REDUCED FRAUD CUSTOMER IMPACT THROUGH PURCHASE PROPENSITY

A method, system and computer program product for reduced fraud customer impact through purchase propensity is disclosed. A probability estimate of spending by a consumer in a merchant transaction category is computed based on historical transaction data and consumer profile data, and a propensity score for the merchant transaction is generated. The propensity score represents a propensity for the consumer to conduct the merchant transaction. The propensity score is combined in a fraud model operating in a real-time transaction stream. The fraud score can be adjusted in accordance with the propensity score.

DETAILED DESCRIPTION

This document describes reduced fraud customer impact through purchase propensity and the use of a propensity model. While fraud models focus on deviations from normal behavior, propensity models focus on the likelihood of a transaction being made. Therefore, a propensity model is different in its intent as it is not predicting normal transactions but likely transactions. For example, if there are transaction history indications related to a customer purchasing a new home, there may be a high level of propensity for that cardholder to spend in home improvement to fix the home. In another example, customers who extensively travel could have a high propensity to make transactions outside of their ZIP code and in areas having reputations as travel and entertainment areas. In yet another example, customers that execute large monthly transactions in education could be more likely to spend in sporting goods or groceries. In other words, by understanding, in detail, the previous spending of the customer over both long and short timescales, and the recency and frequency of these transactions, one can build a model to make a prediction of the probability of a customer making these transactions. In some implementations, propensity model scores are combined with traditional fraud model scores in a computer-implemented system and method to lower false positives for the clients.

Implementations described herein relate mainly to payment card fraud models, but the methods are equally applicable to any payment mechanism. A system and method as described herein can compute, both in a batch and a real-time mode, a probability estimate of spending in a given spending category. A spending category can, in general, be defined as product or product category, merchant or type of merchant, payment method or channel, geographical area such as country or postal code, spent amount, or some combination thereof. In typical card fraud models, the purchase authorization is scored for the probability of fraud, and item-level data (i.e., what exactly was purchased) is not in the authorization data. Accordingly, a system and method as described herein computes a probability estimate of spending at a particular merchant category code (MCC) combined with in vs. out of home area flag expressed as a propensity score. The propensity score on the MCC is then used in the fraud model operating in the real-time transaction stream to adjust the fraud score to reduce the estimated fraud risk if the propensity is high, and to increase the risk of fraud if the propensity is low. A real-time component update to the batch propensity score will be discussed in further detail below.

FIG. 2illustrates using batch propensity scores into a scoring flow of a fraud scoring system200. In some implementations, the fraud detection system200operates as follows. Transaction data for a transaction for a payment card is received by a transaction scoring system204from a client system202. A transaction profile is retrieved from a transaction profile database206and profile variables are updated based on the current transaction and then used to generate predictive input variables used to produce a fraud score. A propensity vector database208is updated, in some implementations on a predetermined batch update schedule with propensity models210from historical transaction data212. This propensity vector database208is also indexed by the card number (PAN) and the elements of the vector are the propensity scores for that cardholder in each of the MCC categories.

The fraud base model score is then blended with, or a second model is used to combine, the propensity scores retrieved from the propensity vector database with the fraud detection system score to produce an improved score. The propensity scores, described in further detail below, are produced by a different specialized propensity prediction model and are based on a much larger extent of historical transaction data212than normally available to a conventional real-time fraud model, which must minimize the extent of data utilized to generate the fraud score to enable real-time decisions.

The fraud scoring system200described above thus combines the real-time fraud score with a batch propensity score. The batch propensity score is based on very large transaction datastore that would not be accessed directly in a fraud detection production environment given that fraud decisions need to be made in real-time. The advantage of the fraud scoring system200illustrated inFIG. 2is that it combines two very different data assets. The real-time fraud model focuses on a real-time summarization of the transaction stream into fraud features, and the propensity score provides a detailed look at the customer transaction history and types of customers to determine likely transactions. Thus, the streaming analytics of a conventional fraud model is periodically updated in the data stream with new, pertinent information to enable refined and improved fraud decisions. When the model accesses the propensity vectors for a customer, the model can utilize the raw propensity scores for MCC, or can create additional variables that can track real-time changes to propensity based on real-time scores and transaction history. The transaction history can be occurring in the stream between the scheduled batch updates of the propensity vectors, or on other schedules or timelines.

