US6839682B1 — Predictive modeling of consumer financial behavio…

Claim 1

Independent Claim 120 words

A method of predicting financial behavior of consumers, comprising: obtaining a set of input transactions for a plurality of consumers with respect to a plurality of merchants; defining at least one merchant segment, each merchant being associated with at least one of the defined merchant segments; and for at least one consumer, applying the input transactions of the consumer by a computer to each of at least one merchant segment predictive model, each merchant segment predictive model defining for a merchant segment a prediction function between input transactions in a past time interval and financial behavior in a subsequent time interval, to produce for each consumer a predicted behavior in each of at least a subset of the merchant segments.

Claim 2

Independent Claim 120 words

A method of predicting financial behavior of consumers, comprising: obtaining a set of input transactions for a plurality of consumers with respect to a plurality of merchants; defining at least one merchant segment, each merchant being associated with at least one of the defined merchant segments; and for at least one consumer, applying the input transactions of the consumer by a computer to each of at least one merchant segment predictive model, each merchant segment predictive model defining for a merchant segment a prediction function between input transactions in a past time interval and financial behavior in a subsequent time interval, to produce for each consumer a predicted behavior in each of at least a subset of the merchant segments.

Claim 3

Dependent Claim 19 words

The method of claim 1 , wherein the predicted behavior comprises a likelihood of positive response to an offer.

Claim 4

Dependent Claim 19 words

The method of claim 1 , wherein the predicted behavior comprises a spending level with respect to a merchant.

Claim 5

Dependent Claim 51 words

The method of claim 1 , further comprising: generating a consumer vector for each of at least a subset of the consumers; generating a merchant vector for each of at least a subset of the merchants; wherein defining at least one merchant segment comprises performing supervised segmentation on the merchant vectors.

Claim 6

Dependent Claim 21 words

The method of claim 4 , wherein performing supervised segmentation comprises applying a learning vector quantization algorithm to the merchant vectors.

Claim 7

Dependent Claim 81 words

The method of claim 4 , wherein performing supervised segmentation comprises: initializing a set of segment vectors; accepting at least one segment label for at least one of the merchants; and for each of at least a subset of the labeled merchants: selecting at least one segment vector for a merchant having a merchant vector; determining whether the selected segment vector matches the segment label for the merchant; and responsive to the determination, adjusting zero or more of the segment vectors.

Claim 8

Dependent Claim 32 words

The method of claim 6 , wherein selecting at least one segment vector for a merchant comprises selecting a segment vector that is closest to the merchant vector corresponding to the merchant.

Claim 9

Dependent Claim 35 words

The method of claim 6 , wherein selecting at least one segment vector for a merchant comprises selecting at least one segment vector having a tolerance range that includes the value of the merchant vector.

Claim 10

Dependent Claim 103 words

The method of claim 1 , further comprising: training a predictive model using the predicted behavior of a plurality of consumers in at least a subset of the merchant segments, and additional observed behavior for the plurality of consumers with regard to a target segment not included in the subset of merchant segments; and for at least one target consumer: providing as input to the trained predictive model predicted behavior of the target consumer in at least a subset of the merchant segments; and obtaining from the trained predictive model a predicted behavior of the target consumer with respect to the target segment.

Claim 11

Dependent Claim 34 words

The method of claim 1 , further comprising: for at least one consumer, associating the consumer with the merchant segment for which the consumer had the highest predicting spending relative to other merchant segments.

Claim 12

Dependent Claim 91 words

The method of claim 1 , further comprising: generating a consumer vector for each of at least a subset of the consumers; generating a merchant vector for each of at least a subset of the merchants; for at least one merchant segment, determining a segment vector as a summary vector of merchant vectors of merchants associated with the segment; and for at least one consumer, associating the consumer with the merchant segment having the greatest dot product between the segment vector of the segment and a consumer vector of the consumer.

