Understanding Sample Algorithm Groups

The following table lists the sample algorithm groups that come with PeopleSoft Receivables. As you review the samples, consider the automatic cash application capabilities of your current system and your customers' remittance patterns.

Algorithm Group SECTION	Selects These Open Items	Use For	Algorithms in Group STEPS
#BALANCE	Selects all open items for identified customers, only if the payment amount exactly matches the open items total.	Payments without item references only and customer identified.	BALGR: Selects all open items.
#BALANCE			BALNET: Selects all open items minus earned discounts.
#COMBOS	Selects all open items for identified customers only if the payment amount matches an item amount, an item amount with discount, or the total amount of any two items. The algorithm selects an item only if only one item matches the payment amount or if only two items match the payment amount. For example, if the payment amount is 200.00 EUR and one item for 200.00 EUR exists, it selects that item. However, if the payment amount is 200.00 EUR and two items exist, each for 200.00 EUR, it does not select any items.	Payments without item references only and customer identified.	DEBITGR: Selects a single unique open item.
			DEBITNT: Selects a single unique open item minus earned discount.
			ANY2GR: Selects any two unique open item balances combined.
#OLDEST1	Selects items with oldest due dates first until the remaining payment amount is less than the next item.	Payments without item references only and customer identified.	OLDEST: Selects open items in oldest first order by due date. Creates a partial payment for the next item.
#OLDESTC	Adds all items with credit balances to the payment amount. Then selects items with debit balances in oldest due dates order until the remaining payment amount is less than the next item.	Payments without item references only and customer identified.	CREDITS: First adds all credits to the payment amount. Then selects open items in oldest first order by due date. Creates a partial payment for the next item.
#OVERDUE	Selects all overdue items for payment matching using the sequence of the entry reasons associated with overdue charges.	Payments without item references only and customer identified.	OVERDUE: Selects the items to apply to the payment. First uses the sequence of overdue entry reasons. Then selects open items in oldest first order by due date. Creates a partial payment for the next item.
#PASTDUE	Selects all past-due items for the customers identified only if the payment amount exactly matches the total of the past-due items.	Payments without item references only and customer identified.	PASTGR: Selects all past-due open items.
#PASTDUE			PASTNET: Selects all past-due open items minus earned discounts.
#REFS	Selects open items that exactly match the references supplied with a payment.	Payments with item summary references only and customer identified does not matter.	ITEMREF: Selects all open items identified by the references regardless of customer identification. Enables you to add a unique debit or credit item for an underpayment or overpayment that completes the application. If a customer reference is provided, Payment Predictor selects the items where the item ID matches the item summary reference and customer ID matches the customer reference. If a customer reference is not provided, and only an item summary reference is provided, Payment Predictor selects the items where the item ID matches the item summary reference.
#REFS			MULPAY: Payment Predictor selects the payments with matching reference values and payment currency, then applies multiple payments to the same item.
#REF_ONE	Selects only open items that exactly match the references supplied with a payment.	Payments with item summary references only and customer identified.	ONECUST: Selects all open items identified by the references limited to the customer whose MICR ID or customer ID is supplied with the payment. Enables you to add a unique debit or credit item that completes the application.
#REFS_NG	Uses reference information that does not exactly match to select items.	Payments with item summary references only and customer identified does not matter.	PeopleSoft Receivables includes these algorithms as samples of possible ways for dealing with bad references. They are platform-specific and you can take advantage of any functions that your database allows.
			FIRST8: Selects all open items identified by the first eight characters of the references.
			MIDDLE7: Selects all open items identified by the seven characters after the first three characters in the references.
#DETAIL	Selects only open items that exactly match the references supplied with a payment.	Payments with item detail references only and customer identified does not matter. Created adjustments only adjust remaining overpayment or underpayment.	DETAIL: Selects all open items identified by matched references. Also accepts WS-08 deduction references. Payments with detail references should balance, but if not, the #DETAIL algorithm creates an Adjust Remaining Underpayment item for underpayments or an Adjust Remaining Overpayment item for overpayments.
#DTL_TLR	Selects only open items that exactly match the references supplied with a payment.	Payments with item detail references only and customer identified does not matter.	DTL_TLR Selects all open items identified by matched references. Closes items that match the payment amount. If an underpayment exceeds the tolerances, it looks at the bill to customer to determine whether partial payments are allowed. If they are allowed, it creates a partial payment on the item. The actions taken are based on the selections on the Receivables Options - Predictor Detail Options page.
#DTL_PM	Selects only open items that exactly or approximately match the detail references supplied with a payment.	Payments with item detail references. Identified customer does not matter.	#DTL_PM: First Pass: Selects all open items identified by matched references, including deduction references (WS-08). Second Pass: Whenever an overpayment is about to be created and there are unmatched items referenced in the payment, this algorithm serves as an example of how to deal with bad references. You can easily modify the algorithm to adapt to your organization's needs for matching open items. FIRST8 Selects all open items identified by the first eight characters of the reference. MIDDLE7 Selects all open items identified by the seven characters after the first three characters in the reference. Payments handled by this algorithm method should balance. If any of the payments do not balance, the #DTL_PM algorithm creates an Adjust Remaining Underpayment item (WS-07) for underpayments or an Adjust Remaining Overpayment item (WS-06) for overpayments. If the customer cannot be identified for the new item, the algorithm applies the payment item based on the value selected (First, Last, Specify) for the Control Customer and Business Unit field on the Receivables Options, and then Predictor Details Options page.
#DTL_TPM	Selects open items that exactly or approximately match the detail reference item supplied with the payment.	Payments with item detail references only and customer identified does not matter.	#DTL_TPM: First Pass: Selects all open items identified by exactly matched references. Second Pass: Whenever an overpayment is about to be created, and there are unmatched items referenced in the payment, this algorithm serves as an example of how to deal with bad references. You can easily modify the algorithm to adapt to your organization's needs for matching open items. FIRST8 Selects all open items identified by the first eight characters of the reference. MIDDLE7 Selects all open items identified by the seven characters after the first three characters in the reference. If an underpayment exceeds the tolerances specified on the Receivables Options, and then Predictor Detail Options page. The system looks at the Bill To customer to see whether partial payments are allowed. If partial payments are allowed, it creates a partial payment on the item. If the customer cannot be identified for the new item, the algorithm applies the payment item based on the value selected (First, Last, Specify) for the Control Customer and Business Unit field on the Receivables Options, and then Predictor Details Options page.
#STATMNT	Selects items from the most recent customer statement.	Payments without item detail references and customer identified.	STMTALL: Selects items from the most recent statement. The payment amount must exactly match the total amount of the items on the statement.