plurality of sources linked with payment delivery system data from a financial service system. The transactions are associated with two or more compartmentalized entities. The transactions are characterizes based on the payment delivery system data and an analysis of customer profile data. The transactions associated with two or more compartmentalized entities are linked as integrated information based on the characterizing of the transactions. An intraday receivables prediction engine and an intraday payables prediction engine are applied to the integrated information to produce an estimation of intraday cash flow. The estimation of intraday cash flow is monitored relative to intraday operations optimization conditions. An alert is generated based on determining that at least one of the intraday operations optimization conditions is met.

Claims

1. A method for intraday cash flow optimization, comprising:

accessing, by a processor, transactions on a business-to-business integration network from a plurality of sources linked with payment delivery system data from a financial service system, wherein the transactions are associated with two or more compartmentalized entities;

characterizing the transactions, by the processor, based on the payment delivery system data and an analysis of customer profile data;

linking, by the processor, the transactions associated with two or more compartmentalized entities as integrated information based on the characterizing of the transactions;

applying, by the processor, an intraday receivables prediction engine and an intraday payables prediction engine to the integrated information to produce an estimation of intraday cash flow;

monitoring, by the processor, the estimation of intraday cash flow relative to intraday operations optimization conditions; and

generating, by the processor, an alert based on determining that at least one of the intraday operations optimization conditions is met.

2. The method of claim 1, wherein the analysis of the customer profile data further comprises determining customer and account information associated with the transactions on a compartmentalized entity basis.

3. The method of claim 1, further comprising:

accessing external information in real-time to link with the transactions and form the integrated information, wherein the external information relates to one or more of;

the transactions and the customer profile data.

4. The method of claim 1, wherein the intraday operations optimization conditions comprise one or more of:

known issues for account management, mitigation rules of accounts, and regulations for maintaining liquidity.

5. The method of claim 1, further comprising:

monitoring, by the processor, the estimation of intraday cash flow relative to reinvestment conditions; and

outputting, by the processor, one or more reinvestment options based on determining that at least one of the reinvestment conditions is met by the estimation of intraday cash flow.

6. The method of claim 1, further comprising:

applying one or more offline model learning engines to produce model parameters based on identifying patterns in historical transaction data;

applying the model parameters to the transactions in real-time in combination with the customer profile data and external information from external data sources by an online transaction analytics engine comprising the intraday receivables prediction engine and the intraday payables prediction engine to produce an intraday receivables prediction and an intraday payables prediction; and

reconciling the intraday receivables prediction and the intraday payables prediction in a hierarchical format to produce the estimation of intraday cash flow.

7. The method of claim 6, further comprising:

producing the intraday receivables prediction and the intraday payables prediction on a customer and account basis.