FNBM Payment Float Strategy Model Development

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FNBM Payment "Float" Strategy Model Development
Amelia Brakefield
Robert Gladd
Don Blackhurst
FNBM CorporateRisk Management
May 2002
rffiiorul
BANK
OF
MAR'N
FNBM Payment t'Float" Strategy Model Development
Amelia Btakefield
Robert Gladd
Don Blackhurst
FNBM Coqpomte Risk Management
Mav 2002
Executive
Summary
This paper documents a study undetaken to derive a datz-diven risk-based strategy for attenuating
the aggregate adverseimpact of catdholder payments returned for "non-sufficient funds" (f{SF) by
identi$'ing payment and account stanrs criteria predictive of elevated likelihood of NSF teturn.
Using logistic regression and Classification and Regtession Ttee (CART) methods, such elements
were found to include delinquency stage at time of payment, payment amount and type, Payment
magnitude as a percentage of credit line, recent priot history of NSF returned payments, and
number of close proximity (same cycle) payment attempts. The study yielded a set of payment
"floaf'l criteria that appear to be significantly more effective than the current strategy deployed in
the FDR Payment Defender system. Relative "effectiveness" is evaluated in terms of the
proportions of "good" payments floated f no floated versus "bad" payments floated /not floated. A
floated "good" payment (one honored by the issuing source) is a "false positive," whereas a payment
not floated yet returned as NSF is a "false negative." False positives rePfesent ttansient lost
utilization (and thereby income) oppotunity, and can result in catdholdet dissatisfaction with the
unavailability of good funds. False negatives represent elevated risk in terms of additional
unwarranted cedit availability. Th. goal is to minimize both types of classification errors to the
extent pmcticable.2
NSFrecenthistoricalsummary
and culminatingin April, 2002,monthly NSFs have
Dnring the period beginningwith January,2001,
of the number of paymentstemitted and 5.5o/oof paymentdollars
compdsedapproximately3.8o/o
(seeFigure2 andTable 2hercinafter).Fot the fust fout months of 2002,NSFs have averaged
approximately$1.47million per month (equivalently,-$17.6 million annualized).
1 Fnnds unavailable to the cardholdet for a specified hold time'
2 Statisticians refer to the "sensitivity" versus "specificity" of predictive measures. We could, of course, float all
payments, a tactic that would hold all bad payments (perfect sensitiviry), but at the unacceptable cost of also holding all
good ones (zero specificity)-the vast maiority of cardholder payments. Given a low prevalence circumstance-e.9., our
io"grtly 3-4%monthly incident NSF rate-at once achieving high specificity and sensitivity becomes problematic.
Consequently, sensitivity/specificity rnisclassification trade-offs must be evaluated in terms of estimated quantitative
risk-cost/benefit analysis, and we did so in concluding this study.
'v
li:
FNBMNSFFloat Model Development
NSF|'Jrn 2n/0l-AprilAna
$1,800,000
$1,600,000
$l,4m,ooo
$1,2oo,ooo
f1,0m,000
$800,000
$600,000
$,loo,ooo
$200,000
$o
CCo.p g*-r-.tg
,.,f-f -f CgrT,'""..$".tr.o,S
""p
Figure1: NSFdollarsby month,January2001- April2002
NSF incidcnt rrtc w. S ntc
8/e
7o/o
6%
5o/o
4%
3Vc
T/o
lo/o
V/o
,.,f -,Sa,,f ,*A+of.,.'$r"" -^f, CC
"S
oo$g" oo*+pe,.*P
of numberof monthlypayments(lower,orangeline)
Flgure2: NSFsas a poroantagp
andbbl amountof rnonhlypayments(uppergre€nline).Dataftttedwithpolynomialtrendlines
-2-
.
