Access Transaction Data with
Temporal Behavior Maps
Shafi Rahman
Director, Analytic Science
FICO
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Patent Pending
Forward-Looking Statements
Product roadmaps and similar marketing materials
should be considered forward looking and
subject to future change at FICO’s discretion.
Future functionality, features or enhancements as shown are
FICO’s current projections of the product direction,
but are not specific commitments or obligations.
2
© 2014 Fair Isaac Corporation. Confidential.
Transaction Data Is Every Where
Improves Decisions Across Businesses
Cash
Grocery
Utility Bill
Cash Transfer
Call Center
Web Access
Payment
Credit Card Bill
Time
3
© 2014 Fair Isaac Corporation. Confidential.
Using Transaction Data For Business Decisions
Predicting Attrition Using Credit Card Transaction Data
► Predicting
card closure
Masterfile Data
(Model A)
+Tx Data
(Model B)
0.73
0.75
Recall
20.3%
24.3%
Precision
8.4%
9.9%
AUROC
𝑅𝑒𝑐𝑎𝑙𝑙 =
# 𝑐𝑙𝑜𝑠𝑒𝑑 𝑐𝑎𝑠𝑒𝑠 𝑖𝑛 𝑡𝑜𝑝 5%
𝑇𝑜𝑡𝑎𝑙 # 𝑜𝑓 𝑐𝑙𝑜𝑠𝑒𝑑 𝑐𝑎𝑠𝑒
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
4
# 𝑐𝑙𝑜𝑠𝑒𝑑 𝑐𝑎𝑠𝑒𝑠 𝑖𝑛 𝑡𝑜𝑝 5%
# 𝑜𝑓𝑐𝑎𝑠𝑒𝑠 𝑖𝑛 𝑡𝑜𝑝 5%
© 2014 Fair Isaac Corporation. Confidential.
Predicting Attrition Using Credit Card Transaction Data
5
Build Predictive
Model with
Transaction Data
Deploy Model
Begin Usage for
Decisions
(Total time to
value)
Traditional Transaction
Approach
5 months
7 months
12 months
Temporal Behavior Maps
3 months
2 months
5 months
© 2014 Fair Isaac Corporation. Confidential.
How To Improve Time To Value
By Using A Reusable Transaction Variable Library
Efficient
Scalable
Portable
6
© 2014 Fair Isaac Corporation. Confidential.
► Quickly
compute transaction variables
► Consume
new data feeds and data elements
► Calculate new characteristics
► Use
same library in modeling and production
► Easy implementation
Risk Prediction Using Masterfile Data
The Behavior Risk Scores Are Quite Predictive
Behavior Score
719
683
667
622
Jan 1
Feb 1
Mar 1
Apr 1
$500 Cash
adv
Overlimit
Min payment made
Min payment
made
Customer Behavior
7
© 2014 Fair Isaac Corporation. Confidential.
Delinquent
Bureau hit
Timing of Transactions Contains Vital Information
Which Masterfile Lacks
Summarized Data
Cardholder
#1
Cardholder
#2
8
© 2014 Fair Isaac Corporation. Confidential.
Transaction Data
Cash
Merchandise
Better and More Timely Risk Detection
Using Transaction Data
Behavior Score
719
683
667
622
Jan 1
Feb 1
Mar 1
Apr 1
722
Transaction
Score
708 690
665
$500 Cash
adv
620
Overlimit
Min payment made
Min payment
made
Customer Behavior
9
© 2014 Fair Isaac Corporation. Confidential.
600
580
Delinquent
Bureau hit
Challenges Working With Tx Data
► Iterative
Computation
► Recursive Approximation
10
© 2014 Fair Isaac Corporation. Confidential.
Computing Characteristics
Historical Transaction Data of John Doe
01/03/14
Restaurant
$
50.00
01/03/14
Movies
$
20.00
01/10/14
Jewelry
$
520.00
01/17/14
Clothes
$
120.00
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
100.00
02/27/14
Car Rental
$
250.00
# of fields vary from few to few dozens
11
© 2014 Fair Isaac Corporation. Confidential.