Claim 13

Dependent Claim 85 words

The method of claim 1 , further comprising: for at least one merchant segment: ranking the consumers by their predicted spending in the merchant segment; and determining for at least one consumer a percentile ranking in the merchant segment; and for each consumer: determining the merchant segment in which the consumer's percentile ranking is the highest, to uniquely associate each consumer with one merchant segment; and for at least one merchant segment, determining summary transaction statistics for the consumers uniquely associated with the merchant segment.

Claim 14

Dependent Claim 64 words

The method of claim 1 , further comprising: for at least one merchant segment: ranking the consumers by their predicted spending in the merchant segment; determining for at least one consumer a percentile ranking in the merchant segment; selecting as a population, the consumers having a percentile ranking in excess of predetermined percentile threshold; and determining summary transaction statistics for selected population of consumers.

Claim 15

Dependent Claim 49 words

The method of claim 1 , further comprising: establishing for at least one merchant in the transaction data a merchant vector; and updating the merchant vector of at least one merchant relative to the merchant vectors of other merchants according to co-occurrences of each merchant in the transaction data.

Claim 16

Dependent Claim 34 words

The method of claim 14 , further comprising: updating the merchant vector of at least one merchant based upon an unexpected amount deviation in a frequency of co-occurrence of the merchant with other merchants.

Claim 17

Dependent Claim 115 words

The method of claim 14 , further comprising: determining a co-occurrence frequency for at least one merchant with at least one other merchant in the transaction data; determining for at least one pair of merchants, a relationship strength between the pair of merchants based on how much the determined co-occurrence frequency deviates from an expected co-occurrence frequency; for at least one pair of merchant vectors, mapping the relationship strength into a vector space as a desired dot product between respective merchant vectors the merchants in the pair; and updating at least one merchant vector so that the actual dot products between each pair of merchant vectors approximate the desired dot product between the merchant vectors.

Claim 18

Dependent Claim 106 words

The method of claim 16 , wherein determining for at least one pair of merchants a relationship strength between the pair of merchants further comprises: determining the relationship strength by r ij = T ij - T ^ ij σ ij ≈ T ij - T ^ ij T ^ ij where r ij is the relationship strength between merchant i and merchant j in a pair of merchants; T ij is the actual co-occurrence frequency of merchant i and merchant j in the transaction data; and {circumflex over (T)} ij is the expected co-occurrence frequency of merchant i and merchant j in the transaction data.

Claim 19

Dependent Claim 104 words

The method of claim 16 , wherein determining for at least one pair of merchants a relationship strength between the pair of merchants further comprises: determining the relationship strength by r ij =sign( T ij −{circumflex over (T)} ij )·√{square root over (2ln λ)} where r ij is the relationship strength between merchant i and merchant j in a pair of merchants; λ is a log-likelihood ratio; T ij is the actual co-occurrence frequency of merchant i and merchant j in the transaction data; and {circumflex over (T)} ij is the expected co-occurrence frequency of merchant i and merchant j in the transaction data.

Claim 20

Dependent Claim 137 words

The method of claim 16 , wherein determining for at least one pair of merchants a relationship strength between the pair of merchants further comprises: determining the relationship strength by r ij = sign ⁡ ( T ij - T ^ ij ) · 2 ⁢ ln ⁢ ⁢ λ T ^ ij = sign ⁡ ( T ij - T ^ ij ) · 2 ⁢ ln ⁢ ⁢ λ · T ^ ij 1 4 where r ij is the relationship strength between merchant i and merchant j in a pair of merchants; λ is a log-likelihood ratio; T ij is the actual co-occurrence frequency of merchant i and merchant j in the transaction data; and {circumflex over (T)} ij is the expected co-occurrence frequency of merchant i and merchant j in the transaction data.

Claim 21

Dependent Claim 65 words

The method of claim 16 , wherein updating at least one merchant vector so that the actual dot products between the at least one pair of merchant vectors approximates the desired dot product between the merchant vectors comprises a gradient descent update that updates the merchant vectors according to whether the actual dot product between them is greater or lesser than the desired dot product.

