System and method for unsupervised abstraction of sensitive data for consortium sharing

An abstraction system for generating a standard customer profile in a data processing system has a processing device and a memory. The abstraction system may receive customer data from a computing device over a network and perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a first feature in common. The abstraction system performs unsupervised learning on the plurality of clusters of customers to produce a plurality of sub-clusters of customers with a second feature in common, and repeats the unsupervised learning on the plurality of sub-clusters produced to produce further sub-clusters with a plurality of features in common. The abstraction system determines that a sub-cluster represents a standard customer and stores a plurality of standard customer profiles based on the determined standard customers. The abstraction system provides the standard customer profiles to a cognitive system for generating synthetic transaction data.

TECHNICAL FIELD

The present invention relates generally to a cognitive system implementing a transaction data simulator, and more particularly to systems and methods for unsupervised abstraction of sensitive data for consortium sharing of information.

BACKGROUND

A financial crime detection system, e.g., IBM® Financial Crimes Alerts Insight with Watson™, can utilize cognitive analytics to help banks to detect money laundering and terrorist financing. The cognitive analytics differentiate “normal” financial activities from “suspicious” activities, and use the differentiation information to build a predictive model for banks. A large set of real financial customer data is required to train the predictive model.

Since the real customer data is very sensitive, only a limited amount of real customer data can be provided by banks. However, in order to best simulate fraudulent situations and detect different types of financial crimes, more simulated customer data, e.g., transaction data for training, which looks realistically, could produce a better predictive model.

SUMMARY

According to some embodiments, the present disclosure describes a computer-implemented method for generating a standard customer profile in a data processing system. The method includes performing steps by a processing device, including receiving customer data from a computing device over a network, the customer data including information for a plurality of customers to a plurality of entities, and performing unsupervised learning on the customer data to produce a plurality of clusters of customers with a first feature in common. The method further includes performing unsupervised learning on the plurality of clusters of customers to produce a plurality of sub-clusters of customers with a second feature in common, repeating the unsupervised learning on the plurality of sub-clusters to produce further sub-clusters with a plurality of features in common, and determining that a sub-cluster represents a standard customer and storing a plurality of standard customer profiles based on the determined standard customers. The method additionally includes providing the plurality of standard customer profiles to a cognitive system for generating synthetic transaction data based on the standard customer.

According to other embodiments, the present disclosure describes an abstraction system for generating a standard customer profile in a data processing system. The abstraction system may include a processing device and a memory. The abstraction system may receive customer data from a computing device over a network, the customer data including information for a plurality of customers to a plurality of entities, and perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a first feature in common. The abstraction system may also perform, by the processing device, unsupervised learning on the plurality of clusters of customers to produce a plurality of sub-clusters of customers with a second feature in common, and repeat the unsupervised learning on the plurality of sub-clusters produced by the unsupervised learning to produce further sub-clusters with a plurality of features in common. The abstraction system may also determine a sub-cluster represents a standard customer and storing a plurality of standard customer profiles based on the determined standard customers, and provide the standard customer profiles to a cognitive system for generating synthetic transaction data based on the standard customer.

According to additional embodiments, the present disclosure describes a non-transitory computer readable medium having stored thereon instructions for generating a standard customer profile in a data processing system, which when executed by at least one processing device performs disclosed methods consistent with disclosed embodiments

Additional features and advantages of this disclosure will be made apparent from the following detailed description of illustrative embodiments that proceeds with reference to the accompanying drawings.

DETAILED DESCRIPTION

As an overview, a cognitive system is a specialized computer system, or set of computer systems, configured with hardware and/or software logic (in combination with hardware logic upon which the software executes) to emulate human cognitive functions. These cognitive systems apply human-like characteristics to conveying and manipulating ideas which, when combined with the inherent strengths of digital computing, can solve problems with high accuracy and resilience on a large scale. IBM Watson™ is an example of one such cognitive system which can process human readable language and identify inferences between text passages with human-like accuracy at speeds far faster than human beings and on a much larger scale. In general, such cognitive systems are able to perform the following functions:Navigate the complexities of human language and understandingIngest and process vast amounts of structured and unstructured dataGenerate and evaluate hypothesesWeigh and evaluate responses that are based only on relevant evidenceProvide situation-specific advice, insights, and guidanceImprove knowledge and learn with each iteration and interaction through machine learning processesEnable decision making at the point of impact (contextual guidance)Scale in proportion to the taskExtend and magnify human expertise and cognitionIdentify resonating, human-like attributes and traits from natural languageDeduce various language specific or agnostic attributes from natural languageHigh degree of relevant recollection from data points (images, text, voice) (memorization and recall)Predict and sense with situation awareness that mimics human cognition based on experiencesAnswer questions based on natural language and specific evidence