FNBMNSFFloatModelDevelopment
Jan-O1
Feb-01
Mar-01
Apr-01
May-O1
Jun-O1
Jul-01
Aug-01
Sep-01
Oct-01
Nov-O1
Dec-01
Jan-02
Feb-02
Mar-02
Apr-02
496,955
570,467
578,347
525,1,02
534,182
548,710
565,163
588,818
603,130
619,959
622,597
630,958
631,,209
679,037
61,4,782
288,968
272,527
300,450
283,684
284,270
282,728
290,809
291,,134
285,313
324,242
305,144
302,296
378,749
306,623
322,907
310,550
58.1.5o/o
53.39o/o
57.960/o
54.02%
53.22%
51,.530/o
51.460/o
49.44o/o
47.31.o/o
52.31,o/o
49.07o/o
47.91.o/o
50.50%
49.53o/o
52.520/o
50.81,o/o
1,.1,9
1,.1,3
1.18
1..27
1.15
1,.1,4
1.15
1,.1,4
1,.1,2
1.18
1..1,4
1.15
1,.16
1.13
1,.1,7
1,.1,6
343,891,
fi20,41,5,1,97.37$59.37
308,920
ff21,,502,955.68 $69.61
355,925
$23,524,1,21,.98$66.09
361,,646
$23,386,096.26 fi64.67
325,783
$21,055,600.19 $64.63
323,230
$21,,275,288.40$65.82
334,1,82
fi21,,571.,231.67$64.55
331,,545
fi22,352,085.21,fi67.42
31,9,304 $21,,31,2,592.11
$66.75
1,,1,70.25 $66.57
384,1,25
fi25,57
80.64 $68.40
348,871,
$23,863,7
348,603
fi22,755,357.77 $65.28
370,21,0 $ 2 5 , 0 5 4 , 1 5 7 . 8 3$ 6 7 . 6 8
347,934
$27,899,892.67 $80,19
377,827
$28,061
,893.94 ff71.27
361,.076 $26,045,210.90 ff12.13
5,543,072 $375,646,632.87 fi67.77
datia,January2001- April2002.Source:ME Histtables.
Table1: Payments
Jan-01
Feb-O1
Mar-01
Apr-O1
May-01
Jun-O1
Jul-O1
Aug-01
Sep-O1
Oct-01
Nov-01
Dec-01
Jan-02
Feb-02
Ma*02
Apr-02
72,953
10,226
11,538
11,,269
17,407
12,454
12,674
14,559
12,573
15,657
14,047
'1,3,768
16,184
12,326
12,226
212,535
fi7,066,235.75 $82.32
$917,658.32 $89.74
$1,040,597.70 $ 9 0 . 1 9
fi961,999.43 $8s.37
$1,490,007.93 $85.02
$1,158,977.85 $93.06
fi1,,21.6,077.A3$9s.9s
$7,335,421.67 $91.72
$1,,177,641.09 $93.66
$1,496,765.08 $9s.60
fiL,579,844.26 $1,1,2.40
$1,,486,932.77$108.00
fi1,597,31,0.47 $98.70
fft,474,074.13 $ 1 1 9 . 5 9
$1,479,998.48 $120.96
fi1,,322,987.76 $104.39
790,429.06 {97.82
Table2: NSFdata,January2001- April2002.Source:ME Histtables
-3 -
3.770/o
5.220/o
3.31,oh
4.27Yo
3.24Yo
4.42oio
3.1.2o/o
4.11.o/o
5.34%o
7.030h
3.85o/o
5.45o/o
3.79o/o
5.640/o
4.39Yo
5.97%o
$19,348,961.62
$20,585,297.36
$22,483,521,28
$22,424,096.83
$19,575,592.26
$20,116,310.55
$20,355,154.64
$21,016,663.54
$20,134,951.02
3,940/o
5.53o/o
4.080/o
5.85o/o
4.03o/o
6.62Yo
3.95%o
6.53o/o
4.37%o
6.38o/o
3.54o/o
5.28Yo
3.24o/o
5.27%o
3.5"1,o/o
5.08%
fi24,074,405.17
fi22,284,936.38
ff21,,268,425.06
$23,456,847.36
fi26,425,818.54
$26,582,995.46
$24,722,223.74
3.830/o
5.53o/o
$354,856,203.81
FNBMNSFFloatModelDevelopment
Paymentand NSFtypes
\Ufeassembled a six-month payment and NSF history dataset from the FDR TranHist files spanning
October 2001through Febnrary 2002. After retaining only exact dollar NSF matches to priot
payments, we wer'e able to tabulate the following estimated NSF distributions by incident and dollar
rates, along with statistical days-to-post intervals.3
Direct Check
Check
Certified Funds
MoneyGram
Western Union
$ 4,930,485.69 63.88o/o 68.72o/o $ 110.78
21,970 fi 2,017,453.833',t.450/o 28.12o/o $ 92.08
4.509
2,370 $ 126,886.42 3.40o/o
486
$ 44,300.84 0.70o/o
357
52,751.75 0.510h
$
475.00 0.030/o
$
tl
351.00 0.02o/o
AutoPav
$
0.07o/o
1,578.15
9
AsencY Payment
$
Xffib
69,6tt $ 7,17438268
Tell-a-Friend
t9
7.77o/o
$ s3.s4
0.620/o
$ 91.1s
0.74o/o $ 1,47.76
0.01,o/o
$ 2s.00
0.00o/o
$
31.91
0.02o/o $ 1,75.35
$ rc2.97
Tabfe3a: NSFdataby remittancetype,October2001- February2002.