One of the tuning parameters in the fraud scoring system200is a batch update frequency. Tuning of the batch update frequency is dependent on a payment channel and the frequency of spending in the channel. As an example, a monthly batch update may be too infrequent as there are too many transactions occurring in the stream between propensity vector updates, thereby decreasing an accuracy of the propensity scores. On the other hand, too frequent updates, such as every few minutes, may not be operationally feasible due to the expense of updating the propensity vector database.

Accordingly, in some implementations, an approach is to have real-time adjustments to the static propensity scores, illustrated inFIG. 3, which demonstrates that the batch propensity vector database302can be supplemented with real-time propensity vector profiles304that form a propensity update matrix model306. This model relies on batch updates of batch propensity vector database302, and non-batch transaction propensity profiles304. Each transaction propensity profile304records and summarizes spending that occurs for a particular payment card between batch updates. The model then utilizes the batch propensity vector302and transaction propensity vector304to adjust, in real-time, the values of the propensity for a merchant category code (MCC) of the transaction308that is currently being scored by a propensity score310.

This approach takes into account situations in which a customer might have a high propensity for making an electronics purchase, but a much lowered probability of making two or more large electronics purchases during the batch period. In a situation where the batch updates are weekly, the real-time propensity profiles allow interim transactions to be reflected in the propensity scores to improve the accuracy of the propensity scores, resulting in a reduction in false positives. When the next batch update of the vector propensity matrix comes into the system, then the transaction propensity profiles are reset and the process repeats.

Computing the propensity of a transaction requires full transaction history over defined observation periods stored in an offline datastore which is used to compute the relationships between past spending and the probability of a transaction occurring within the subsequent future time-period. These propensities for payment card authorizations are most naturally based on the MCC, given the lack of ITEM level data in the typical payment card authorization process, but can be segmented more finely, such as using location information of the cardholder residence to understand spending such as MCC_IN_local area and MCC_OUT_of_local_area or further segment as MCC_OUT_of_local_area_more_than_$50, utilize item level information when available, etc. The definitions of these spending categories are used to refine the analytics based on the differences in spending in different MCC categories, as well as different meaningful binnings of dollar amount.

In some implementations, incorporating fraud propensity scores into the real-time stream includes developing batch propensity score models.FIG. 4depicts a process400in which cardholder histories of transaction data are used to create a variable record. The variable record summarizes the recency and frequency of spending in different categories such as merchant category codes (MCC), dollar amount, international spend, within or outside zip code area spending, etc.

In some implementations, these variables can be based on a number of factors including variables defined using finite spans of a transaction history over various different (“short”, “medium”, and “long”) timescales to enable creation of predictive variables associated with spending in the different categories in the next observation period. Training data is labeled based on whether a transaction in the target spending category occurred for the given customer during a certain performance period. For example, the fact of having a purchase with a particular MCC, particular MCC outside zip code area, particular MCC high dollar amount, etc. during the next 7 days, etc., may be used as a target label. Then, for each target a predictive model is constructed based on the variable records created by summarizing the transactions for each cardholder over an observation window. Different types of models can be used including but not limited to logistic regression, scorecard, or neural network. Given the large number of model targets, the models can be developed in parallel through parallelization of the model development process, or through use of analytics tools for large data structures, to allow for rapid creation of variable vectors and training of a large number of models for all defined spending categories in parallel. The system implementing the model construction shown inFIG. 4is preferably a batch system which can update, on a regular interval, the variable records and the targets to train models for use in follow-on phases of the system operation. The development of the different propensity models inFIG. 4is based on historical data based on known outcomes within a designated or predefined performance window. These models then form the basis for the batch scoring system. In some implementations, the system can include hundreds to thousands of models based on the number of propensity targets to be predicted, and the scoring system is well-suited for massively parallel batch scoring of customers through the use of data-intensive distributed computing platforms.