In one aspect, the cognitive system can be augmented with a transaction data simulator, to simulate a set of customer transaction data from a financial institution, e.g., a bank. The simulated customer transaction data, even if it is not “actual” customer transaction data from the financial institution, can be used to train the predictive model for identifying financial crimes.

The transaction data simulator combines a multi-layered unsupervised clustering approach with interactive reinforcement learning (IRL) model to create a large set of intelligent agents that have learned to behave like “standard customers.”

In an embodiment, the multi-layered unsupervised clustering approach creates a large set of standard customer transaction behaviors (extracted from real customer transaction data provided by a bank), using information including hundreds of attributes of “standard customers” over varying periods of time. Each standard customer transaction behavior can be associated with a group of customers having similar transaction characteristics. An intelligent agent generates an artificial customer profile, and selects one of standard customer transaction behaviors to be combined with the generated artificial customer profile. In this way, the intelligent agent can simulate a “standard customer,” and learn to behave like the “standard customer.” The intelligent agent is then provided with a period of time (e.g., ten years), during which the intelligent agent can observe an environment, e.g., past behaviors of the represented “standard customer”) and learn to perform “fake” customer transactions which are similar to standard customer transaction behavior of the represented “standard customer.” Each factor of the standard customer transaction behavior can be statistic data. For example, the transaction amount of the standard customer transaction behavior can be a range of values, e.g., the transaction amount of the standard customer transaction behavior is $20-$3,000. The transaction location of the standard customer transaction behavior can be provided statistically, e.g., 30% of transaction locations are shopping malls, 50% of transaction locations are restaurants, and 20% of transaction locations are gas stations. The transaction type of the standard customer transaction behavior can be provided statistically, e.g., 20% of transaction types are check payment, 40% of transaction types are POS payment, 25% of transaction types are ATM withdrawal, and 15% of transaction types are wire transfer. The transaction medium of the standard customer transaction behavior can be provided statistically, e.g., 15% of transaction mediums are cash, 45% of transaction mediums are credit card, 25% of transaction mediums are checking accounts, and 15% of transaction mediums are PayPal®.

In an embodiment, a large number of artificial customer profiles are generated from a plurality of real customer profile data. The real customer profile data can be provided by one or more banks. Each real customer profile can include an address of a customer; a name of a customer (the customer can be a legal entity or individual); contact information such as a phone number, an email address, etc.; credit information, such as a credit score, a credit report, etc.; income information (e.g., an annual revenue of a legal entity, or a wage of an individual), and the like. The real customer profile data are stored under different categories. For example, commercial customers (i.e., legal entities) can be divided into different categories based on the size, product or service of the commercial customers. An artificial customer profile can be generated by randomly searching all the real customer profile data. For example, an artificial customer profile can be generated by combining randomly selected information including address, first name, second name, phone number, email address, credit score, revenue or wage, etc. Thus, the generated artificial customer profile extracts different pieces of information from real customer profile data, and thus looks like a realistic customer profile. Financial transaction data is further simulated associated with each artificial customer profile.

In an embodiment, to protect privacy of real customers, composite information, such as an address, a name, etc. can be split into a plurality of parts before the random selection. For example, the address “2471 George Wallace Street” can be parsed into 3 parts: [number] “2471,” [name] “George Wallace,” and [suffix] “Street.” These parts can be randomly selected individually to form an artificial customer profile. In a further embodiment, the composite information of an artificial customer profile, such as an address, a name, etc. is compared to the composite information of a real customer profile. If the similarity degree is greater than a predefined threshold value, then the artificial customer profile is unacceptable and needs to be updated until the similarity degree is less than the predefined threshold value.