Direct Check
1
4
5
6
6
8
Check
1
8
10
1.2
1,4
18
Certified Funds
1
3
5
8
14
3
4
7
1,1
MoneyGram
1
2
2
Western Union
1
1
1
2
4
15
Tell-a-Friend
3
10
1,7
29
37
AutoPay
3
5
18
18
Agency Payrnent
10
35
64
720
37
26
t20
26
t20
T*lN
2002.
2001- February
type,October
daysto postbyremittance
Table3b:NSFdatia,
Direct Checks ('DCs'), private checks, and Certified Funds payment methods comprise nearly 99o/o
of the NSF experience, with DC payments alone accounting for approximately t'wo thuds of NSFs
(-640/0 of incident NSF tate, -690/o of NSF dollars). DCs account fot approximately 25o/oof overall
remittances. Consequently, the DC payment is significantly more likely to be returned NSF than
othet payment methods
3 Current float strategy holds all designated payments for 10 days irrespective of remittance tyPe.
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FNBMNSFFloatModelDevelopment
Dataanalysisand modeling
At the outset of this study, March 2002 payment and NSF data were the most recent available,and
so were chosen to comprise the development study set. Data were assembled from the FDR "short
month-end ME Hist" table HIST0302.dbf, a dataset containing number and dollar amounr of
payments and NSFs per account, as well as balance and credit line amounts. Only accounts showing
one or more remittances in March were retained for the study.
To these data we added, from the Febnrary 2002 FDR full month-end file, account attributes such
as ACS Behaviot Score, FICO NextGen Scote, delinquency bucket, months on books, and a tally of
any NSFs (incident) for the most recent 4 months,'1,2 months, and lifetime. The NSF history was
obtained from the Risk Group SAS DelHist0302 portfolio history table. Utilization (balance/line),
payment-percent-of-balance,"dollars-at-risk" (balance-savings)and payment-percent-of-line were
also calculated.After the metging of the several source files, 295,932 account records were retained.
9,024 accountshaving at least one NSF in March were taggedas "bad" (-3.05o/o).Good/Bad
statistics are sutnmanzed in Table 4 below:
0.609
0.1,97
# of NSFs last 4 rnonths
0.459
0.087
# of NSFs last 12 months
0.834
0.209
# of NSFs accountlifetime
# of prior NSFs this cycle
1.248
0.400
0.091
0.000
# of payment attempts this cycle
0.015
0.001
Months on books
1,9.7
24.9
FICO NextGen Score
516
550
FDR Behavior Score
560
620
fi104.44
fi70.77
$5s2.33
$4e0
fi463.92
$365.45
0.81,2
$286.61
0.755
0.231
0 . 15 3
0.153
0 . 18 4
# of cvcles delinquen
Pavment amount
Balance prior to payment
Credit line
Dollars at risk (balance-savinEs)
Utiliz2lien
Pavment o/oof ltne
Pavment o/oof balance
$508
Table4
Univariate statistics for the Good (no NSF) group were uniformly more favorable than were those
pertainingto the "Bad" (9,024 NSF) Soup.However, while many of these variables were found to
be statistically"significant'according to a subsequentSAS logrstic regression trial, the resulting
regression model (using these variables to predict the likelihood of making a good payment) was
found to have a weak "adiusted R-Square" value of 0.1114, owing to pervasive drstnbuuonal nonnormalities (i.e. ske*) and ovedap between Good/Bad predictor values. In short, a logistic
regressionmodel employing these predictors would yield a hrgh misclassification r^te.