𝐶ℎ𝑎𝑟1 =
$ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 30 𝑑𝑎𝑦𝑠
$ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 60 𝑑𝑎𝑦𝑠
𝐶ℎ𝑎𝑟2 = $ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 15 𝑑𝑎𝑦𝑠
# of chars vary from 10 to 10,000
Computing Characteristics Using Iterative Approach
𝐶ℎ𝑎𝑟1 =
$ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 30 𝑑𝑎𝑦𝑠
$ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 60 𝑑𝑎𝑦𝑠
Compute on 03/01/2014
𝐶ℎ𝑎𝑟1 =
12
01/03/14
Restaurant
$
50.00
01/03/14
Movies
$
20.00
01/10/14
Jewelry
$
520.00
01/17/14
Clothes
$
120.00
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
100.00
02/27/14
Car Rental
$
250.00
© 2014 Fair Isaac Corporation. Confidential.
=
$ 𝑠𝑝𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 01/31 𝑡𝑜 03/01
$ 𝑠𝑝𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 01/01 𝑡𝑜 03/01
$ 250 + $ 100 + $ 250
$ 50 + $20 + $520 + $ 120 + $630 + $ 250 + $ 100 + $ 250
= 0.309
Unable to leverage computation
already done for numerator
Accounting for New Transactions
𝐶ℎ𝑎𝑟1 =
$ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 30 𝑑𝑎𝑦𝑠
$ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 60 𝑑𝑎𝑦𝑠
01/03/14
Restaurant
$
50.00
01/03/14
Movies
$
20.00
01/10/14
Jewelry
$
520.00
01/17/14
Clothes
$
120.00
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
100.00
02/27/14
Car Rental
$
250.00
Compute on 03/07/2014
𝐶ℎ𝑎𝑟1 =
=
$ 𝑠𝑝𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 02/06 𝑡𝑜 03/07
$ 𝑠𝑝𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 01/07 𝑡𝑜 03/07
$ 100 + $ 250 + $ 780 + $ 40
$520 + $ 120 + $630 + $ 250 + $ 100 + $ 250 + $ 780 + $ 40
= 0.435
T = O(p), p = # of transactions
Unable to leverage computation
already done on 03/01/2014
13
03/07/14
Hotel Expense
$
780.00
03/07/14
Taxi
$
40.00
© 2014 Fair Isaac Corporation. Confidential.
Handling More than One Characteristics
𝐶ℎ𝑎𝑟2 = $ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 15 𝑑𝑎𝑦𝑠
14
01/03/14
Restaurant
$
50.00
01/03/14
Movies
$
20.00
01/10/14
Jewelry
$
520.00
01/17/14
Clothes
$
120.00
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
100.00
02/27/14
Car Rental
$
250.00
© 2014 Fair Isaac Corporation. Confidential.
Compute on 03/01/2014
𝐶ℎ𝑎𝑟 2 = $ 100 + $ 250
= $ 350
T = O(n), n = # of variables
Unable to leverage computation
already done for Char1
Unable to Leverage Any Previous Computation
𝐶ℎ𝑎𝑟2 = $ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 15 𝑑𝑎𝑦𝑠
15
01/03/14
Restaurant
$
50.00
01/03/14
Movies
$
20.00
01/10/14
Jewelry
$
520.00
01/17/14
Clothes
$
120.00
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
100.00
02/27/14
Car Rental
$
250.00
03/07/14
Hotel Expense
$
780.00
03/07/14
Taxi
$
40.00
© 2014 Fair Isaac Corporation. Confidential.
Compute on 03/07/2014
𝐶ℎ𝑎𝑟 2 = $ 100 + $ 250 + $ 780 + $ 40
= $ 1170
Alternative Approach for Computing Characteristics
Recursive Approximation Technique
𝐶ℎ𝑎𝑟2 = $ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 15 𝑑𝑎𝑦𝑠
16
01/03/14
Restaurant
$
50.00
01/03/14
Movies
$
20.00
01/10/14
Jewelry
$
520.00
01/17/14
Clothes
$
120.00
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
100.00
02/27/14
Car Rental
$
250.00
03/07/14
Hotel Expense
$
780.00
03/07/14
Taxi
$
40.00
© 2014 Fair Isaac Corporation. Confidential.
Compute on 03/07/2014
1
Char2 = ∗ Char2 03/01 +($ 40 + $ 780)
λ
Where λ is a decay factor
How Recursive Approximation Technique Works?
