audit strategies – bringing your audit function into the 21st … · 2019-08-30 · description...

WEALTH ADVISORY | OUTSOURCING | AUDIT, TAX, AND CONSULTING

Investment advisory services are offered through CliftonLarsonAllen Wealth Advisors, LLC, an SEC-registered investment advisor. | ©2017 CliftonLarsonAllen LLP

Audit Strategies – Bringing Your Audit Function Into the 21st Century

David Heneke, CPA, CISA

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Disclaimers

The information contained herein is general in nature and is not intended, and should not be construed, as legal, accounting, or tax advice or opinion provided by CliftonLarsonAllen LLP to the user. While efforts have been made to ensure the content is accurate, the user should not rely on the information contained herein as authority and should consult with a licensed professional to obtain advise related to any particular situation. The user also is cautioned that this material may not be applicable to, or suitable for, the user’s specific circumstances or needs, and may require consideration of non-tax and other tax factors if any action is to be contemplated. The user should contact his or her CliftonLarsonAllen LLP or other tax professional prior to taking any action based upon this information. CliftonLarsonAllen LLP assumes no obligation to inform the user of any changes in tax laws or other factors that could affect the information contained herein.

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Agenda

• Analyzing Call Report data beyond standard reports

• Data Analytics Defined

• Improving audit function with data analytic techniques

• Analyzing loan portfolio performance, migration and allowance for loan loss levels

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Key Takeaways

• Learn how to leverage publically available information through use of call report data

• Understand the definition of data analytics and the process for its proper application

• Be able to identify areas of financial institutions in which the use of data analytics will be effective

• Understand how to use currently available tools to apply data analytics to populations

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Analyzing Call Report Data

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FDIC Website

• The FDIC has numerous tools that allow you to extract a wide variety of data

• This allows financial institutions to be specific in data they are trying to analyze

• You can identify specific peers groups for which you want to compare

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WEALTH ADVISORY | OUTSOURCING | AUDIT, TAX, AND CONSULTING 7

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This link takes you to call report data

This link takes you to a peer comparison tool

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This link will allow you to download every call report field for every bank in the country

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Industry Wide Net Charge-Off Data (12/31/17)

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Description 12/31/2017 12/31/2016 12/31/2015 12/31/2014 12/31/2013

LN SECURED BY 1-4 FAM RES CONSTRUCTI -0.02% -0.08% -0.01% 0.12% 0.51%

LN SECURED BY OTHR CNSTRCTN LN & LA -0.02% -0.02% -0.06% 0.00% 0.51%

R.E. LOANS-FARMLAND 0.04% 0.02% 0.02% 0.04% 0.09%

REVOLVING, OPEN-END LNS SECD BY 1-4F 0.14% 0.27% 0.39% 0.59% 1.08%

ALL OTH LNS SECD BY 1-4FMLY-1ST LIEN 0.02% 0.05% 0.13% 0.16% 0.40%

ALL OTH LNS SECD BY 1-4FMLY-JR LIENS 0.03% 0.23% 0.64% 1.01% 2.33%

R.E. LOANS MULTI-FAMILY -0.01% 0.00% 0.00% 0.02% 0.10%

LN SECURED BY OWNER OCCPD NONFARM NO 0.05% 0.04% 0.08% 0.15% 0.32%

LN SECURED BY OTHR NONFARM NONRES PR 0.03% 0.00% 0.07% 0.06% 0.22%

C&I LOANS 0.42% 0.49% 0.29% 0.27% 0.37%

LOANS TO FINANCE AGRICULTURAL PROD 0.23% 0.24% 0.08% 0.04% 0.06%

CREDIT CARDS LOANS 3.74% 3.33% 3.11% 3.36% 3.68%

AUTO LOANS 0.87% 0.71% 0.58% 0.55% 0.52%

OTHER CONSUMER LOANS 1.02% 0.94% 0.88% 1.11% 1.33%

LNS TO FGN GOVTS & OFFICIAL INST 0.00% 0.00% 0.00% 0.01% 0.23%

OTHER LOANS 0.18% 0.13% 0.16% 0.15% 0.15%

LEASE FINANCING RECEIVABLES 0.18% 0.26% 0.18% 0.10% 0.16%

Total 0.52% 0.49% 0.46% 0.52% 0.74%

3 Year Average 0.49%



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Current Allowance Levels (As of 12/31/17)

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Loan Type

Impaired ALLL as % of

Loans

Collective Allowance as

% of Loans

Purchased Impaired

Allowance as % of

Loans

Total Allowance as % of

Loans

Real Estate Construction 4.43% 1.12% 3.71% 1.15%

Commercial Real Estate 4.17% 0.79% 2.35% 0.83%

Residential Real Estate 6.21% 0.48% 2.26% 0.57%

Commercial Loans 10.07% 1.03% 2.97% 1.14%

Credit Cards 28.74% 3.98% 26.21% 4.25%

Other Consumer Loans 9.89% 1.08% 1.82% 1.14%

Total 10.03% 1.22% 2.38% 1.32%

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Current Allowance Levels (As of 12/31/17)

ALLL Type % of Total ALLL

Impaired Allowance % 7.14%

Collective % 89.94%

Purchased Credit % 1.52%

Unallocated % 1.39%

100.00%

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Trend Analysis

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Non Interest Income Analysis (As of 12/31/17)

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Mortgage Servicing Rights Analysis (As of 12/31/17)

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Peer Comparison (As of 12/31/17)

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Utilizing Data Analytics in the Audit Function

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What is the audit function to you?

