How Internal Auditors can win The War against Spreadsheet Fraud

To prevent another round of million dollar scandals due to fraudulent manipulations on spreadsheets, regulatory bodies have launched major offensives against these well-loved User Developed Applications (UDAs). Naturally, internal auditors are front and center in carrying out these offensives.

While regulations like the Sarbanes-Oxley Act, Dodd-Frank Act, and Solvency II can only be effective if end users are able to carry out the activities and practices required of them, auditors need to ascertain that they have. Sad to say, when it comes to spreadsheets, that is easier said than done.

Because spreadsheets are loosely distributed by nature, internal auditors always find it hard to: locate them, identify ownership, and trace their relationships with other spreadsheets. Now, we’re still talking about naturally occurring spreadsheets. How much more with files that have been deliberately tampered?

Spreadsheets can be altered in a variety of ways, especially if the purpose is to conceal fraudulent activities. Fraudsters can, for instance:

  • hide columns or rows,
  • perform conditional formatting, which changes the appearance of cells depending on certain values
  • replace cell entries with false values either through direct input or by linking to other spreadsheet sources
  • apply small, incremental changes in multiple cells or even spreadsheets to avoid detection
  • design macros and user defined functions to carry out fraudulent manipulations automatically

Recognising the seemingly insurmountable task ahead, the Institute of Internal Auditors released a guide designed specifically for the task of auditing user-developed applications, which of course includes spreadsheets.

But is this really the weapon internal auditors should be wielding in their quest to bring down spreadsheet fraud? Our answer is no. In fact, we believe no such weapon has to be wielded at all?because the only way to get rid of spreadsheet fraud is to eliminate spreadsheets once and for all.

Imagine how easy it would be for internal auditors to conduct their audits if data were kept in a centralised server instead of being scattered throughout the organisation in end-user hard drives.

And that’s not all. Because a server-based solution can be configured to have its own built-in controls, all your data will be under lock and key; unlike spreadsheet-based systems wherein storing a spreadsheet file inside a password-protected workstation does not guarantee equal security for all the other spreadsheets scattered throughout your company.

Learn more about Denizon’s server application solutions and discover a more efficient way for your internal auditors to carry out their jobs.

More Spreadsheet Blogs

 

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Top 10 Disadvantages of Spreadsheets

 

Disadvantages of Spreadsheets – obstacles to compliance in the Healthcare Industry

 

How Internal Auditors can win the War against Spreadsheet Fraud

 

Spreadsheet Reporting – No Room in your company in an age of Business Intelligence

 

Still looking for a Way to Consolidate Excel Spreadsheets?

 

Disadvantages of Spreadsheets

 

Spreadsheet woes – ill equipped for an Agile Business Environment

 

Spreadsheet Fraud

 

Spreadsheet Woes – Limited features for easy adoption of a control framework

 

Spreadsheet woes – Burden in SOX Compliance and other Regulations

 

Spreadsheet Risk Issues

 

Server Application Solutions – Don’t let Spreadsheets hold your Business back

 

Why Spreadsheets can send the pillars of Solvency II crashing down

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New Focus on Monitoring Soil

There is nothing new about monitoring soil in arid conditions. South Africa and Israel have been doing it for decades. However climate change has increased its urgency as the world comes to terms with pressure on the food chain. Denizon decided to explore trends at the macro first world level and the micro third world one.

In America, the Coordinated National Soil Moisture Network is going ahead with plans to create a database of federal and state monitoring networks and numerical modelling techniques, with an eye on soil-moisture database integration. This is a component of the National Drought Resilience Partnership that slots into Barrack Obama?s Climate Action Plan.

This far-reaching program reaches into every corner of American life to address the twin scourges of droughts and inundation, and the agency director has called it ?probably ?… one of the most innovative inter-agency tools on the planet?. The pilot project involving remote moisture sensing and satellite observation targets Oklahoma, North Texas and surrounding areas.

Africa has similar needs but lacks America?s financial muscle. Princeton University ecohydrologist Kelly Caylor is bridging the gap in Kenya and Zambia by using cell phone technology to transmit ecodata collected by low-cost ?pulsepods?.

He deploys the pods about the size of smoke alarms to measure plants and their environment.?Aspects include soil moisture to estimate how much water they are using, and sunlight to approximate the rate of photosynthesis. Each pod holds seven to eight sensors, can operate on or above the ground, and transmits the data via sms.

While the system is working well at academic level, there is more to do before the information is useful to subsistence rural farmers living from hand to mouth. The raw data stream requires interpretation and the analysis must come through trusted channels most likely to be the government and tribal chiefs. Kelly Caylor cites the example of a sick child. The temperature reading has no use until a trusted source interprets it.