FIG. 1depicts a schematic diagram of one illustrative embodiment of a cognitive system100implementing a transaction data simulator110and an abstraction system120in a computer network114. The cognitive system100is implemented on one or more computing devices112(comprising one or more processing devices and one or more memories, and potentially any other computing device elements generally known in the art including buses, storage devices, communication interfaces, and the like) connected to the computer network114. The computer network114includes multiple computing devices112in communication with each other and with other devices or components via one or more wired and/or wireless data communication links, where each communication link comprises one or more of wires, routers, switches, transmitters, receivers, or the like. Other embodiments of the cognitive system100may be used with components, systems, sub-systems, and/or devices other than those that are depicted herein. The computer network114includes local network connections and remote connections in various embodiments, such that the cognitive system100may operate in environments of any size, including local and global, e.g., the Internet. The cognitive system100is configured to implement a transaction data simulator110that can simulate standard customer transaction data106(i.e., a standard customer transaction behavior). The transaction data simulator110can generate a large set of simulated customer transaction data108based on the standard customer transaction data106, so that the simulated customer transaction data108looks like real customer transaction data. In an embodiment, the standard customer transaction data106is obtained through unsupervised clustering approach. Raw customer data including a large amount of customer transaction data is provided by one or more banks, and a large set of small groups representing different characteristics of bank customers are clustered or grouped from the raw customer data through unsupervised clustering approach. Each small group includes transaction data from customers having similar characteristics. For example, group A represents customers who are single attorneys practicing patent law in New York, while group B represents customers who are married attorneys practicing commercial law in New York.

The abstraction system120is implemented in hardware and/or software and is configured to perform unsupervised abstraction of standard customer transaction data106to produce one or more standard customers that are abstract representations of real customers, but which do not contain traceable customer information that could expose sensitive information. In an exemplary embodiment, the abstraction system120is configured to perform repeated unsupervised learning steps to cluster and sub-cluster real customer data to produce a standard customer that represents a small group of customers.

FIG. 2is a block diagram of an example data processing system200in which aspects of the illustrative embodiments are implemented. Data processing system200is an example of a computer in which computer usable code or instructions implementing the process for illustrative embodiments of the present invention are located. In one embodiment,FIG. 2represents the transaction data simulator110, which implements at least some of the aspects of the cognitive system100described herein.

In the depicted example, data processing system200can employ a hub architecture including a north bridge and memory controller hub (NB/MCH)201and south bridge and input/output (I/O) controller hub (SB/ICH)202. Processing unit203, main memory204, and graphics processor205can be connected to the NB/MCH201. Graphics processor205can be connected to the NB/MCH201through an accelerated graphics port (AGP).

In the depicted example, the network adapter206connects to the SB/ICH202. The audio adapter207, keyboard and mouse adapter208, modem209, read only memory (ROM)210, hard disk drive (HDD)211, optical drive (CD or DVD)212, universal serial bus (USB) ports and other communication ports213, and the PCI/PCIe devices214can connect to the SB/ICH202through bus system216. PCI/PCIe devices214may include Ethernet adapters, add-in cards, and PC cards for notebook computers. ROM210may be, for example, a flash basic input/output system (BIOS). The HDD211and optical drive212can use an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. The super I/O (SIO) device215can be connected to the SB/ICH202.

An operating system can run on processing unit203. The operating system can coordinate and provide control of various components within the data processing system200. As a client, the operating system can be a commercially available operating system. An object-oriented programming system, such as the Java™ programming system, may run in conjunction with the operating system and provide calls to the operating system from the object-oriented programs or applications executing on the data processing system200. As a server, the data processing system200can be an IBM® eServer™ System p® running the Advanced Interactive Executive operating system or the LINUX® operating system. The data processing system200can be a symmetric multiprocessor (SMP) system that can include a plurality of processors in the processing unit203. Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as the HDD211, and are loaded into the main memory204for execution by the processing unit203. The processes for embodiments of the web site navigation system can be performed by the processing unit203using computer usable program code, which can be located in a memory such as, for example, main memory204, ROM210, or in one or more peripheral devices.

A bus system216can be comprised of one or more busses. The bus system216can be implemented using any type of communication fabric or architecture that can provide for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit such as the modem209or network adapter206can include one or more devices that can be used to transmit and receive data.

Those of ordinary skill in the art will appreciate that the hardware depicted inFIG. 2may vary depending on the implementation. For example, the data processing system200includes several components which would not be directly included in some embodiments of the abstraction system120. However, it should be understood that a transaction data simulator110may include one or more of the components and configurations of the data processing system200for performing processing methods and steps in accordance with the disclosed embodiments.

