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You may be quite staggered to learn that in 2012 alone the Association of Certified Fraud Examiners (ACFE) 2012 Report to the Nations* estimated ‘that the typical organisation loses 5% of its revenues to fraud each year.’ In many cases, evidence of the fraudulent activity is captured in the online systems and databases that support the business. However, using existing tools – such as spreadsheets - mean that this web of company systems can be difficult to navigate and analyse effectively.

You may be quite staggered to learn that in 2012 alone the Association of Certified Fraud Examiners (ACFE) 2012 Report to the Nations* estimated ‘that the typical organisation loses 5% of its revenues to fraud each year.’ In many cases, evidence of the fraudulent activity is captured in the online systems and databases that support the business. However, using existing tools – such as spreadsheets - mean that this web of company systems can be difficult to navigate and analyse effectively.

The ACFE has developed its ‘Occupational Fraud and Abuse Classification System’ that identifies over 40 types of fraud schemes that fraudsters use to victimise their companies.

Today’s Data Analysis tools are designed for fraud and audit analysis and are used by successful businesses to data mine for evidence of fraud proactively and to make a big impact on reducing the company’s fraud risk exposure from both a financial and a reputational perspective.

Distribution of Losses

Of the 1,388 individual fraud cases reported CaseWare corporate in Canada, 1,379 included information about the number of dollars that were lost to fraud. The median loss for all of these cases was $140,000, and more than one-fifth of the cases involved losses of at least $1 million. The overall distribution of losses was notably similar to those observed in previous 2010 and 2008 studies.

“There Is NO one size fits all biscuit-cutter solution”

 While there are certain areas in every business that should always be looked at for fraud, there are many others that can impact. This makes it difficult to find and fight fraud since most applications and ERPs are customised to the business they support, so there is no silver bullet.

“IDEA - A Smart Approach For Success”

Ensure your team is aligned on a clear vision, strategy and plan for using audit software tools to fight fraud. Don’t set the bar too high as you get started. Set realistic expectations for success. Don’t expect everyone to have the same capabilities. Task the folks that are more IT savvy with getting and importing the data in IDEA®. Once the data is in the tool, encourage the people that are more analytical to perform the evaluation. You can even use a workshop approach as a team to brainstorm and help build capability across the group.

Getting Started

Here is an example of a simple three-step approach that can be successfully applied to any business or industry:

1. Identify which processes or areas in the business could have the highest risk of fraud

2. Select a high-risk process to evaluate and identify how fraud could occur in the particular process/area and evaluate:

  • How can someone perpetuate the fraud?
  • What would/could they do to conceal it? (i.e. nothing; make journal entries to cover their tracks; alter back-up documentation, etc.)

3. Determine what the fraudulent activity would look like in the data in order to determine what data is needed and what data analyses to perform.

Easy To Run Analysis For Fraud Detection

People that have championed the implementation and use of audit software tools like IDEA know the incredible value of being able to convert raw data into business information from disparate sources.

They know about the power of analysing complete populations of data to reveal the needles in the haystack. They also know that their implementation was successful because of their dedication to setting teams up for success.

Built-in routines and easy-to-use interfaces make fraud detection analysis easy with IDEA®. Regardless of the business or industry that you are in, there are some simple common sense analysis routines that you can use to begin to look for evidence of fraud.

“The Practical Use of Data Analysis – let me tell you a story”

A case study produced by Fraud Specialists, The Fraud and Risk and Advisory Group, revealed how they were hired by a Global 500 retailer to investigate why credit card chargebacks in their e-commerce business were increasing at an alarming rate. The activity was becoming frustrating to manage and costly for the business.

Upon initial review, it appeared that a majority of the chargebacks were identified by the credit card companies, as being fraudulent transactions. Using IDEA®, they analysed the entire transaction population of chargebacks over a 12-month period and found that there were common elements in the data that indicated that most of the fraudulent activity was somehow connected. Simple summarisation analyses of the originating IP addresses revealed that the fraudulent transactions were originating from the same individual or group of individuals. Duplicate key detections on billing addresses, shipping addresses, telephone numbers, emails addresses and fuzzy matching of names further supported the linkages in the transactional activity.

Once the connection patterns were identified, The Fraud and Risk Advisory Group used IDEA® to look at the transactional activity that had not yet been evaluated by the credit card companies for chargebacks. With a high degree of precision, they were able to predict which existing transactions were likely to result in additional chargebacks.

The Fraud and Risk Advisory Group evaluated the company’s existing fraud screening methods and helped to develop a more robust set of upfront screening processes including a transactional fraud scoring model to improve the business’ ability to spot the activity before the order was shipped. As a result, chargeback rates reduced significantly. They were reduced to levels below the normal rates they were experiencing before they noticed the increase in the fraudulent activity.

“CONCLUSION - Get the bad news as early and as quickly as possible!”

The timely detection of fraud directly impacts the bottom line, reducing losses and reputational risks for an organisation. Data analysis tools, when integrated with the strengths of an organisation’s fraud and audit teams, can reap tremendous benefits in the proactive fight against fraud.

With the bounty of regulatory and compliance demands instituted over the past decade, the internal controls debate is over; it is no longer a question of “if” an organisation should implement a complete fraud detection and prevention program, but rather how quickly that program can be put into place. Get the IDEA!

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