• The combination of all the audits/exams required by a financial institution can provide valuable insight into increasing the profitability of a institution

• Efficiencies can be identified if diligent risk assessments are performed and processes are established to address identified risks

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Internal Controls Defined

• Internal controls are not just about preventing fraud, although that is a big piece, they can also be designed to maximize profitability

• Internal controls, when designed properly, should identify potential for additional revenue, and reduce the probability of loss

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COSO

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Operational controls related to maximizing profitability

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Do you have controls in place to identify opportunities for additional revenue?• Are waived deposit/wire/other fees reviewed to

validate compliance with bank policy?

• Are reimbursable expenses reviewed for accuracy?

• Is a profitability analysis performed by customer?

• Do we periodically review our performance compared to peer?

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Data Analytics

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Data Analytics Defined

• Data analytics is the process of inspecting, normalizing, and processing data in order to support decision making and draw conclusions

• When performed effectively, data analytics allows you to examine significant amounts of data in a short period of time

• Analytics are becoming a larger part of audit processes, as well as management review processes

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Data Analytics Tools• Web-based solutions

• Auditing/Data Interrogation Software: IDEA, ACL

• Database Software: Access, SQL, others

• Spreadsheet/Excel

• Tools and Desired Capabilities◊ Analyze large sets of data efficiently

◊ Work with many types of data

◊ Robust analytical capability built in

◊ Ability to program (macro, script) repeatable processes

◊ Logging all procedures and work steps

◊ Read only

◊ User friendly

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Data Analytics Process

• What is your goal? What do you hope to accomplish?

• Data gathering

• Determine population completeness

• Data normalization

• Data evaluation

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Categories of Data Analytics

• Population

• People

• Trending

• Transaction

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Examples of Data Analytics

• Benford’s Law

• Journal Entries

• Loans

• Accounts Payable/Employee Expenditures

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Benford’s Law

• Mathematical principle that states in any large, randomly produced set of natural numbers, the leading digit of each number will occur a certain percentage of the time

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Benford’s Law

• Example: Use in Analyzing Expenditures

– Manufacturing company which required dual signature on expenses over $5,000

– Expenses under $5,000 only required an accounting employee’s signature

– Controller of company was embezzling funds by paying fictitious vendors

– How could Benford’s law have helped?

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Benford’s Law

• The Benford’s law distribution of expenditures paid for this company is as follows:

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1 2 3 4 5 6 7 8 9

1 Digit Benford Analysis

Frequency

Expected Frequency

Upper Bound

Lower Bound

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Benford’s Law

• The 2 digit Benford’s law distribution of expenditures paid for this company is as follows:

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60

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2 Digit Benford Analysis

Frequency

Expected Frequency

Upper Bound

Lower Bound

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Benford’s Law

• The Controller was authorizing checks for just under $5,000, so the dual control procedure was not performed

• The application of Benford’s Law would have identified an inconsistency in the data which would have been investigated

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Accounts Payable/Employee Expenditures

• What do we want to accomplish?

– Identify potential fictitious vendors via unusual transaction activity

– Identify unusual employee reimbursements for expenses

• What types of data analytics would best serve our goal?

– Population – See example earlier in presentation

– Trending – Are vendor payments consistent

– People – Who is submitting expenses and what is their frequency and dollar volume

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Accounts Payable/Employee Expenditures

• First Step: Validate Completeness

– Are you able to obtain a report directly from the system?

– Can you reconcile the activity showed on the report to expenses recorded on the general ledger?

– Typically the first option is the more practical approach. The auditor should obtain reporting directly from the system to limit the risk the data has been tampered with.

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Accounts Payable/Employee Expenditures -Trending• Review of unusual payments based on historical

payments to vendors

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Accounts Payable/Employee Expenditures –People• Review trends in expense submissions to those of

other employees, as well as historical submissions

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Loans

• What do we want to accomplish?

– Identify loan officers that originate higher than normal problem assets

– Identify potential fictitious loans by looking for loans with no payments in the first year of origination

• What types of data analytics would best serve our goal?

– People – Are there any loan officers with higher than normal problem assets?

– Transaction – Identify and review loans with no payments during first year of origination

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Loans

• First Step: Validate Completeness

– This is typically achieved by reconciling the loan trial balance to the general ledger to ensure all reported loans on balance sheet are included in our population

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Loans

• Breakout past due and problem assets by loan officer to identify loan officers with high past due rates

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Loans – Transaction

• Investigate loans in which a payment has not been received within the first year of origination

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Loans

• You can also perform a variety of other analysis by information already contained in your loan trial balance:

– Migration analysis

– Vintage analysis

• This can also assist financial institutions as they plan for the new allowance standard implementation

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Conclusion

• Data analytics is a means to analyze large volumes of data

• When setup properly, meaningful results can be generated to focus audit procedures, and identify potential malicious activity

• You most likely can perform data analytics with tools already available to you

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Thank you!David HenekePhone: 320-203-5621Email: [email protected]. Cloud, Minnesota

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audit strategies – bringing your audit function into the 21st … · 2019-08-30 · description...

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