Moreover, other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives may be used in addition to or in place of the hardware depicted. Moreover, the data processing system200can take the form of any of a number of different data processing systems, including but not limited to, client computing devices, server computing devices, tablet computers, laptop computers, telephone or other communication devices, personal digital assistants, and the like. Essentially, data processing system200can be any known or later developed data processing system without architectural limitation.

FIG. 3is a schematic diagram of one illustrative embodiment of the abstraction system120. In some embodiments, the abstraction system120may include a plurality of modules stored in the main memory204. The plurality of modules may be implemented in hardware and/or software. The abstraction system120may include a data collection module310, an unsupervised learning module320, a standard customer module330, and a boundary module340. In some embodiments, the abstraction system120may further include and/or be connected to one or more data repositories350.

The data collection module310may be configured to receive customer data from a computing device112. The customer data may be actual customer data. For example, the customer data106from a financial institution and include information such as identifying information, transaction information, etc. The customer data106may include a variety of features that are separately stored as individual categories of information. For instance, the customer data106may include spending data, payment data, time period data, location data, etc. In some embodiments, the data collection module310may be configured to collect data from a plurality of computing devices112, such as from a plurality of financial institutions. In some embodiments, the data collection module310may be configured to perform a filtering process to create groups of data for analysis. For example, the data collection module310may use a manual or automatic categorization of customers to create a pool of similar customers (e.g., individuals, corporations, retail, service, etc.).

The unsupervised learning module320may be configured to perform unsupervised learning on a data set. The unsupervised learning may be, for example, a clustering algorithm configured to group one or more subsets of data based on patterns, trends, and/or other similarities found in the data. The unsupervised learning module320may be configured to perform a clustering process without manual input into the groupings (thus “unsupervised” learning). As a result, the clusters may be free from biases of how a user may believe that data should be grouped.

The standard customer module330may be configured to extract clusters or groups from the output of the unsupervised learning module in order to generate and store a standard customer profile that is based on the input data from the data collection module. The standard customer module330may be configured to perform a general sanity check of a cluster (e.g., sample size, statistical significance, etc.) to determine when a cluster or sub-cluster can be considered a standard customer.

The boundary module340may be configured to further divide collected customer data according to one or more boundaries. For instance, the boundary module340may be a statistic and/or time slicing module configured to further filter data according to one or more parameters such that an individual customer and/or standard customer can be analyzed from different viewpoints. For instance, the boundary module340may create subcategories of data based on two or more features (e.g., transaction information and time information). For instance, customer data collected by the data collection module310may provide transaction information for a customer over a year. The boundary module340may place time period boundaries on the data over the year to identify additional features that can be considered data points. For instance, the boundary module340may create categories for “holiday spending,” “vacation spending,” “lunchtime spending,” “savings periods,” etc. The boundary module340may thus be used to further subdivide and categorize customer data. The boundary module340may apply these principles to standard customers in some embodiments. For example, the boundary module340may derive additional standard customer behaviors from an established customer behavior by groping data at certain time periods or based on other statistical boundaries.

FIG. 4is a diagram of a process flow for using unsupervised learning on customer data106to produce one or more standard customer profiles using data abstraction. As a result of the data abstraction, the customer data106is abstracted/aggregated to the point that it can be saved and stored locally, without privacy issues. In some embodiments, the data collection module310may receive customer data106from one or more computing devices112. The data collection module310may perform initial filtering405of the data. For instance, the data collection module310may perform an RFM (recency, frequency, monetary value) analysis to sub-group data from the customer data106. The unsupervised learning module320may perform a clustering process410to create one or more data clusters415. The one or more data clusters415may be groupings of customers based on the unsupervised learning algorithm applied as the clustering process410. The clusters415may be based on a similarity of one or more features in the customer data. For instance “Cluster 1” of the clusters415may be all customers in a particular geographic area while “Cluster 2” of the clusters415may be all customers over a particular age, that spend a certain amount per year, that deposit less than a certain amount per year, etc. The unsupervised learning410may produce any number of clusters415and a customer may be in more than one of the clusters.