► Consider:
1
𝑆𝑡+1 = 𝑥𝑛𝑒𝑤 + 𝑆𝑡
λ
∞
𝑆𝑡 =
𝑛=0
1
1
1
1
𝑥
=
𝑥
+
𝑥
+
𝑥
+
𝑥 + ⋯
0
λ𝑛 𝑛
λ 1 λ2 2 λ3 3
1
1
1
1
𝑆𝑡+1 = 𝑥𝑛𝑒𝑤 + 𝑥0 + 2 𝑥1 + 3 𝑥2 + 4 𝑥3 + ⋯
λ
λ
λ
λ
𝑆𝑡+1 = 𝑥𝑛𝑒𝑤 +
17
© 2014 Fair Isaac Corporation. Confidential.
1
1
1
1
𝑥0 + 𝑥1 + 2 𝑥2 + 3 𝑥3 + ⋯
λ
λ
λ
λ
Limitations of Recursive Approximation Technique
Not suitable for
Suitable for
Black Box
Models
Rules based
solutions
Triggers
Interpretable
values
We need a third approach to overcome these limitations
18
© 2014 Fair Isaac Corporation. Confidential.
Overcoming Challenges Using Temporal
Behavior Maps (TBM)
19
© 2014 Fair Isaac Corporation. Confidential.
Temporal Behavior Maps (TBM)
A Simple Solution to Address the Challenges of Working with Tx Data
Efficient
Accurate
Scalable
20
© 2014 Fair Isaac Corporation. Confidential.
► Computationally
► Palatable
cheap and low latency
values can be used across use
cases
► Consumes
new data feeds and data elements
► Easy to add new characteristic computation
Portable
► Use
Multiple
modes
► Supports
same library in modeling and production
mode
batch, incremental and real-time
Temporal Behavior Maps
Key Idea: Reuse Previous Computations
► Step
01/03/14
Restaurant
$
50.00
3
01/03/14
Movies
$
20.00
3
01/10/14
Jewelry
$
520.00
10
01/17/14
Clothes
$
120.00
17
01/24/14
Flight Booking
$
630.00
24
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
02/27/14
Car Rental
$
► Step
3
$ 70
10
$ 520
17
$ 120
34
24
$ 630
100.00
58
34
$ 250
250.00
58
58
$ 350
Aggregation step
1: Discretize time
Period
21
© 2014 Fair Isaac Corporation. Confidential.
2: Store pre-computed
summary of the transactions
Base Map
Computing Characteristics Using TBM
Generation Step
𝐶ℎ𝑎𝑟1 =
$ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 30 𝑑𝑎𝑦𝑠
$ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 60 𝑑𝑎𝑦𝑠
01/03/14
Restaurant
$
50.00
01/03/14
Movies
$
20.00
01/10/14
Jewelry
$
520.00
01/17/14
Clothes
$
120.00
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
100.00
02/27/14
Car Rental
$
250.00
► Use
Base Map
[3:$70, 10:$520, 17: $120, 24:$630,
34: $250, 58: $350]
On 03/01/2014
𝐶ℎ𝑎𝑟1 =
=
$ 𝑠𝑝𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 𝑝𝑒𝑟𝑖𝑜𝑑 31 𝑡𝑜 60
$ 𝑠𝑝𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 𝑝𝑒𝑟𝑖𝑜𝑑 1 𝑡𝑜 60
$ 250 + $ 350
$ 70 + $520 + $ 120 + $630 + $ 250 + $ 350
= 0.309
22
© 2014 Fair Isaac Corporation. Confidential.
Computing Multiple Characteristics Using TBM
Much Cheaper Due to Base Maps
𝐶ℎ𝑎𝑟2 = $ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 15 𝑑𝑎𝑦𝑠
01/03/14
Restaurant
$
50.00
01/03/14
Movies
$
20.00
01/10/14
Jewelry
$
520.00
01/17/14
Clothes
$
120.00
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
100.00
02/27/14
Car Rental
$
250.00
No need to traverse through Tx
data again
23
© 2014 Fair Isaac Corporation. Confidential.