-5-
FNBMNSFFloatModelDevelopment
CARTmodelingtrials
The Classification and RegtessionTree (CART) methodology is frequendy employed for explo rz,tory
data analysis,and was used in this snrdy. A sgruficant advantage of CART is that it is considered a
"non-parametdc" ptocedute not bound by the critical data normality assumptions of Least-Squares
analytical and modeling methods such as regression.Moreovet, CART can operate of a mix of
categorical, ordinal (tank-ordered) and intenralf rao.olevel data.
CART is a "ttee-based" method also known as "binaq/ recursive partitioning" in which the
dependent variable must be expressed as a dichotomous outcome (hence "binary")-in
this case
Good=1 (no NSD versus Good=0 G.{SD.CARTiteratively attempts to find the optimal separation
of Good /Bd outcomes as a function of the ptedictor variables, splitting the data into hierarchical
"tree nodes" (hence "tecursive partitiooitg') using the strongest predictor each time until pre-set
node size limits are reached. The splits tesulting from the final iteration are called terminal nodes.
One terminal node will conain the highest proportion of Good casesfound (no NSF), and another
will contain the lowest propottion of Goods. An example of our S-Plus CART output follows:o
( 1 ) r o o t 2 9 5 9 3 2 8 7 . 1 9 . 0 00 . 9 6 9 5
( 2 ) S U i l . N S F 4 < o . s2 7 3 2 7 2 6 2 4 8 . 0 0 0 . 9 7 6 6
( 4 ) D C < 0 . 52 1 0 0 8 1 2 6 0 7 . 0 0 0 . 9 8 7 4
( 8 ) D E L . C Y C0<. 5 1 8 8 4 5 0 1 5 9 6 . 0 0 0 . 9 9 1 5
(16) 830202<630.5 59222 825.20 0.9859 *
(17) 8S0202>636.5129228 768.40 0.9940 .
( 9 ) D E L . C Y C > o .251 6 3 1 9 8 1 . 0 0 0 . 9 5 2 4
( 1 8 ) A V G . P i l T < 2 s . 94
21 8 1 6 9 . 7 9 0 . 9 8 3 0 *
( 1 9 ) A V G . P I T > 2 5 . 9 21 7 4 5 O 9 0 6 . 3 0 0 . 9 4 5 0
(38) 850202<223.51688 156.10 0.8969 *
( 3 9 ) B S O 2 0 2 > 2 2 3 . 51 5 7 6 2 7 4 s . 9 0 0 . 9 5 0 2 *
( 5 ) D C > 0 . 56 3 1 9 1 3 5 3 5 . 0 0 0 . 9 4 0 5
( 1 0 ) P P 7 < 0 . 56 2 9 3 8 3 4 4 2 . 0 0 0 . 9 4 1 9
(20) ltoB<lo.65 26183 1781.00 0.9266
( 4 0 ) U T I L < I . 0 5 3 0 52 1 9 0 9 1 3 5 4 . 0 0 0 . 9 3 3 8
( 8 0 ) A V G . P t t T < s 8 . 4 3 51 3 7 6 1 7 2 5 . 1 O O . 9 4 4 2 '
( 8 1 ) A V G . P M T > 5 8 . 4 83 15 4 8 6 2 4 . 9 0 0 . 9 1 6 3 *
( 4 1 ) U T I L > I . 0 5 3 0 54 2 7 4 4 1 9 . 9 0 0 . 8 8 9 6 '
( 2 1 ) i l O B > 1 0 . 6 53 6 7 5 5 1 6 5 0 . 0 0 0 . 9 5 2 9
(42) SUil.NSF<1.5
30843 1209.00 0.9591
( 8 4 ) U T I L < 1 . 0 8 3 0 52 5 6 9 1 9 2 6 . 0 0 0 . 9 6 2 6
( 1 6 8 ) A V G . P i l T < 6.'6r 4 3 4 1 7 7 2 9 5 7 1 . 3 0 0 . 9 6 6 7
( 1 6 9 ) A V G . P M T > 6. 614 3 4 7 9 6 2 3 5 3 . 7 0 0 . 9 5 3 4 *
(85) UTIL>1.08305 5152 280.80 0.9422 '
( 4 3 ) S t i l . N S F > l . 55 9 1 2 4 3 4 . 3 0 0 . 9 2 0 2 *
( 1 1 ) P P 7 > 0 . 52 5 3
61.25 0.5889 *
(3) SUil.NSF4>0.5 22660 2322.OO 0.8841
(6) 8S0202<585.55381 855.40 0.8017 *
( 7 ) 8 S 0 2 0 2 > 5 8 5 . 51 7 2 7 9 1 4 1 8 . 0 0 0 . 9 0 9 8
( 14) 0C<0.5 9778 s64.90 0.9384 '
( 1 5 ) D C > 0 . 57 5 0 1 8 3 4 . 9 0 0 . 8 7 2 4 .