The unsupervised learning module320may perform additional clustering processes420to create one or more sub-clusters425. This unsupervised learning module320may generate sub-clusters425through further grouping of customers based on an additional similarity in the data. For instance, for customers in an initial cluster415based on location, a sub-cluster may be based on age, job, spending, transaction details, etc. The unsupervised learning420to produce sub-clusters425may be repeated any number of times until the standard customer module330identifies a cluster of sub-cluster that is considered a standard customer430. For instance, the standard customer module330may select clusters that satisfy certain criteria, such as number of customers and/or similar features in a group. The customer module330may store these as standard customers430as profiles for use as “abstract” customers that can be used to reproduce realistic customer data. For instance, the standard customers430may be provided to the cognitive system100for use with the transaction data simulator110.

FIG. 5is an exemplary process500for converting customer data into abstract standard customers for use in generating synthetic transaction data that is realistic but cannot be traced back to the actual data. In step510, the data collection module310receives and filters customer data. In step520, the unsupervised learning module320applies an algorithm to data to produce clusters of customers based on their similarity in at least one feature. In step530, the unsupervised learning module performs unsupervised learning on clusters to produce sub-clusters of customers and customer features. The clustering process may be repeated as necessary to produce smaller and more specific groups of customers. In at least some embodiments, each unsupervised learning step adds a data feature to a grouping of customers.

In step540, the standard customer module330determines standard customers based on the clusters and sub-clusters of data through unsupervised learning. The standard customer module330may use a rules database to determine when a cluster is considered a standard customer. For instance, the standard customer module330may compare a number of data features and a number of customers in a grouping to threshold values to determine whether the group has sufficient and/or narrow enough data to be considered a standard customer.

In step550, the boundary module340may further derive additional standard customers. For example, in some embodiments, the boundary module340may add customers to standard customer profiles based on a portion of their data fitting into a customer profile. For instance, the boundary module340may perform a bounding operation on customer data to identify customers that fit into standard customer profiles when certain boundaries are applied. For instance, the boundary module340may select a cluster or a standard customer profile and perform additional analysis to view the evolution of behavior of a customer when the element of time is considered. In other examples, the boundary module340may apply a statistical boundary to derive additional standard customers.

In step560, the abstraction system120may provide the standard customers to the cognitive system100. The cognitive system may use the standard customers as an input to create new, synthetic transaction data108that fits in the standard customer behavior, but is not traceable back to the original, actual customer data. As a result, real customer data106is used to create artificial customer data108that can be relied upon as being realistic but which does not expose the actual sensitive customer data.

FIG. 6is a representation of standard customers610,620that may be generated through one or more disclosed processes based on customer data106. In an exemplary embodiment, the standard customers610,620include a plurality of features that describe the customers that are present in the grouping that makes up the standard customer610,620. For instance, feature 1 may include a customer age, feature 2 may include customer income, feature 3 may include customer spending, etc. At least some of the features that make up the standard customers610,620may be represented as a distribution of data. For instance, the distribution may be a distribution of data with data points for each customer in the standard customer profile. The distribution is thus a representation of the actual customer data, but it is a generic, statistical representation that is abstracted such that the actual data is not exposed.

FIG. 7depicts a schematic diagram of one illustrative embodiment of the transaction data simulator110. The transaction data simulator110utilizes reinforcement learning techniques to simulate financial transaction data. The transaction data simulator110includes intelligent agent702, and environment704. The intelligent agent702randomly selects a standard transaction behavior720(i.e. goal720) representing a group of “customers” having similar transaction characteristics, and associates the standard transaction behavior with a randomly selected artificial customer profile718. The intelligent agent702takes an action712in each iteration. In this embodiment, the action712taken in each iteration includes conducting a plurality of transactions in a single day. Each transaction has the information including transaction type (e.g., Automated Clearing House (ACH) transfer, check payment, Wire transfer, Automated Teller Machine (ATM) withdrawal, Point of Sale (POS) payment, etc.); transaction amount; transaction time; transaction location; transaction medium (e.g., cash, credit card, debit card, PayPal®, checking account, etc.); the second party who is related to the transaction (e.g., a person who receives the wire transferred payment), and the like. The environment704takes the action712as input, and returns reward714(or feedback) and state716from environment704as the output. The reward714is the feedback by which the success or failure of the action712is measured. In this embodiment, the environment704compares the action712with goal720(e.g., standard transaction behavior). If the action712deviates from the goal720beyond a predefined threshold, then the intelligent agent702is penalized, while if the action712deviates from the goal720within a predefined threshold (i.e., the action712is similar to the goal720), the intelligent agent702is rewarded. The action712is effectively evaluated, so that the intelligent agent702can improve the next action712based on the reward714. In this embodiment, the environment704is a set of all old actions taken by the intelligent agent702, i.e., the environment704is a set of all old simulated transactions. The intelligent agent702observes the environment704, and gets information about the old transactions, e.g., the number of transactions that have been made within a day, a week, a month, or a year; each transaction amount, account balance, each transaction type, and the like. The policy engine706can adjust the policy based on the observations, so that the intelligent agent702can take a better action712in the next iteration.