He has a vision of climate-smart agriculture where tradition gives way to global warming. He involves local farmers in his research by enrolling them when he places pods, and asking them to sms weekly weather reports to him that he correlates with the sensor data. As trust builds, he hopes to help them choose more climate-friendly crops and learn how to reallocate labour as seasons change.

Migrating from CRM to Big Data

Big data moved to centre stage from being just another fad, and is being punted as the latest cure-all for information woes. It may well be, although like all transitions there are pitfalls. Denizon decided to highlight the major ones in the hope of fostering better understanding of what is involved.

Accurate data and interpretation of it have become increasingly critical. Ideas Laboratory reports that 84% of managers regard understanding their clients and predicting market trends essential, with accelerating demand for data savvy people the inevitable result. However Inc 5000 thinks many of them may have little idea of where to start. We should apply the lessons learned from when we implemented CRM because the dynamics are similar.

Be More Results Oriented

Denizon believes the key is focusing on the results we expect from Big Data first. Only then is it appropriate to apply our minds to the technology. By working the other way round we may end up with less than optimum solutions. We should understand the differences between options before committing to a choice, because it is expensive to switch software platforms in midstream. data lakes, hadoop, nosql, and graph databases all have their places, provided the solution you buy is scalable.

Clean Up Data First

The golden rule is not to automate anything before you understand it. Know the origin of your data, and if this is not reliable clean it up before you automate it. Big Data projects fail when executives become so enthused by results that they forget to ask themselves, ?Does this make sense in terms of what I expected??

Beware First Impressions

Big Data is just that. Many bits of information aggregated into averages and summaries. It does not make recommendations. It only prompts questions and what-if?s. Overlooking the need for the analytics that must follow can have you blindly relying on algorithms while setting your business sense aside.

Hire the Best Brains

Big Data?s competitive advantage depends on what human minds make with the processed information it spits out. This means tracing and affording creative talent able to make the shift from reactive analytics to proactive interaction with the data, and the customer decisions behind it.

If this provides a d?j? vu moment then you are not alone. Every iteration of the software revolution has seen vendors selling while the fish were running, and buyers clamouring for the opportunity. Decide what you want out first, use clean data, beware first impressions and get your analytics right. Then you are on the way to migrating successfully from CRM to Big Data.

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The Connection Between Six Sigma and CRM

Six Sigma is an industrial business strategy directed at improving the quality of process outputs by eliminating errors and system variables. The end objective is to achieve a state where 99.99966% of events are likely to be defect free. This would yield a statistical rating of Sigma 6 hence the name.

The process itself is thankfully more user-friendly. It presents a model for evaluating and improving customer relationships based on data provided by an automated customer relations management (CRM) system. However in the nature of human interaction we doubt the 99.99966% is practically achievable.

Six Sigma Fundamentals

The basic tenets of the business doctrine and the features that set off are generally accepted to be the following:

  1. Continuous improvement is essential for success
  1. Business processes can be measured and improved
  1. Top down commitment is fundamental to sustained improvement
  1. Claims of progress must be quantifiable and yield financial benefits
  1. Management must lead with enthusiasm and passion
  1. Verifiable data is a non-negotiable (no guessing)

Steps Towards the Goal

The five basic steps in Six Sigma are define the system, measure key aspects, analyse the relevant data, improve the method, and control the process to sustain improvements. There are a number of variations to this DMAIC model, however it serves the purpose of this article. To create a bridge across to customer relationships management let us assume our CRM data has thrown out a report that average service times in our fast food chicken outlets are as follows.

<2 Minutes 3 to 8 Minutes 9 to 10 Minutes >10 Minutes
45% 30% 20% 5%
Table: Servicing Tickets in Chippy?s Chicken Caf?s

Using DMAIC to unravel the reasons behind this might proceed as follows

  • Define the system in order to understand the process. How are customers prioritised up front, and does the back of store follow suit?
  • Break the system up into manageable process chunks. How long should each take on average? Where are bottlenecks most likely to occur?
  • Analyse the ticket servicing data by store, by time of day, by time of week and by season. Does the type of food ordered have a bearing?
  • Examine all these variables carefully. Should there for example be separate queues for fast and slower orders, are there some recipes needing rejigging
  • Set a goal of 90% of tickets serviced within 8 minutes. Monitor progress carefully. Relate this to individual store profitability. Provide recognition.

Conclusion

A symbiotic relation between CRM and a process improvement system can provide a powerful vehicle for evidencing customer care and providing feedback through measurable results. Denizon has contributed to many strategically important systems.?

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