aThe parentheticalnumbers on the left indicate the splits.Asteriskson the right denote terminal nodes.This trial
produced 16 terminal nodes.The highestproportion of Goods is found in node 17. The lowest proportion is found in
mode 6. Interpretation: no NSFs in pdor four months (node 2), not paying by DC (node 4), account current (node 8),
and a Behavior Scoreof 637 or better (node 17) yielded 99.4ohGoods. Conversel/, one or more NSFs in the prior 4
Goods.
months (node 3) followed by a Behavior Scoreof 586 or lessresultedin a terminus containing only 80.1,7o/o
-6-
FNBMNSFFloatModelDevelopment
The following is a gtaphical depiction of the CART decision tree paths to the best and worst
'al
terminal nodes. This t
resulted in 16 terminal nodes. The proportions of Good accounts in the
remaining 14 not displayed here vry within the worst-to-best (80.17o/oto 99.4o/o)percentages
illustrated below
( Nurter of NSFs in prior four npnths )
(Rerittance type)
Cycles delinquenl
Behavior Score
Figure3:CARTtrialtreepathsummary
The CART method is hierarchical in that it chooses the strongest predictor of Good/Bad
separation on each iteration for the data remaining in the path aftet a prior split (starting, of course
with the entfueset. In this instance, NSFs in the prior four months was strongest on the Fust pass).
The number of splits and terminal nodes in a CART ta:ralarc govemed by researcher-supplied
parameter conftols such as [1] minimum number of observations prior to a node split, [2] minimum
number of observations in any tree node, and [3] a factor called "minimum node deviance." Tradeoff judgments must be made tegatding Good/Bad separation and node size. In other words, an
unconstrained CART trial would result in perhaps hundreds or thousands of terminal nodes, a
relative few of which would contain either all Goods ot all Bads, but all of which would have very
-7-
FNBMNSFFloatModelDevelopment
few observations. Whete the objective entails yes/no decision criteria such as that required for our
float/do not float sttategfr an overly latge and too-detailed tree would serve no useful purpose.
Gandidatefloat models
The initial statistical andyses heretofote described provided ditection for candidate float sffategy
modeling. After dozens of logistic tegtession and CART sessions,a number of predictors showed
promise, including
o
o
o
o
o
o
o
o
Time on books;
Payment amount;
Recent prior NSFs;
'lization;
U
DC vetsus non DC remittance;
Multiple payments this cycle;
Payment o/oof bdance or limit;
Behaviot Scote
Given that our month-end daa do not provide us with direct account-level payment floatf no-float
identifiers, we 6rct had to dedve an estimate of the existing FDR Payment DefenderrM float strategy
now in ptoduction as they would apply to the Match 2002 study set. The existing float criteria are
summarized as follows:
IF paynent amount is >=$100Al,lD
If External Status is A or
I f In te rn a l S ta tu s n o t ' Bl a n k' or
If # days since last paymentis less than 8 on
If Balance before payment / Credit Line is >=125t on
If Account has had more than 3 NSFsin the last 4 months or
If Account was more than one cycle delinquent befone payment
OR
Account was 1 cycle delinquent before paymentand
Account has already had an NSFthis cycle
OR
Time on Books is <=6 months and Account is less than 2 cycles delinquent and
Account has had an NSFthis cycle
OR
Account is less than 2 cycles delinquent and
Account has not had any NSFsthis cycle and
Paymentis >=50rbof the credit limit
OR
Time on Books is >6 and <=12 and
Paymentis >=50t of the credit limit
OR
Time on Books is >12 and
Paynent is >=100t of the cnedit limit
FLOATPAYMENT
-8-
FNBMNSFFloatModelDevelopment
Subsequently,we estimated the follo*ing floatf no float segmentation. Bad/no float and Good/f7oat
quadrants constitute misclassification effors.