Claim 22

Dependent Claim 83 words

The method of claim 16 , wherein updating at least one merchant vector so that the actual dot products between the at least one pair of merchant vectors approximates the desired dot product between the merchant vectors comprises determining for at least one merchant vector an error weighted average of the desired positions of the merchant vector from current position of at least one other merchant vector and the desired dot product between the merchant vector and at least one other merchant vector.

Claim 23

Dependent Claim 104 words

The method of claim 1 , further comprising: generating a consumer vector for each of at least a subset of the consumers; generating a merchant vector for each of at least a subset of the merchants; determining for at least one merchant name in the transaction data a merchant vector; clustering the merchant vectors to form a plurality of merchant segments, wherein at least one merchant vector is associated with one and only one merchant segment; and for at least one merchant segment, determining from the transactions of consumers at the associated merchants of the merchant, statistical measures of consumer transactions in the segment.

Claim 24

Dependent Claim 43 words

The method of claim 1 , further comprising: selecting a plurality of consumers associated with at least one merchant segment, the selected plurality selected according to their predicted spending in the merchant segment; and providing promotional offers to the selected plurality of consumers.

Claim 25

Dependent Claim 39 words

The method of claim 1 , further comprising: training at least one of the merchant segment predictive models to predict spending in a predicted time period based upon transaction statistics of the consumer's transactions in a past time period.

Claim 26

Dependent Claim 50 words

The method of claim 24 , wherein the transaction statistics comprises variables describing the recency of the consumer's transactions in one or more merchant segments, the frequency of the consumer's transactions in one or more merchant segments, and the amount of the consumer's transactions in one or more merchant segments.

Claim 27

Independent Claim 99 words

A system for predicting financial behavior of consumers, comprising: a database for storing a set of input transactions for a plurality of consumers with respect to a plurality of merchants; at least one merchant segment, each merchant being associated with at least one of the defined merchant segments; at least one merchant segment predictive model, for defining for a merchant segment a prediction function between input transactions in a past time interval and financial behavior in a subsequent time interval, to produce for each consumer a predicted behavior in each of at least a subset of the merchant segments.

Claim 28

Independent Claim 99 words

A system for predicting financial behavior of consumers, comprising: a database for storing a set of input transactions for a plurality of consumers with respect to a plurality of merchants; at least one merchant segment, each merchant being associated with at least one of the defined merchant segments; at least one merchant segment predictive model, for defining for a merchant segment a prediction function between input transactions in a past time interval and financial behavior in a subsequent time interval, to produce for each consumer a predicted behavior in each of at least a subset of the merchant segments.

Claim 29

Dependent Claim 19 words

The system of claim 26 , wherein the predicted behavior comprises a likelihood of positive response to an offer.

Claim 30

Dependent Claim 19 words

The system of claim 26 , wherein the predicted behavior comprises a spending level with respect to a merchant.

Claim 31

Dependent Claim 75 words

The system of claim 26 , further comprising: a merchant vector build module, coupled to the database, for generating a merchant vector for each of at least a subset of the merchants; a consumer vector build module, coupled to the database, for generating a consumer vector for each of at least a subset of the consumers; and a segmentation module, coupled to the merchant vector build module, for performing supervised segmentation on the merchant vectors.

Claim 32

Dependent Claim 25 words

The system of claim 29 , wherein the at least one merchant segment predictive model applies a learning vector quantization algorithm to the merchant vectors.

Claim 33

Dependent Claim 69 words

The system of claim 26 , wherein: the merchant vector build module determines, for at least one merchant segment, a segment vector as a summary vector of merchant vectors of merchants associated with the segment; and the consumer vector build module associates at least one consumer with the merchant segment having the greatest dot product between the segment vector of the segment and a consumer vector of the consumer.

