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Big Data Techniques to Help Banks Avoid Losses : = Complying with FASB's Credit Losses Standard.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Big Data Techniques to Help Banks Avoid Losses :/
Reminder of title:	Complying with FASB's Credit Losses Standard.
Author:	Casstevens, Darlene Corder.
Description:	1 online resource (110 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.
Contained By:	Dissertation Abstracts International79-11A(E).
Subject:	Accounting. -
Online resource:	click for full text (PQDT)
ISBN:	9780438133761

Big Data Techniques to Help Banks Avoid Losses : = Complying with FASB's Credit Losses Standard.
Casstevens, Darlene Corder.

Big Data Techniques to Help Banks Avoid Losses :Complying with FASB's Credit Losses Standard. - 1 online resource (110 pages)

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.

Thesis (Ph.D.)--Northcentral University, 2018.

Includes bibliographical references

After the financial crisis (2007--2008), the Financial Accounting Standards Board (FASB) wanted banks to use a more forward-looking accounting model and set aside more money to cover future losses. To prevent future losses, the FASB created the new current expected credit loss (CECL) standard. Complying with the new CECL standard will increase a bank's loan-loss reserves and could also increase the banks expenses. The net effect is to decrease a bank's net income resulting in bank losses. This is a problem since it is not known yet whether banks could absorb the losses that will be brought about by the new CECL. Some banks do not want the CECL to take effect. A controversy exists about whether the new CECL standard should have been imposed. The purpose of this quantitative case study was to determine whether banks could reduce the losses that could be brought about by the new CECL using big data techniques such as social medial analysis (SMA). For this study, the population consisted of 5865 FDIC-insured banks in the United States. From this population, a random sample of 25 banks was drawn. Then, the banks' data from the financial reports for the year 2015 was compared to the financial reports for the year 2016. A correlational study on J.P. Morgan Chase Bank (Chase) showed that Chase' net income for 2016 was 24% higher than 2015. Descriptive statistics showed the average net income for all 25 banks for 2016 was higher than 2015. The average net income for all 25 banks for 2016 was $1658227 and the average net income for all 25 banks for 2015 was $1576257. The difference in average net income from 2015 to 2016 was $81970. The average percent change in net income from 2015 to 2016 was 12.01%. However, a paired samples t-test showed this difference in the means was not statistically significant.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438133761Subjects--Topical Terms:

561166
Accounting.
Index Terms--Genre/Form:

554714
Electronic books.

Big Data Techniques to Help Banks Avoid Losses : = Complying with FASB's Credit Losses Standard.
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504 $a Includes bibliographical references
520 $a After the financial crisis (2007--2008), the Financial Accounting Standards Board (FASB) wanted banks to use a more forward-looking accounting model and set aside more money to cover future losses. To prevent future losses, the FASB created the new current expected credit loss (CECL) standard. Complying with the new CECL standard will increase a bank's loan-loss reserves and could also increase the banks expenses. The net effect is to decrease a bank's net income resulting in bank losses. This is a problem since it is not known yet whether banks could absorb the losses that will be brought about by the new CECL. Some banks do not want the CECL to take effect. A controversy exists about whether the new CECL standard should have been imposed. The purpose of this quantitative case study was to determine whether banks could reduce the losses that could be brought about by the new CECL using big data techniques such as social medial analysis (SMA). For this study, the population consisted of 5865 FDIC-insured banks in the United States. From this population, a random sample of 25 banks was drawn. Then, the banks' data from the financial reports for the year 2015 was compared to the financial reports for the year 2016. A correlational study on J.P. Morgan Chase Bank (Chase) showed that Chase' net income for 2016 was 24% higher than 2015. Descriptive statistics showed the average net income for all 25 banks for 2016 was higher than 2015. The average net income for all 25 banks for 2016 was $1658227 and the average net income for all 25 banks for 2015 was $1576257. The difference in average net income from 2015 to 2016 was $81970. The average percent change in net income from 2015 to 2016 was 12.01%. However, a paired samples t-test showed this difference in the means was not statistically significant.
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