► Use
Base Map
[3:$70, 10:$520, 17: $120, 24:$630,
34: $250, 58: $350]
On 03/01/2014
𝐶ℎ𝑎𝑟2 = $ 𝑠𝑝𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 𝑝𝑒𝑟𝑖𝑜𝑑 46 𝑡𝑜 60
= $ 350
Accounting for New Transactions
► Only
need to use the new transactions
01/03/14
Restaurant
$
50.00
3
01/03/14
Movies
$
20.00
3
01/10/14
Jewelry
$
520.00
10
01/17/14
Clothes
$
120.00
17
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
02/27/14
Cash Withdrawal
02/27/14
Car Rental
► Retrieve
► Update
Old Base Map
the Base Map
3
$ 70
3
$ 70
10
$ 520
10
$ 520
17
$ 120
17
$ 120
24
24
$ 630
24
$ 630
250.00
34
34
$ 250
34
$ 250
$
100.00
58
58
$ 350
58
$ 350
$
250.00
58
66
$ 820
Aggregation step
24
03/07/14
Hotel Expense
$
780.00
66
03/07/14
Taxi
$
40.00
66
© 2014 Fair Isaac Corporation. Confidential.
Updating Characteristics
Accounting for New Transactions
𝐶ℎ𝑎𝑟1 =
25
$ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 30 𝑑𝑎𝑦𝑠
$ 𝑠𝑝𝑒𝑛𝑡 𝑖𝑛 𝑙𝑎𝑠𝑡 60 𝑑𝑎𝑦𝑠
01/03/14
Restaurant
$
50.00
01/03/14
Movies
$
20.00
01/10/14
Jewelry
$
520.00
01/17/14
Clothes
$
120.00
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
100.00
02/27/14
Car Rental
$
250.00
03/07/14
Hotel Expense
$
780.00
03/07/14
Taxi
$
40.00
© 2014 Fair Isaac Corporation. Confidential.
► Use
updated Base Map
[3:$70, 10:$520, 17: $120, 24:$630,
34: $250, 58: $350, 66:$820]
On 03/07/2014
𝐶ℎ𝑎𝑟1 =
=
$ 𝑠𝑝𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 𝑝𝑒𝑟𝑖𝑜𝑑 37 𝑡𝑜 66
$ 𝑠𝑝𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 𝑝𝑒𝑟𝑖𝑜𝑑 7 𝑡𝑜 66
$ 350 + $ 820
$520 + $ 120 + $630 + $ 250 + $ 350 + $ 820
= 0.435
Defining Temporal Behavior Maps
Domain specific
A collection of base-maps
01/03/14
Restaurant
$
50.00
01/03/14
Movies
$
20.00
01/10/14
Jewelry
$
520.00
01/17/14
Clothes
$
120.00
01/24/14
Flight Booking
$
630.00
02/03/14
Car Repair
$
250.00
02/27/14
Cash Withdrawal
$
100.00
02/27/14
Car Rental
$
250.00
Price Base Map
[3:$70, 10:$520, 17: $120, 24:$630, 34:
$250, 58: $250]
Cash Base Map
[58: $100]
Count Base Map
[3:2, 10:1, 17: 1, 24:1, 34: 1, 58: 2]
“Aggregator” function creates and updates the Base Maps
26
© 2014 Fair Isaac Corporation. Confidential.
Key Concepts
Base maps
Temporal
Behavior Map
Aggregator
Generator
Period
27
© 2014 Fair Isaac Corporation. Confidential.
► Data
structures, either maps or list of maps
► Store pre-computed summary of the transactions by period
► A collection
of base maps for a given customer
► Summarizing
► Templates
► Time
transaction data to generate base maps
to generate characteristics from base maps
discretization (daily, weekly, monthly,…)
Modular Structure
A Schematic Representation
Derived Chars
►Ratio
of spend in 1 and 2 months
►Spend
in last 1 month
►Spend in last 2 months
Total
Spend
Avg
Spend
Price Base Map
28
© 2014 Fair Isaac Corporation. Confidential.
►Ratio
of cash in 1 and 2 months
►Cash
in last 1 month
►Cash in last 2 months
Total
Cash
Avg
Cash
Cash Base Map
…
…
…
…
►Ratio
of visits in 1 and 2 months
►Visits
in last 1 month
►Visits in last 2 months
Total
Visits
Avg
Visits
Count Base Map
Extensibility and Scalability
Adding New Capabilities Is Easy Due to Modular Structure
New data element
New base maps
New class of
variables
29
© 2014 Fair Isaac Corporation. Confidential.
Expand input data interface
Extend aggregator to add a new base map
Extend Generator to add a new function
Working in Batch Mode
Aggregator
Sorted
Tx Data
Sorted on ID, date
30
© 2014 Fair Isaac Corporation. Confidential.