Claim 34

Dependent Claim 136 words

A computer-readable medium comprising computer-readable code for predicting financial behavior of consumers, the computer-readable medium comprising: computer-readable code adapted to obtain a set of input transactions for a plurality of consumers with respect to a plurality of merchants; computer-readable code adapted to define at least one merchant segment, each merchant being associated with at least one of the defined merchant segments; and computer-readable code adapted to, for at least one consumer, apply the input transactions of the consumer to each of at least one merchant segment predictive model, each merchant segment predictive model defining for a merchant segment a prediction function between input transactions in a past time interval and financial behavior in a subsequent time interval, to produce for each consumer a predicted behavior in each of at least a subset of the merchant segments.

Claim 35

Dependent Claim 136 words

A computer-readable medium comprising computer-readable code for predicting financial behavior of consumers, the computer-readable medium comprising: computer-readable code adapted to obtain a set of input transactions for a plurality of consumers with respect to a plurality of merchants; computer-readable code adapted to define at least one merchant segment, each merchant being associated with at least one of the defined merchant segments; and computer-readable code adapted to, for at least one consumer, apply the input transactions of the consumer to each of at least one merchant segment predictive model, each merchant segment predictive model defining for a merchant segment a prediction function between input transactions in a past time interval and financial behavior in a subsequent time interval, to produce for each consumer a predicted behavior in each of at least a subset of the merchant segments.

Claim 36

Dependent Claim 20 words

The computer-readable medium of claim 32 , wherein the predicted behavior comprises a likelihood of positive response to an offer.

Claim 37

Dependent Claim 20 words

The computer-readable medium of claim 32 , wherein the predicted behavior comprises a spending level with respect to a merchant.

Claim 38

Dependent Claim 69 words

The computer-readable medium of claim 32 , further comprising: computer-readable code adapted to generate a consumer vector for each of at least a subset of the consumers; computer-readable code adapted to generate a merchant vector for each of at least a subset of the merchants; wherein the computer-readable code adapted to define at least one merchant segment comprises computer-readable code adapted to perform supervised segmentation on the merchant vectors.

Claim 39

Dependent Claim 31 words

The computer-readable medium of claim 35 , wherein the computer-readable code adapted to perform supervised segmentation comprises computer-readable code adapted to apply a learning vector quantization algorithm to the merchant vectors.

Claim 40

Dependent Claim 99 words

The computer-readable medium of claim 35 , wherein the computer-readable code adapted to performing supervised segmentation comprises: computer-readable code adapted to initialize a set of segment vectors; computer-readable code adapted to accept at least one segment label for at least one of the merchants; and computer-readable code adapted to, for each of at least a subset of the labeled merchants: select at least one segment vector for a merchant having a merchant vector; determine whether the selected segment vector matches the segment label for the merchant; and responsive to the determination, adjust zero or more of the segment vectors.

Claim 41

Dependent Claim 42 words

The computer-readable medium of claim 37 , wherein the computer-readable code adapted to select at least one segment vector for a merchant comprises computer-readable code adapted to select a segment vector that is closest to the merchant vector corresponding to the merchant.

Claim 42

Dependent Claim 45 words

The computer-readable medium of claim 37 , wherein the computer-readable code adapted to select at least one segment vector for a merchant comprises computer-readable code adapted to select at least one segment vector having a tolerance range that includes the value of the merchant vector.

Claim 43

Dependent Claim 112 words

The computer-readable medium of claim 32 , further comprising: computer-readable code adapted to train a predictive model using the predicted behavior of a plurality of consumers in at least a subset of the merchant segments, and additional observed behavior for the plurality of consumers with regard to a target segment not included in the subset of merchant segments; and computer-readable code adapted to, for at least one target consumer: provide as input to the trained predictive model predicted behavior of the target consumer in at least a subset of the merchant segments; and obtain from the trained predictive model a predicted behavior of the target consumer with respect to the target segment.