The intelligent agent702further includes policy engine706, configured to adjust a policy based on the state716and the reward714. The policy is a strategy that the intelligent agent702employs to determine the next action712based on the state716and the reward714. The policy is adjusted, aiming to get a higher reward714for the next action712taken by the intelligent agent702. The policy includes a set of different policy probabilities or decision-making probabilities which can be used to decide whether a transaction is going to be performed in a particular day or not, the number of transactions per day, transaction amount, transaction type, transaction party, etc. In reinforcement learning model, outcome of events are random, and a random number generator (RNG) is a system that generates random numbers from a true source of randomness. In an example, the maximum number of transactions per day is 100, and the maximum transaction amount is $15 million. In the first iteration, a random transaction with transaction amount of $15 million to Zimbabwe is made by the intelligent agent702. This action712deviates far from the goal720(e.g., transaction made by married attorneys practicing commercial law in Maine), and thus this action712is penalized (i.e., the reward714is negative). The policy engine706is trained to adjust the policy, so that a different transaction which is closer to the goal720can be made. With more iterations, transactions which are similar to the goal720can be simulated by the “smarter” policy engine706. As shown inFIG. 8, a plurality of transactions from the customer “James Culley” are simulated, and the simulated transaction data is similar to the goal720.

As shown inFIG. 2, in an embodiment, one feedback loop (i.e., one iteration) corresponds to one “day” of actions (i.e., one “day” of simulated transactions). During a period of time, e.g., ten years, the intelligent agent702learns how to take an action712to get a reward714as high as possible. The number of iterations corresponds to the duration of time. For example, ten years correspond to 10×365=3650 iterations. Reinforcement learning model judges the actions712by the results that the actions712produce. It is goal720oriented, and its aim is to learn sequences of actions712that will lead the intelligent agent702to achieve its goal720, or maximize its objective function.

In an embodiment, the transaction data simulator110further includes updater710. A new action712is performed in each iteration. The updater710updates the environment704with the action712taken by the intelligent agent702after each iteration. The action712taken in each iteration is added into the environment704by the updater710. In an embodiment, the transaction data simulator110further includes pruner708, configured to prune the environment704. In an embodiment, the pruner708can remove one or more undesired actions. For example, actions712which are taken in the first ten iterations are removed, because these ten iterations deviate far from the goal720, and the degree of similarity is below a predefined threshold. In another embodiment, a full re-initialization of the transaction data simulator110can be performed to remove all the accumulated actions in the environment704, so that the intelligent agent702can start over again.

FIG. 8illustrates a flow chart of one illustrative embodiment showing a method800of simulating transaction data. At step802, standard customer transaction behavior data is provided as goal720. The standard customer transaction behavior represents a group of customers having similar transaction characteristics. The standard customer transaction behavior is obtained through unsupervised clustering approach.

At step804, an action712is taken to conduct a plurality of transactions in an iteration representing e.g., a single day (e.g., 100 transactions per day). Each transaction has the information including transaction type, transaction amount, transaction time, transaction location, transaction medium, the second party who is associated with the transaction, and the like.

At step806, the environment704compares the goal720with the action712taken in this iteration, rewards or penalizes the action712based on similarity to or deviation from the goal720. The threshold or rule to decide whether the action712is similar to the goal720or not, is predefined, and can be adjusted based on how similar to the goal720the user prefers.

At step808, the environment704is updated to include the action712in the present iteration. The environment704includes a set of all old actions.