Decision
Engine
Generators
Temporal
Behavior Maps
Chars
Decision
Working in Real-Time or Incremental Modes
Aggregator
Temporal
Behavior Maps
Fetch
NewTx
Data
Temporal
Behavior
Maps Storage
TBM storage support
random access
31
© 2014 Fair Isaac Corporation. Confidential.
Decision
Engine
Generators
Update
Chars
Decision
Housekeeping Functions For Base Maps
For Real-Time or Incremental Modes
Maturation
Trimming
Append
► Use
historical Tx data to instantiate base maps
► Discard
earliest irrelevant periods as base maps grow
with time
► Introduce
a new base map in existing TBM store
Point of singularity ► A date from which period is calculated
32
© 2014 Fair Isaac Corporation. Confidential.
Portability
The Biggest Contributor to the
Accelerated Time to Value
FICO® Blaze Advisor®
FICO® Model Builder
FICO® Analytic Offer Manager
FICO® Analytic Cloud (coming soon)
33
© 2014 Fair Isaac Corporation. Confidential.
TBM in Action: Use Case #1
Time to Event (TTE) Scorecards
►Time
to Event Model is a scorecard model that predicts propensity of an
event to happen after a predetermined duration in future
► Combination
of scorecards and survival models
► Predicts likelihood of each event to occur in a certain duration of time in future
► Ex. the probability that Guy Ritchie will use the card at a high end restaurant in
the next 7 days
►TTE
considers more customer attributes at the individual customer level
and includes a timing element
►Need
to train one model for each event to be predicted
1000s of events to predict = 1000s of models to build
34
© 2014 Fair Isaac Corporation. Confidential.
Training 1000s of TTE Model
Highly Automated, Scalable, Distributed Modeling Platform
Event-specific
scorecards
TBM makes
it possible to
compute
10,000 chars
Transaction,
Demographic
and Product
Data
TBM
Predictive
modeling
processes
Event-specific
training data
Model for
event B
Auto-binning
A
Cleansing
Summarization
Sampling Feature
Generation
Data
Reports
35
Model for
event A
© 2014 Fair Isaac Corporation. Confidential.
Auto-variable selection
Auto-training
B
Model for
event C
...
Auto-scoring
C
...
Model
Reports
Customer-Event
propensities
Using TTE Models For Making Timely and Relevant Offers
TBM Makes It Easy to Deploy Variable Computation From Modeling to Production
FICO® Analytic Offer Manager
Objectives
Decisions
TTE Models
TBM
Data
Propensity
Matrix
36
Customer
Offers
Optimization
Constraints
Pr{Car Loan}
Pr{Insurance}
….
….
Pr{Restaurant}
...
Jack
0.0815
0.0158
0.0439
0.2067
0.6564
0.0276
Joe
0.0906
0.0971
0.0382
0.0329
0.0363
0.0068
Jill
0.0127
0.0957
0.0766
0.2773
0.8499
0.0655
Pete
0.0913
0.0485
0.0795
0.0467
0.0934
0.0163
…
0.0547
0.0916
0.0646
0.1708
0.3923
0.0034
© 2014 Fair Isaac Corporation. Confidential.
Propensity
Matrix
TBM in Action: Use Case #2
Multiple Decisions Supported in Production Using Tx Data
FICO® Blaze Advisor® business rules management system
Scoring Engine
Transaction Scoring Core
TBM
Update
TBM Storage*
37
© 2014 Fair Isaac Corporation. Confidential.
Chars
Attrition risk
Bankruptcy
Fetch
Sorted Tx
(Backend)
Credit risk
Revenue
Decision fed
into Client’s
system
Recapping Benefits of Temporal Behavior Maps
Efficient
Accurate
Scalable
38
© 2014 Fair Isaac Corporation. Confidential.
► Computationally
► Palatable
cheap and low latency
values can be used across use
cases
► Consumes
new data feeds and data elements
► Easy to add new characteristic computation
Portable
► Use
Multiple
modes
► Supports
same library in modeling and production
mode
batch, incremental and real-time
39
Thank You!
Shafi Rahman
ShafiRahman@fico.com
+1 (858) 353-8280
+91 (804) 137-1768
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
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Shafi Rahman
ShafiRahman@fico.com
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© 2014 Fair Isaac Corporation. Confidential.