Claim 44

Dependent Claim 39 words

The computer-readable medium of claim 32 , further comprising: computer-readable code adapted to, for at least one consumer, associate the consumer with the merchant segment for which the consumer had the highest predicting spending relative to other merchant segments.

Claim 45

Dependent Claim 108 words

The computer-readable medium of claim 32 , further comprising: computer-readable code adapted to generate a consumer vector for each of at least a subset of the consumers; computer-readable code adapted to generate a merchant vector for each of at least a subset of the merchants; computer-readable code adapted to, for at least one merchant segment, determine a segment vector as a summary vector of merchant vectors of merchants associated with the segment; and computer-readable code adapted to, for at least one consumer, associate the consumer with the merchant segment having the greatest dot product between the segment vector of the segment and a consumer vector of the consumer.

Claim 46

Dependent Claim 94 words

The computer-readable medium of claim 32 , further comprising: computer-readable code adapted to, for at least one merchant segment: rank the consumers by their predicted spending in the merchant segment; and determine for at least one consumer a percentile ranking in the merchant segment; and computer-readable code adapted to, for each consumer: determine the merchant segment in which the consumer's percentile ranking is the highest, to uniquely associate each consumer with one merchant segment; and for at least one merchant segment, determine summary transaction statistics for the consumers uniquely associated with the merchant segment.

Claim 47

Dependent Claim 69 words

The computer-readable medium of claim 32 , further comprising: computer-readable code adapted to, for at least one merchant segment: rank the consumers by their predicted spending in the merchant segment; determine for at least one consumer a percentile ranking in the merchant segment; select as a population, the consumers having a percentile ranking in excess of predetermined percentile threshold; and determine summary transaction statistics for selected population of consumers.

Claim 48

Dependent Claim 58 words

The computer-readable medium of claim 32 , further comprising: computer-readable code adapted to establish for at least one merchant in the transaction data a merchant vector; and computer-readable code adapted to update the merchant vector of at least one merchant relative to the merchant vectors of other merchants according to co-occurrences of each merchant in the transaction data.

Claim 49

Dependent Claim 39 words

The computer-readable medium of claim 45 , further comprising: computer-readable code adapted to update the merchant vector of at least one merchant based upon an unexpected amount deviation in a frequency of co-occurrence of the merchant with other merchants.

Claim 50

Dependent Claim 132 words

The computer-readable medium of claim 45 , further comprising: computer-readable code adapted to determine a co-occurrence frequency for at least one merchant with at least one other merchant in the transaction data; computer-readable code adapted to determine for at least one pair of merchants, a relationship strength between the pair of merchants based on how much the determined co-occurrence frequency deviates from an expected co-occurrence frequency; computer-readable code adapted to, for at least one pair of merchant vectors, map the relationship strength into a vector space as a desired dot product between respective merchant vectors the merchants in the pair; and computer-readable code adapted to update at least one merchant vector so that the actual dot products between each pair of merchant vectors approximates the desired dot product between the merchant vectors.

Claim 51

Dependent Claim 116 words

The computer-readable medium of claim 47 , wherein the computer-readable code adapted to determine for at least one pair of merchants a relationship strength between the pair of merchants further comprises: computer-readable code adapted to determine the relationship strength by r ij = T ij - T ^ ij σ ij ≈ T ij - T ^ ij T ^ ij where r ij is the relationship strength between merchant i and merchant j in a pair of merchants; T ij is the actual co-occurrence frequency of merchant i and merchant j in the transaction data; and {circumflex over (T)} ij is the expected co-occurrence frequency of merchant i and merchant j in the transaction data.

Claim 52

Dependent Claim 114 words

The computer-readable medium of claim 47 , wherein the computer-readable code adapted to determine for at least one pair of merchants a relationship strength between the pair of merchants further comprises: computer-readable code adapted to determine the relationship strength by r ij =sign( T ij −{circumflex over (T)} ij )·√{square root over (2ln λ)} where r ij is the relationship strength between merchant i and merchant j in a pair of merchants; λ is a log-likelihood ratio; T ij is the actual co-occurrence frequency of merchant i and merchant j in the transaction data; and {circumflex over (T)} ij is the expected co-occurrence frequency of merchant i and merchant j in the transaction data.