FIG. 5illustrates an example of propensity score generation, in which batch propensity scoring proceeds through the creation of customer variable records to be scored to make a prediction of whether the customer would transact in a propensity category for a particular propensity model. As depicted inFIG. 5, once the variable record is constructed, the cardholder is scored by the models illustrated inFIG. 4, and a propensity to purchase in that category of spend is produced. As an example, a single customer might have hundreds to thousands of these propensities associated with their recent transaction history, which can be used to indicate a likelihood that a customer will make a transaction and spend in a particular spending category.

The score generation illustrated inFIG. 5produces a propensity vector associated with each customer. These propensity vectors would then be sent to a production scoring system. The real-time data stream can be used in the operational system to allow the propensities to be used in conjunction with more recent transactions made in the stream (and not available to the batch update system), to inform the fraud system of the real-time propensity scores. The scores can also be used in conjunction with a fraud score so as to adjust the fraud score based on whether the customer would have likely made that transaction based on a deeper understanding of their transaction history in batch.FIG. 6illustrates an exemplary set of propensity vectors. Such deep analysis of the customer transaction history is not possible in the 1s to 10s of millisecond response times required by conventional fraud detection systems, and therefore these propensity vectors provide a unique and new detailed view of the individual customer to allow a reduction in false positives. These propensity vectors are then utilized in the fraud scoring system represented inFIG. 2.

The development or building of propensity models requires a few different considerations: A propensity target needs to be defined which includes a time-window for the prediction window, such as, for example, whether a purchase would be made in the next week in the merchant category code of restaurants, outside local area, for dollar amount greater than $200. Further, relevant variables for the batch propensity models need to be defined, which requires exhaustive mining of the transaction history to determine the appropriate mix of variables to support high-performing propensity models for the targets defined above.

Finally, relevant sub-populations, or segmentation for the propensity models, need to be developed. For example, sub-populations can include segments of early life, mature customers, high spend customers, risky spend customers, etc. Different models, each with its own variables, can be built for each customers' segment/subpopulation. The segmentation can either be static, or dynamic to be performed at the time of scoring the batch propensity vectors, as illustrated inFIG. 7.

Once these considerations are resolved, then the batch propensity models are built based for different sub-populations of cardholders. In some exemplary implementations, these can be calculated as follows:

First, a logistic regression model is trained (on the general population) to create a propensity of Cardholder X (pj(x)) having a transaction in a specified subcategory j:

where νirepresents an input variable vector associated with Cardholder X's transaction history and αiare the coefficients to be determined by the training algorithm. The input variables reflect different dimensionalities which make the individual cardholder unique. In particular, frequency of specific transactions, recency of specific transactions, and cyclical behaviors are captured by these input variables, among other transactional traits. For example, the models can incorporate data representing that a specific cardholder purchases groceries only once per week (and never more), always purchases fast food on Monday nights, makes 2-3 fast food purchases a week, purchases gas every fifth day or so, or recently purchased an airline ticket, and thus has a higher propensity of spending in restaurants in the next week, etc.

After weights a, are fitted by the training algorithm, the propensity score of each Cardholder X in the category j, is computed as:

To further improve the propensity score for the purposes of fraud prediction, a separate logistic regression model is trained on each of the sub-populations of cardholders for each of the same spending categories j. This sub-population propensity score, denoted as qj, is modeled and calculated in the same way as pj(but using only the observations that belong to the sub-population):

The sub-population propensity score can be computed for each cardholder, and represents the likelihood of a customer X transacting in subcategory j given that X is a member of the cardholder subpopulation Q.

FIG. 8, shows an example of how customer transation data is utilized to construct general population and subpopulation propensity models, through using historical data over different time scales—1 week, 5 weeks, and 12 weeks, in the example above—and then the performance window—in this example 1 week in the future—to determine whether the customer made the transaction. Historical data is used in this fashion to generate variables and target labels on which the general population and subcategory population propensity models are trained.