295.932
Pct of all floated 1,0.91,o/o
Pct of Bads floated 22.33o/o
Pct of Goods floated 1,0.55o/o
Table5
Thus fat the discussion has focused on the incidence of NSFs. A strategy focused solely on
improving the "catch r^te" of bad payments while mirumrzrng the floating of good payments,
however, might well fall short in terms of aggregateNSF dollars held relauve to good payments
floated-and consequently net value to the bank. For example, while the current strategy is
estimated to catch only 22.33o/oof the bad payment incident rate, it floats better than 57o/oof the
NSF dollats. See table 6 below.
NSFs not floated
NSFs floated
Goodsnot floated
Goods floated
7,009
2,075
256,646
30,262
480,574.83$ 68.57
O+8,459.10$ 321.82
15,821,893.74$ 61.65
8,723,367.73fi 268.43
$
$
$
$
5l.43Yo
33.92o/o
Table6
Dozens of predictor combinations were tried (seelist on page 8), gurded by CART analyttcaloutput,
with the obiective of at once maximizing NSF dollars floated and minimiznggood payments floated
while also minimizing the incident rate of good paying accounts floated. A model emerged that
corectly identified better than 56Yoof the incident NSFs and approximately 78o/oof the NSF dollars
while floating slightly fewer good payment dollars.
Model NSFs not floated
Model NSFs floated
Model goodsnot floated
Model soods floated
3,962
5,062
237,116
49,792
$
$
$
$
249,475.24 $ 62.95
879,678.69 $ 173.77
76,434,264.39$ 69.31
7,510,990.48 $ 150.85
77.970/o
31,.37o/o
Table7
The proposed alternative model cdteria are sftaightforwatd: float where [1] an account is 30+ days
delinquent, or plhad more one or more prior payment attempts this cycle, or [3] a DC payment of
rnore than $75, or [4] there was at least one NSF during the prior 4 months, or [5] a DC payment
and account is 5+ days delinquent, or [6] at least one prior NSF this cycle, or [7] payment is great
than 150% of line, or [8] payment is mote than $1,000.5
5 A cost of this strategy is the total number of accounts floated: 32,277 under the current procedure (10.9%) versus
54,854 (18.570) under the new model.
-9
-
FNBMNSFFloatModelDevelopment
Validationsamples
We appliedthe float model criteria [anuary / Februaryattributes) to February 2002 payment data,
with the following outcome (comparedto the current procedure outcome estimates):
Model badsnot floated
Model badsfloated
Model goodsnot floated
Model soods floated
Default badsnot floated
Default badsfloated
Default goodsnot floated
Default qoodsfloated
2,622
5,464
189,462
55,524
5,818
2,268
210,148
34.838
$
$
$
$
$
$
$
$
276,325.96 $
777,683.63 fi
12,613,947.57 $
8,600,318.14 $
345,066.65 $
648,942.94 fi
71,433,485.75 $
9,780,773.90 $
82.50
142.33
66.58
154.89
59.31
286.13
54.41
280.75
Table8, February
2002payment
data
Finally, we merged three months of payment data covering February, March, and April 2002,
keeping only obsenrations with at least one payment attempt during the period (n=325,483). We
then applied the float model criteria using the combination of January and February attributes to
determine how well the model would predict at least one NSF dunng the February-Apnl period,
The findings are set forth below in table 9.
Model bads, not 0oated
Model bads floated
Model goods not floated
Model goods floated
17,240I
10,632$
247,723$
61,888$
325,#3
1,760,029.06
1,660,386.75
47,853,432.40
15,402,136.25
41.130h
58.87%
73.10%
26.900/0
57.39o/o
48.610/o
79.620/o
20.38%
Table9
The NSF prevalence in this 3-month period is 6.720/oQ1,,872/325,483).The model identifiec
approximately 49o/oof the incident NSF and 59o/oof the NSF dollars while floating approximately
20.4o/oof the good payment dollars.6We compare these findings to the estimate yielded by the
cunent float strategy over the same three-month period (table 10).