Claim 53

Dependent Claim 147 words

The computer-readable medium of claim 47 , wherein the computer-readable code adapted to determine for at least one pair of merchants a relationship strength between the pair of merchants further comprises: computer-readable code adapted to determine the relationship strength by r ij = sign ⁡ ( T ij - T ^ ij ) · 2 ⁢ ln ⁢ ⁢ λ T ^ ij = sign ⁡ ( T ij - T ^ ij ) · 2 ⁢ ln ⁢ ⁢ λ · T ^ ij 1 4 where r ij is the relationship strength between merchant i and merchant j in a pair of merchants; λ is a log-likelihood ratio; T ij is the actual co-occurrence frequency of merchant i and merchant j in the transaction data; and {circumflex over (T)} ij is the expected co-occurrence frequency of merchant i and merchant j in the transaction data.

Claim 54

Dependent Claim 76 words

The computer-readable medium of claim 47 , wherein the computer-readable code adapted to update at least one merchant vector so that the actual dot products between the at least one pair of merchant vectors approximates the desired dot product between the merchant vectors comprises computer-readable code adapted to perform a gradient descent update that updates the merchant vectors according to whether the actual dot product between them is greater or lesser than the desired dot product.

Claim 55

Dependent Claim 93 words

The computer-readable medium of claim 47 , wherein the computer-readable code adapted to update at least one merchant vector so that the actual dot products between the at least one pair of merchant vectors approximates the desired dot product between the merchant vectors comprises computer-readable code adapted to determine for at least one merchant vector an error weighted average of the desired positions of the merchant vector from current position of at least one other merchant vector and the desired dot product between the merchant vector and at least one other merchant vector.

Claim 56

Dependent Claim 125 words

The computer-readable medium of claim 32 , further comprising: computer-readable code adapted to generate a consumer vector for each of at least a subset of the consumers; computer-readable code adapted to generate a merchant vector for each of at least a subset of the merchants; computer-readable code adapted to determine for at least one merchant name in the transaction data a merchant vector; computer-readable code adapted to cluster the merchant vectors to form a plurality of merchant segments, wherein at least one merchant vector is associated with one and only one merchant segment; and computer-readable code adapted to, for at least one merchant segment, determine from the transactions of consumers at the associated merchants of the merchant, statistical measures of consumer transactions in the segment.

Claim 57

Dependent Claim 52 words

The computer-readable medium of claim 32 , further comprising: computer-readable code adapted to select a plurality of consumers associated with at least one merchant segment, the selected plurality selected according to their predicted spending in the merchant segment; and computer-readable code adapted to provide promotional offers to the selected plurality of consumers.

Claim 58

Dependent Claim 44 words

The computer-readable medium of claim 32 , further comprising: computer-readable code adapted to train at least one of the merchant segment predictive models to predict spending in a predicted time period based upon transaction statistics of the consumer's transactions in a past time period.

Claim 59

Dependent Claim 51 words

The computer-readable medium of claim 55 , wherein the transaction statistics comprises variables describing the recency of the consumer's transactions in one or more merchant segments, the frequency of the consumer's transactions in one or more merchant segments, and the amount of the consumer's transactions in one or more merchant segments.

Annuity Stage	Window Opens	Payment Deadline	Surcharge Period Until	Estimated Fee	Status
3.5 Years (Year 4)	2008-01-04	2008-07-05	2009-01-04	$2000.00	Window Closed / Lapsed
7.5 Years (Year 8)	2012-01-04	2012-07-05	2013-01-04	$3760.00	Window Closed / Lapsed
11.5 Years (Year 12)	2016-01-04	2016-07-05	2017-01-04	$7700.00	Window Closed / Lapsed

Predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching

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