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Running head: DAT 520 FINAL PROJECT MILESTONE TWO
DAT 520 FINAL PROJECT MILESTONE TWO 7
DAT 520 Final Project Milestone Two
Student Name
Decisions Methods and Modeling
Southern New Hampshire University
Bank Failures
Structure
The model employed was a top-down structure. The focus of the decision tree is to find states where bank failures are most probable. It, therefore, defines a model where; the liquidity of the bank defines their stability. The more stable a bank, the less likely it is to fail. Using Asset to deposit ratios and further non – current assets to loss ratio will give the best estimate of the stability of different banks in a multitude of states. These three variables will be the major determinants through which the model will be used in determining the nature of the Bank Failures.
Documentation
Different banks have in the past failed. This is often characterized by their inability to meet depositor money. When a bank receives money from a prospective client, often than not, they decide to use the money deposited in investment projects. Should they be in a position to meet the obligations to their depositors, then they are continuing operations, however, in the event, their investments do not return favorable profits, a bank may lose its stability and be declared to have failed (Bruce, 2017). A bank may also be unable to meet its obligations to its creditors. Such instance often leads to an unstable economic and financial environment and have in the past led to the need for banks to receive bailouts through which they can meet their obligations to their depositors (Bruce, 2017). For this analysis, a summary, of different states and the corresponding failures was used to determine the trends between bank failures and states.
To determine the probability of a bank being capable of offsetting some of its debt, the first comparison that will be made will be the ratio between the assets the bank holds, and the total amount made in deposits. This should give a rough estimate of the capacity of the bank to meet its obligations to its main clientele. The higher the ratio, the more stable the bank. Secondly, the banks capacity to mitigates itself from loss is another measure that can be used to determine the stability of the bank. The difference between the Assets and amount Deposited can give a good picture of the overall liquidity of the company. With this figure, finding its ratio against the losses incurred in the last fiscal year (2016) can give a good picture of the stability of the bank and hence the overall probability of it incurring losses.
Evaluation
Data on Bank Failures between 2010 and 2017 was used as the primary information on the trends in bank failures. From the analysis, nine states appear to have experienced a lot of failures over the past seven years (“FDIC: HSOB Commercial Banks,” 2017). The states of Arizona, California, Florida, Georgia, Illinois, Minnesota, Missouri, South Carolina, and Washington have noted the highest propensity of bank failures. Georgia ranked the highest with a total of 61 bank failures in this period. Florida then followed with 56 bank failures and then Illinois with 44(“FDIC: HSOB Commercial Banks,” 2017). Looking at the ratio between assets and deposit for these three banks, all were above 1, which is a sign of stability. However, the banks in Georgia and Illinois recorded significantly lower ratios. The state of Georgia had the least with 1.07(“FDIC: HSOB Commercial Banks,” 2017). It is important to note that states like Connecticut, Idaho, and Minnesota also recorded low Asset/Deposit ratios.
The second measure of overall failure was to determine the ratio between the difference between the banks capacity to liquidate its assets and the losses that it made. This would make for a clearer picture of the stability of the bank as a recent figure was used in this instance. From the analysis, Connecticut recorded the lowest figure at 0.07(“FDIC: HSOB Commercial Banks,” 2017). Still, comparatively, the number of deposits for the state was significantly lower compared to that of Georgia. It can be difficult to predict the geographical location of banks that will experience the most loss in the future. It, however, can be assumed that the states of Georgia, Florida, and Illinois present the largest risk for bank failures (“FDIC: HSOB Commercial Banks,” 2017). Consequently, states like Connecticut and Minnesota present some of the tales of a dwindling trust and investment into local banks. These states, therefore, present with the highest risk.
To summarize the steps are followed, first, the data is pulled from the site provided by Federal Deposit Insurance Corporation that includes the summary of assets, deposits and loses in banks across fifty different states in the United States. Following that, the ratio of assets/deposits per state, and assets-deposits to loss are used to determine the stability of the banks in different states. As the decision model presents in relation with the defined ratio for stability, banks that scored a ratio above 1.1 in the first instance (assets/deposit ratio) are considered relatively stable, and therefore, they are the ones identified as not likely to fail (see Appendix A for decision tree model). The financial ratio above 1.1 indicates that the banks in the specified area can meet their obligations to depositors, since they have more assets than they do deposits. However, the banks that score below the figure were at a high risk of failing. In the excel attachment, ‘’I’’ and ‘’J’’ columns provide the clear picture of the rational state by state. (see Appendix B for excel analysis). The financial ratio, which is below 1.1, shows that the bank is barely capable of meetings its obligations to depositors, and following that this is considered as not a good sign and a clue of failure.