Once such propensity models are constructed, a variety of propensity-based quantities can be constructed and used by blending the propensity that a customer would have made the transaction observed with the base fraud score in real-time in the operational system. For example, general population and subcategory propensities can be combined through a ratio, which we call propensity ratio, rj(x) of Cardholder X in subcategory j, which is computed by taking the ratio of the general and sub-population propensity scores:

where summation in the denominator is done over different pre-defined non-overlapping cardholder subpopulations for which propensity models have been built and coefficients cjqare determined by risk and transactional frequency associated with the given spending category and subpopulation.

Finally, a model (such as another logistic regression model) can be built to blend the fraud score with the vector of propensity scores to predict the fraud probability of a given transaction in category i made by Cardholder X. For this model, we do linear blending in logodds space and choose a transformation of rj(x) which shows best linear correlation with Fraud logodds

where the βjiterms represent weights (contributions) of propensity score associated with propensity category j (could be propensity scores, ratios, or other mathematical manipulation of the propensity scores) to probability of fraudulent transaction in category i that an authorization decision as to whether to allow the transaction to proceed can be made by the financial institution that issued the payment card, or device.

By using the propensity ratio for a transaction that may not be the same category for a transaction currently being scored, second or third (or etc.) order predictors can be included in the final score. For example, if a high dollar jewelry purchase begets purchasing a home, while purchasing a home begets home improvement purchases, etc. This second-order predictor—a high dollar jewelry purchase begetting home improvement purchases—can be better included in a combined model of individual propensities.

The coefficients from this final logistic regression model are the blend coefficients used to produce the final blended fraud prediction score for each transaction:

The propensities rj(x) (the propensity vector) are stored in the batch propensity vector database for each cardholder x and get updated periodically (e.g. weekly).

Experiments can show that the approach outlined above results in relative performance gains in fraud detected, of on average of approximately 25% for various MCC+in/out of home area regions, on top of conventional fraud scores. In addition to the periodic batch updates to the propensity scores for each individual cardholder and the model weights, real-time updates to the propensity vectors for each individual cardholder can also be used to allow in the stream for the most accurate estimate of propensities. As an example, if a customer has a large propensity of transacting in a jewelry store and makes a purchase at a jewelry store on a Monday after the batch update of the propensity vector, he/she might have a much lower propensity to make a second or third transaction that week, and so the propensity associated with the jewelry transactions for the rest of the week would need to be modified in the stream between the batch propensity vector updates.

FIG. 9illustrates a system900to handle these updates. For each customer, a real-time propensity vector is constructed which stores transaction detail made between the batch propensity vector updates. This can then be used to scan the near-term transaction history between batch updates to determine if the high propensity transaction has already occurred earlier in the week and to adjust, ignore, or improve the batch propensity score.

There are various ways to adjust the batch propensity based on a near-term transaction history within the scope of this disclosure, but in some implementations, a set of models for each category is used to adjust the score based on subsequent transactions in the stream. An alternative, simpler, approach is to ignore the propensity score if the predicted transaction has already occurred, or if the propensity score is weak, so there is not a high propensity score, and to not utilize the scores at all for that type of category and not supplement the fraud model whatsoever.

For example: a cardholder may have a high propensity to transact at a grocery store in the upcoming week, but a very low propensity to transact at a grocery store more than once in the upcoming week. By utilizing the real-time propensity vector profile, the final propensity score for each transaction would more be more accurate than simply utilizing the same propensity score across all grocery store transactions and not reflecting any transactions that occur in the transaction stream between batch propensity vector database updates.

FIG. 10illustrates a system that includes both the batch propensity vector computation, and the real-time propensity vector profile which will inform the model in the stream of near-term transactions that occurred between batch updates for the customer that allows the propensities in the transaction category associated with the current transaction to be adjusted. This entire system allows deep inspection of the customer spending and what they are likely to legitimately spend on to help inform the fraud detection system and considerably reduce the false positives and customer impact.

Transaction categories can be defined by other merchant transaction attributes generating a multitude of different propensity scores. Customer segmentation can be utilized to create different propensity scores, and which can then be combined into a single propensity ratio. Further, customers' batch propensity scores can be adjusted in real-time in the stream through a customer transaction profile to reflect most recent purchase behavior and further improve the accuracy of propensity scores between batch propensity updates.