Abstract:
A method for solving a business problem includes pooling transaction data received from a plurality of subscribers over a network, the transaction data including samples of fraudulent transactions. A data mining algorithm is then applied to the pooled transaction data, resulting in a predictive model that detects a fraudulent transaction. The predictive model is then provided to the subscribers in exchange for a subscription fee. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. 37 CFR 1.72(b).

Description:
FIELD OF THE INVENTION 
   The present invention relates generally to information technology in a business enterprise; more specifically, to data mining techniques and algorithms used to discover hidden insights and patterns present in data. 
   BACKGROUND OF THE INVENTION 
   A paramount concern in a modern enterprise is to understand the quality of its particular business. For example, it is often difficult to identify the value that a particular business entity has in the dimension of fraud, as it relates to business transactions. Often times, identification of fraudulent transactions involves analyzing data to uncover hidden insights or patterns. In the field of computer science, data mining algorithms have traditionally been utilized across a variety of industries to uncover hidden insights into data. 
   By way of example, U.S. Pat. No. 6,836,773 teaches an enterprise-wide web data mining system that generates a plurality of data mining models for generating a prediction or recommendation using data collected from the Internet. An integrated data mining and relational database management system that makes patterns uncovered during data mining available in virtual relational database tables that can be queried is described in U.S. Pat. No. 6,629,095. Similarly, U.S. Pat. No. 6,708,163 teaches a collective data mining approach for finding patterns from a network of databases, each with a distinct feature space. The approach is useful distributed fault detection in an electrical power distribution network. By way of further example, U.S. Pat. No. 6,480,844 teaches a method for mining information from large volumes of data regarding transactions of a multitude of parties. 
   Data mining algorithms generally fall into two broad categories of learning techniques: supervised learning and unsupervised learning. Unsupervised learning techniques can be used to discover associations and clusters in data, independent of a particular business objective. Alternatively, supervised learning techniques can construct predictive models for particular dimensions of a business problem, such as whether a transaction is fraudulent or not. 
   A good predictive model needs to be able to uncover patterns that are not obvious or intuitive. However, generating good predictive models for identifying particular dimensions of a business problem using supervised learning typically requires large sets of data. One particular business problem that has received much attention is the problem of detecting fraudulent transactions. A large database of fraudulent transactions is usually needed in order to train the model to differentiate between the two categories (i.e., fraudulent or legitimate) based on the hidden trends inherent to fraud. It is also important that the data set be balanced in a way that produces a good model. For example, if there are only a few fraudulent transactions in the example, and the rest are non-fraudulent, the resulting predictive model might not be able to accurately differentiate between the two categories. Often times, individual organizations lack a large enough sample of fraudulent transactions, as well as data of different types, needed to generate an adequate predictive model. 
   A number of different approaches to the problem of fraud detection have been proposed. For example, a technique for automatically designing a fraud detection system using a series of machine learning methods is described in U.S. Pat. No. 5,790,645. U.S. Patent Publication 2005/0182712 teaches a data-driven model for detecting fraudulent behavior where statistically significant data elements are not known in advance. 
   A primary drawback of existing fraud detection systems and methods is that many enterprises suffer from an inadequate volume or number of business transactions (e.g., fraudulent transactions) needed to generate an accurate predictive model. For instance, a single organization in good financial standing typically lacks a large enough sample of fraudulent transactions with which to generate an adequate model. This constitutes a fundamental barrier to learning the inherent structure of corporate fraud. 
   What is needed, therefore, is a method and/or system that overcomes the problems inherent in the prior art approaches, and which permits the construction of more accurate predictive models for business problems such as fraud detection. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The present invention will be understood more fully from the detailed description that follows and from the accompanying drawings, which however, should not be taken to limit the invention to the specific embodiments shown, but are for explanation and understanding only. 
       FIG. 1  is a conceptual block diagram of a system according to one embodiment of the present invention. 
       FIG. 2  is a flow chart illustrating a method useful in detecting fraudulent transactions according to one embodiment of the present invention. 
   

   DETAILED DESCRIPTION 
   The present invention provides a method for generating predictive models useful in identifying particular dimensions of a business problem. In the following description numerous specific details are set forth, such as particular business problems, data acquisition methods, and details of specific embodiments in order to provide a thorough understanding of the present invention. However, persons having ordinary skill in the communication arts will appreciate that these specific details may not be needed to practice the present invention. 
     FIG. 1  shows a conceptual block diagram of a system  10  according to one embodiment of the present invention. System  10  may be implemented as a stand-alone computer or data processing unit. The system  10  may also be implemented as a server-based application in which subscribers  15  interact via an agent such as a web browser or another server or other software. The server may include hardware and software elements for managing the various functions and component elements described herein. For example, a server in accordance with one embodiment of the present invention may comprise a memory or database that stores a pool of transaction data. It is appreciated that system  10  may be located at any location, for example, in a business enterprise premises or offsite connected via a global communications network such as the Internet. Alternatively, system  10  may be implemented in a distributed environment (e.g., part within an enterprise&#39;s premise and part on the Internet). 
   In a basic embodiment, the present invention comprises a business method in which a business entity sells subscriptions to other businesses or entities interested in obtaining highly accurate predictive models for their particular business problem. In the example of  FIG. 1 , a plurality of subscribers  15  enter into an agreement with a business service provider (SP) wherein the SP provides each of them with a predictive model useful in detecting fraudulent transactions, as might occur, for example, in the banking industry. As a term or condition of their subscription, each subscriber  15  agrees to provide or submit some set of transaction data to a transaction pool  11 , which is managed by the business entity. Ideally, the transaction data submitted by the subscriber&#39;s includes examples of fraudulent transactions along with non-fraudulent transaction data. 
