Abstract: Systems and methods may be provided for deposit prediction based upon template matching. The system and methods may include receiving deposit information associated with a plurality of deposits for a deposit account of a financial institution customer, where the deposit information includes a plurality of deposit amounts, and a respective date associated with each of the plurality deposit amounts; identifying, based upon the received deposit information, one or more deposit patterns by applying one or more template patterns to the deposit information and determining that one or more of the applied template patterns match at least a portion of the deposit information; and generating an indication of the identified one or more deposit patterns.

Abstract: Systems and methods may be provided for deposit prediction based upon an example Monte Carlo analysis. The system and methods may include receiving deposit information associated with a plurality of deposits for a deposit account of a financial institution customer, where the deposit information includes a plurality of deposit amounts, and a respective date associated with each of the plurality of deposit amounts; applying a Monte Carlo analysis to at least a portion of the received deposit information; and identifying, based upon the applied Monte Carlo analysis, a largest total monetary amount that can be expected to be deposited within a time interval according to a predetermined confidence level.

Abstract: Systems and methods may be provided for deposit prediction based upon an example Monte Carlo analysis. The system and methods may include receiving deposit information associated with a plurality of deposits for a deposit account of a financial institution customer, where the deposit information includes a plurality of deposit amounts, and a respective date associated with each of the plurality of deposit amounts; applying a Monte Carlo analysis to at least a portion of the received deposit information; and identifying, based upon the applied Monte Carlo analysis, a largest total monetary amount that can be expected to be deposited within a time interval according to a predetermined confidence level.

Abstract: Systems and methods may be provided for deposit prediction based upon template matching. The system and methods may include receiving deposit information associated with a plurality of deposits for a deposit account of a financial institution customer, where the deposit information includes a plurality of deposit amounts, and a respective date associated with each of the plurality deposit amounts; identifying, based upon the received deposit information, one or more deposit patterns by applying one or more template patterns to the deposit information and determining that one or more of the applied template patterns match at least a portion of the deposit information; and generating an indication of the identified one or more deposit patterns.

Abstract: A method for minimizing overdraft charge-off in a bank account includes: (a) identifying a plurality of factors that can result in a bank account charge-off; (b) building a case history database of bank account holder information based upon a plurality of data elements associated with each of the plurality of factors; (c) creating numeric and/or categorical representations of the bank account holder information for each of the bank accounts in the case history database; (d) tabulating the numeric and/or categorical representations and the data elements for the bank accounts in the case history database; (e) combining certain ones of the numeric and/or categorical representations to separate the bank accounts in the case history database into a plurality of risk groups, each risk group having an associated probability of charge-off; and (f) optimizing, for each of the plurality of risk groups, a bank profit, by setting overdraft limits.

Abstract: A method for minimizing overdraft charge-off in a bank account includes: (a) identifying a plurality of factors that can result in a bank account charge-off; (b) building a case history database of bank account holder information based upon a plurality of data elements associated with each of the plurality of factors; (c) creating numeric and/or categorical representations of the bank account holder information for each of the bank accounts in the case history database; (d) tabulating the numeric and/or categorical representations and the data elements for the bank accounts in the case history database; (e) combining certain ones of the numeric and/or categorical representations to separate the bank accounts in the case history database into a plurality of risk groups, each risk group having an associated probability of charge-off; and (f) optimizing, for each of the plurality of risk groups, a bank profit, by setting overdraft limits.