References
Bruce, L. (2017). What happens to your accounts if the bank fails?. Retrieved from https://www.bankrate.com/banking/what-happens-if-your-bank-fails/
FDIC: HSOB Commercial Banks. (n.d.). Retrieved from
https://www5.fdic.gov/hsob/hsobRpt.asp
Appendix A
Decision Tree Model
Appendix B
Excel Analysis
> ecision ree
. 0 0 0 D 2 1 2 0 0 0 1 5 5 TRUE 0 0 TRUE 9 TRUE TRUE 0 0 0 13 TRUE TRUE 17 TRUE 17 TRUE x )
0 0 0 0 0 0 4 4 0 842
2 2 0 5
10 10 0 5
18 0 7 7 0 1 1 0 0 0 0 0 0 0 ERROR:#DIV/0! 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 56 56 0 61 0 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 1 1 0 0.6 1 1 0 44 44 0 2 2 0 6 6 0 1 1 0 3 3 0 1 1 0 0.6 8 8 0 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 8 8 0 0.5 16 16 0 11 11 0 0.5 2 2 0 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 5 5 0 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 2 2 0 911672
0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 4 4 0 3 3 0 6 6 0 3 3 0 0.21 3 3 0 6 6 0 0.41 3 3 0 7 7 0 4 4 0 1.27 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 10 10 0 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 6 6 0 4 4 0 1.1 5 5 0 0.08 4 4 0 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 16 16 0 0.76 9 9 0 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 0 0 0 0 0 0 ERROR:#DIV/0! ERROR:#DIV/0! 363 0 DAT 520 Milestone Three Guidelines and Rubric In this milestone, you will perform an evaluation of your decision model and revise your decision model as needed. Evaluation examples are if you are Specifically, the following critical elements must be addressed in your final submission: Include the structure of your revised decision tree, with a clear description.
Evaluate the results of your revised model, including analysis that is specific to your revised model. In your evaluation, reflect on the appropriateness Suitable diagnostics should be incorporated into the model. Guidelines for Submission: This milestone should be 2 to 3 double-spaced pages of text, with tree model images and any other supporting material appended. Critical Elements Proficient (100%) Needs Improvement (70%) Not Evident (0%) Value
Structure Deci s i on tree and des cri ption are cl early Deci s i on tree and des cri ption are Deci s i on tree and des cri pti on are not 30
Evaluation of Results Eval uati on cons i ders reas onablenes s ,
accuracy, mi s s ing/extraneous el ements , Eval uati on does not ful l y cons i der
reas onabl enes s , accuracy, Eval uati on does not cons i der
reas onabl enes s , accuracy, Model Diagnostics Model i ncl udes cl ear us e of di agnos ti cs Model bui l ds i n parti al us e of Model does not i ncl ude di agnos ti cs 30
Articulation of
Response
Submi s s i on has no major errors rel ated
to grammar, s pel l i ng, s yntax, or Submi s s i on has major errors rel ated to
grammar, s pel l i ng, s yntax, or Submi s s i on has criti cal errors rel ated to
grammar, s pel l i ng, s yntax, or 10
Earned Total 100%
2
D
T
Asset-Deposit/ Loss below
0
1
Grossly Unstable
Asset/ Deposit Ratio below
1.1
1
Asset -Deposit/Loss above 1.0
Relatively stable
0
Asset/ Deposite Ratio above 1.1
Stable
ID
Name
Value
Prob
Pred
Kind
NS
S1
S2
S
3
S
4
S
5
Row
Col
Mark
0
TreePlan
25
TRU
E
1 0 0 E 2 3 4 0 0 0
1
8
TRUE
2 0 0 T 0 0 0 0 0 0
32
3 1 D 3 5
6
7
10
9
4 1 T 0 0 0 0 0 0
27
5 0 3 T 0 0 0 0 0 0 2
13
6 0 3 E 2 8 9 0 0 0 9 13 TRUE
7 0 3 E 2 10
11
19
8 6 T 0 0 0 0 0 0 7
17
9 6 T 0 0 0 0 0 0
12
10 7 T 0 0 0 0 0 0 17 17 TRUE
11 7 T 0 0 0 0 0 0
22
Analysis
FDIC: HSOB Bank & Thrift Failures
Table BF03
Federal Deposit Insurance Corporation
US and Other Areas
(Dollar amounts in thousands)
Effective Date(s): 2010 – 2017
Insurance Fund: ALL
Charter Type: ALL
Transaction Type: All Failures
State
Number of Institutions
Number of Failures
Number of Assistance Transactions
Assets
Deposits
Estimated Loss (12/31/20
16
Ratio (Assets/Deposits)
Assets-Deposits/Loss
Alaska
Alabama
3,923,592
3,524,148
527,116
1.1133
44
0.76
Arkansas
258,100
237,227
31,980
1.087987455
0.6
Arizona
1,724,911
1,521,634
367,101
1.133591258
0.5
California
18
10,369,863
8,527,943
1,180,136
1.215986434
1.