At step810, the policy engine706adjusts a policy for determining the next action712based on the reward714(i.e., reward or penalty). The policy is made based on a variety of factors, e.g., probability of occurrence of a transaction, the number of transactions per day, transaction amount, transaction type, transaction party, transaction frequency of each transaction type, an upper bound and a lower bound for each transaction, transaction medium, and the like. The policy can adjust weights of these factors based on the reward714in each iteration.

At step812, in a new iteration, the intelligent agent702takes a new action712. The steps804to812are repeated until the action712is similar enough to the goal720(step814). For example, the transaction amount specified in the goal720is $20-$3,000. If the transaction amount of each transaction in the action712falls within the range of $20-$3,000, then the action712is similar enough to the goal720.

Since the standard customer transaction data106may include abnormal data, e.g., a fraudulent transaction, the simulated customer transaction data108may also include abnormal data, because the simulated customer transaction data108is similar to the standard customer transaction data106. In reinforcement learning model, the intelligent agent702explores the environment704randomly or stochastically, learns a policy from its experiences, and updates the policy as it explores to improve the behavior (i.e., transaction) of the intelligent agent702. In an embodiment, a behavioral pattern (e.g., spending “splurges” until running out of savings, or experiencing “buyer's remorse” on one big purchase, etc.), as opposed to random actions, may emerge during RNG based exploration. An abnormal behavioral pattern may indicate a fraudulent transaction. For example, a simulated customer James Culley may generally make transactions having a transaction amount below $1,000. Suddenly, there is a transaction having a transaction amount of $5,000, and this suspicious transaction may be a fraudulent transaction (e.g., the credit card of James Culley is stolen, or the checking account of James Culley is hacked).

There is a behavioral pattern that naturally emerges during exploration. For example, as shown inFIG. 9, the simulated customer James Culley received an amount of $12,387.71 in a checking account on Jan. 1, 2014. James Culley spent $474.98 on Jan. 9, 2014, $4,400 on Jan. 31, 2014 and $3,856.55 on Mar. 2, 2014 through a debit card associated with the checking account. In the next Month, James Culley received an amount of $12,387.71 in the checking account on Feb. 1, 2014. James Culley spent $4,500 on Feb. 2, 2014, and $1,713.91 on February 3 through the debit card associated with the checking account, and transferred $8,100 out of the checking account on Jun. 27, 2014. In this example, this simulated customer James Culley has a tendency of save-and-spend, and occasionally has a big purchase. The behavioral pattern makes this simulated customer James Culley behave more realistically (i.e., look more like a real customer, rather than a robot). A plurality of parameters, such as “behavioral consistency” (the degree of behavioral consistency in a period of time), “consistency volatility” (frequency of behavior change), “behavior abnormality” (deviation from regular transaction behaviors), etc. are generated by the policy engine706, and used to show a different personality of each simulated customer.

The transaction data simulator110uses abstracted or aggregated real customer data to simulate customer data that is representative of real customers. The transaction data simulator110can provide a large set of simulated customer data (i.e., simulated transaction data in combination with an artificial customer profile) that can be used to train a predictive model for detecting abnormal customer behaviors. Further, the simulated customer data is generated based on abstracted data of the real raw customer data, rather than the real raw customer data itself, and thus it is impossible to derive actual transaction actions of any real customer. Additionally, the transaction data simulator110allows generation of a behavioral pattern for each simulated customer during iterations.

The system and processes of the figures are not exclusive. Other systems, processes, and menus may be derived in accordance with the principles of embodiments described herein to accomplish the same objectives. It is to be understood that the embodiments and variations shown and described herein are for illustration purposes only. Modifications to the current design may be implemented by those skilled in the art, without departing from the scope of the embodiments. As described herein, the various systems, subsystems, agents, managers, and processes can be implemented using hardware components, software components, and/or combinations thereof. No claim element herein is to be construed under the provisions of 35 U.S.C. 112 (f), unless the element is expressly recited using the phrase “means for.”

Although the invention has been described with reference to exemplary embodiments, it is not limited thereto. Those skilled in the art will appreciate that numerous changes and modifications may be made to the preferred embodiments of the invention and that such changes and modifications may be made without departing from the true spirit of the invention. It is therefore intended that the appended claims be construed to cover all such equivalent variations as fall within the true spirit and scope of the invention.