   The transaction pool  11 , itself, may be implemented as a memory or data storage unit configured as a relational database. Transaction data may be submitted to pool  11  through a variety of known mechanisms, such as personal computers, servers, or other devices connected via a public network to the server, relational database, or mass storage device that comprises transaction pool  11 . Data may be submitted in accordance with a normative data model or data format. Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), and the Internet. Furthermore, the systems may be connected via a wireless network, e.g., a wireless LAN. System  10  may also include additional servers, subscribers, and other devices not shown. 
   In the example of  FIG. 1 , subscribers  15  submit transaction data, shown as circles  12  &amp;  13 , to populate a transaction pool  11 . The empty circles  12  represent non-fraudulent transaction data; whereas crossed circles  13  represent fraudulent data transactions. Once a sufficiently large pool  11  of transaction data has been accumulated or collected from subscribers  15 , one or more data mining algorithms  17  are applied to the pool of data in order to output or generate a predictive model  18 . In accordance with their business agreement, or as part of their subscription service, each of the subscribers  15  is provided with a copy of the resulting predictive model  18 . In other words, in exchange for signing up to the subscription service offered by the business SP (and possibly also contributing transaction data to the transaction pool  11 ) each subscriber  15  receives a highly-accurate predictive model  18  generated from the existent transaction data, which includes a “rich” pool of fraudulent transactions  13 . In this context, a “rich” transaction data refers to a large variety of data that provides different hidden patterns and modalities of fraudulent transactions. 
   In one embodiment, system  10  is implemented as an Internet portal where registered subscribers  15  contribute fraudulent transactions in exchange for the value of receiving and using the predictive model  18  generated from the existent data in pool  11 , which model uncovers the hidden patterns in data pool  11 . Practitioners in the art will understand that more than one predictive model  18  may be generated by the data mining algorithm  17 . That is, multiple types of models may be created by data mining algorithm  17 . Additionally, it is appreciated that more than one algorithm  17  may be applied to the transaction data in pool  11  to identify hidden patterns in the data. 
   It is appreciated that a variety of different business models may be employed in conjunction with system  10 . For instance, subscribers  15  who contribute significant examples or sets of fraudulent transactions may be provided with a reduced subscription rate or fee. Other subscribers, who might contribute little or no transaction data to pool  11 , may be charged a higher fee for a subscription service that provides them with predictive model  18 . For example, once pool  11  has been populated with data representing a sufficiently large number of fraudulent transactions, new subscribers may be charged a higher subscription rate under terms that does not obligate them to submit any transaction data to pool  11 . 
   In one implementation, access to predictive model(s)  18  may be offered with restrictions on the period of availability based on the number of transactions contributed. During a given subscription period, the resulting predictive model(s)  18  may change or evolve over time as more and more transaction data  16  enters the transaction pool  11 . Each registered subscriber is provided with the new, more recent predictive model as it is generated. The predictive model, itself, may comprise a variety of different forms or formats, such as simple correlation rules, decision-tree queries, and the like. 
   By way of specific example, a substantial quantity of transaction data may be supplied to the transaction pool  11  by subscribers  15  such as banks, financial institutions, credit card companies, and other types of business entities. In this manner, the predictive models  18  generated by the data mining algorithms  17  are improved by having a wide variety of fraudulent transactions  13  and non-fraudulent transactions  12  in pool  11 . The data mining algorithms  17  uncover the hidden patterns inherent in the fraudulent transactions, thereby enabling subscribers  15  to then use the predictive models  18  to detect when a particular transaction is potentially fraudulent or otherwise problematic. 
   In order to ensure that transaction data  16  contributed by subscribers  15  across a variety of organizations and industries are pooled properly, the business SP of system  10  may enforce a normative format (i.e., a normative data model). Thus, subscribers  15  who wish to contribute fraudulent transaction data  16  to the transaction pool  11  may first prepare the data in the normative format, obfuscating sensitive aspects such as the identity of the organization. In one embodiment, the business service provider of system  10  may also take measures aimed at ensuring that the contributing subscribers  15  are legitimate and that their transaction data  16  represents legitimate instances of fraud. For instance, this process may involve some type of an intermediary (e.g., internal or external auditors). 
     FIG. 2  is a flow chart illustrating a method useful in detecting fraudulent transactions according to one embodiment of the present invention. In the exemplary process shown, a subscription service is provided to entities (block  21 ) such as companies, individuals, the government, etc., interested in obtaining accurate predictive models. Subscribers collectively provide a sufficiently large amount or number of fraudulent transactions (and possibly a certain number of legitimate transactions) which is used to populate the transaction pool (block  22 ). After the pool has been sufficiently populated, one or more data mining algorithms are applied to this rich pool of data to produce one or more predictive models (block  24 ). The predictive model(s) are then made available to each of the registered subscribers (block  25 ). 
   It should be understood that elements of the present invention may also be provided as a computer program product which may include a “machine-readable medium” having stored thereon instructions which may be used to program a computer (e.g., a processor or other electronic device) to perform a sequence of operations. A “machine-readable medium” may include any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. Alternatively, the operations may be performed by a combination of hardware and software. The machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, magnet or optical cards, or other type of machine-readable medium suitable for storing electronic instructions. 
   Although the present invention has been described with reference to specific exemplary embodiments, it should be understood that numerous changes in the disclosed embodiments can be made in accordance with the disclosure herein without departing from the spirit and scope of the invention. The preceding description, therefore, is not meant to limit the scope of the invention. Rather, the scope of the invention is to be determined only by the appended claims and their equivalents.