Discovering patterns in sequences of events
Artificial Intelligence
The art of computer programming, volume 3: (2nd ed.) sorting and searching
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C/C++ Users Journal
Automatic spelling correction in scientific and scholarly text
Communications of the ACM
Computer programs for detecting and correcting spelling errors
Communications of the ACM
A technique for computer detection and correction of spelling errors
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Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Predicting Rare Events In Temporal Domains
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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IEEE Transactions on Knowledge and Data Engineering
Stream prediction using a generative model based on frequent episodes in event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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Most major businesses use business process outsourcing for performing a process or a part of a process including financial services like mortgage processing, loan origination, finance and accounting and transaction processing. Call centers are used for the purpose of receiving and transmitting a large volume of requests through outbound and inbound calls to customers on behalf of a business. In this paper we deal specifically with the call centers notes from banks. Banks as financial institutions provide loans to non-financial businesses and individuals. Their call centers act as the nuclei of their client service operations and log the transactions between the customer and the bank. This crucial conversation or information can be exploited for predicting a customer's behavior which will in turn help these businesses to decide on the next action to be taken. Thus the banks save considerable time and effort in tracking delinquent customers to ensure minimum subsequent defaulters. Majority of the time the call center notes are very concise and brief and often the notes are misspelled and use many domain specific acronyms. In this paper we introduce a novel domain specific spelling correction algorithm which corrects the misspelled words in the call center logs to meaningful ones. We also discuss a procedure that builds the behavioral history sequences for the customers by categorizing the logs into one of the predefined behavioral states. We then describe a pattern based predictive algorithm that uses temporal behavioral patterns mined from these sequences to predict the customer's next behavioral state.