56
Colorado
5,907,934
5,033,322
997,068
1.173764365
0.88
Connecticut
26,368
25,715
9,211
1.025393739
0.07
District of Columbia
ERROR:#DIV/0!
Delaware
Florida
17,814,855
15,603,039
2,734,235
1.141755462
0.81
Georgia
61
17,615,642
16,457,170
5,499,612
1.070393148
0.21
Guam
Hawaii
Iowa
91,580
81,967
16,053
1.117278905
Idaho
153,361
145,813
3,487
1.051764932
2.16
Illinois
18,464,884
16,970,638
2,757,912
1.0880489
0.54
Indiana
2,176,991
1,864,957
128,806
1.167314313
2.42
Kansas
2,732,313
2,504,392
442,837
1.091008516
0.51
Kentucky
92,982
87,196
7,767
1.066356255
0.74
Louisiana
3,952,217
3,587,749
77,872
1.101586817
4.68
Massachusetts
245,534
233,222
20,560
1.052790903
Maryland
1,538,639
1,433,845
305,523
1.073086003
0.34
Maine
Michigan
3,769,306
3,358,462
816,063
1.122330996
Minnesota
2,081,648
2,010,361
405,386
1.035459801
0.18
Missouri
2,330,765
2,023,419
614,026
1.151894393
Mississippi
288,777
268,518
56,284
1.075447456
0.36
Montana
North Carolina
1,030,991
957,681
148,152
1.076549498
0.49
North Dakota
Nebraska
2,930,812
2,291,278
17,655
1.27
36.22
New Hampshire
New Jersey
446,500
437,714
79,422
1.020072467
0.11
New Mexico
3,470,419
2,788,769
400,484
1.244426842
1.7
Nevada
1,263,154
1,215,863
344,056
1.038895007
0.14
New York
927,324
892,626
163,505
1.038871823
Ohio
154,325
137,841
40,112
1.11958706
0.41
Oklahoma
1,104,610
1,005,099
244,219
1.099006168
Oregon
1,920,174
1,828,737
147,585
1.050000082
0.62
Pennsylvania
896,785
818,579
170,463
1.095538732
0.46
Puerto Rico
24,830,175
18,908,535
4,654,070
1.313172861
Rhode Island
South Carolina
3,041,833
2,792,700
595,639
1.089208651
0.42
South Dakota
Tennessee
2,278,148
2,224,875
637,427
1.023944267
0.08
Texas
3,538,873
2,739,025
725,795
1.29201924
Utah
2,714,230
2,655,833
766,758
1.021988205
Virginia
1,208,342
1,106,594
288,370
1.091947001
0.35
Virgin Islands
Vermont
Washington
9,437,876
8,373,008
1,401,612
1.127178667
Wisconsin
2,893,010
2,600,210
354,328
1.112606289
0.83
West Virginia
Wyoming
Totals:
363
159647843
139275704
28178689.59
1.146272023
0.72
performing a bottom-up style recursive partitioning analysis, and you should report on the error rate and variable selection. You might also consider alternative
variable categorizations to improve your model. If you are performing a top -down decision tree modeling exercise, what are the threshold values that cause the
tree to flip? You should perform sensitivi ty analysis on the critical variables in your tree and report what those sensitivity analyses are telling you. For either sty le
of modeling, what makes your tree stronger? What breaks the model? For more information on completing this milestone, please ref er to the Final Project
Notes in the Assignment Guidelines and Rubrics folder.
and adjustments of the revised model, as well as the accuracy of the results you obtained.
Review your work to ensure that there are no major errors in writing mechanics. If you have citations, include the sources at the end and cite them APA format.
s tructured
s omewhat cl early s tructured
adequatel y s tructured
and error i n the model
mi s s i ng/extraneous el ements , and error
i n the model
mi s s i ng/extraneous el ements , and error
i n the model
30
di agnos ti cs
organi zati on
organi zati on that negati vel y i mpact
readabi l ity and arti culation of mai n
i deas
organi zati on that prevent
unders tandi ng of i deas