Current bads not floated
Current bads floated
Current goods not floated
Curent goods floated
17,980$
3,892 $
270,996 $
32,615 $
7,743,235.08
1,077,180.13
42,468,223.59
74,787,345.06
61.81%
38.790/o
74.770/o
25.83%o
82.27o/o
1,7.79o/o
89.260/o
1,0.74"h
325,483
Table10
In these data, the new model floats appreciably more NSF dollars while holding significandy fewer
good payments relative to the opetative strategy.
6 Noteworthy here is that, while the recent average monthly NSF incident rate is slightly less than 4o/o(see table 2), the
likelihood of an NSF across this 3-month period is closer to 7o/o
_1,0_
FNBMNSFFloatModelDevelopment
The proposed float strategy provides approximately a 48o/olift ovet the current procedure in the
monthly development and validation samples and neady a 38o/olift with respect to the 3-month
sample (seetable 11). 'Ufhile the proportion of actual NSF accounts ("bads") floated to total
accounts floated might seernlow+.g.,9.23o/o in the March sample model-, it is important to keep
in mind that the incident prevalence of NSFs in the Match data, for example, is only 3.05o/o.ln other
words, the model NSF float captute is three times the rncidence of NSFs in the data.
Floctcd
Good
Bad
Developrncot sample
30,262
2.015
34,838
2,268
7o bads floated
6.24%
6.77o/o
Vdidation
sample
3-month sarnple
32,61.5
3,892
'1,0.66o/o
Moddsd Stratesy
Floated
Good
Bad
7o bads floated
Development
sample
Validation sample
49,792
55,524
5.062
9.23%
5,464
8.96Yo
3-month sample
61,888
10,632
1,4.660/o
Table11
analysis
GosUBenefit
These data indicate that the float model discussedin this paper ptoffers the potential of
approximately $1 million annualized savings relative to the current strategy. The assumptions and
estimate used to derive this finding are tabulated in table 1,2on the follo*ing page.
A few caveatsare in order. Presently we are not provided with misclassification incident rates and
sulns occurring under the operative float strategy deployed thtough Payment DefenderrM, rendering
problematic our ability to make direct, accuratebenchmark champion/challenger comparisons with
our model. Moreover, our modeling is based on certain variables that are proxies derived from
month-end data, as we do not have accessto many FDR Master File data available to the Adapuve
Control System (ACS) that are the actual drivers of any production float strategy. Finally, modeling
is consffained to the use of predictors availablein ACS production. Other factors such as, for
example, number of outbound and/or inbound collections contacts, PTPs (promises-to-pay), or
RTPs (refusals)per cycle m^y have predictive utility with respect to NSFs, but are necessarily
excluded.
-11 -
FNBMNSFFloatModelDevelopment
otal NSF $
otal $ Lost if no stra
$ 7,729,033.93
648,459.10
$
480,574.83
$
442,987.75
$
254,429.47
$
fi
$
fi
fi
et $ Lost
umber of Accts floated
'o Accts
of
that Don't pay after NSF and have open to buy:
Cosu
Good $ Floated
Current Stratew:
7,570,990.48 $
50.0%o
85.0%
$
20.0%
$
9
1,9.8o/o
12,467.83
8,123,367.73
6 of Float that could be re-used
49.7o/o
85.0o/o
20.0%
9
19.8%
t $ C/O Rate
Revenue
$
of Accts that call re: Float
otal Cust Svc Cost
231,,759.59
(231,1,59.59)
78,114.31
90,697.78
(90,697.78)
22,577
46.1.60/o
85.00%
that would have used up the open to buy:
'otal
7,729,033.93 $
879,678.69 $
249,475.24
$
521,1,62.06 $
345,727.19
97,860.56
54,954
$
I
$
fi
$
$
fi
73,233.53
5.0%
1.00
1,613.85
254,429.47
8,000.00
13,233.53
1,613.85
22,847.38
237,582.04
-12-
(612,371.25)
(765.6e)
5.0%o
1.00
2,742.70
$
$
$
$
$
345,727.79
8,000.00
72,467.83
2,742.70
23,270.53
32't,91.6.66
1,128.85
$
$
$
]h
V
$
90,697.78
(765.6e)
1,128.85
363.t6