Top Ways To Identify Fraud and Unusual Patterns Through Data Analytics

There are many indications today where customers, clients and many others believe that fraudulent billing occurs in all industries across our nation and the world for that matter.

There is much to be considered in the review of company methods that can determine fraudulent behavior, from data analytics to advanced analytics that will expose all different sorts of activity that tend to take advantage of customers and clients on a financial level.

Fundamental Data Analytics Techniques include the ability to determine and find the following:

  • Duplicate transactions
  • Uneven amounts
  • Frauds caused by Ratio Discrepancies
  • Frauds caused by trends

While we have some fundamental data analytics techniques that can be used to evaluate the billing systems of any company, there are the different, in-depth motions that can be taken throughout data analytics while also providing those more advanced analytics results.

With some of the basic information on data analytics there are six basic steps taken to cover all of the information of financial data that can be fraudulently affected. With the ability for fraud to be uncovered here are six potential steps for data analysis:


1. Data Pre-Processing

missing or incorrect data

There are included techniques for detection, validation, error correction, and filling up of missing or incorrect data. It is great to see that companies can help rely on proactive and automated data analytics to help clean up the battle against fraudulent financial activity all around.

There is always the value of making sure that records are updated to the clearest numbers possible, ensuring that there is nothing fraudulent sent out to the customer and client, as well as nothing fraudulent received inside the files of the company.


2. Calculation of Various Statistical Parameters

Calculation of Various Statistical Parameters

Another useful approach being used for fraud prevention and detection involves the calculation of patterns and parameters in the actual data. The rationale is that unexpected patterns can be symptoms of possible fraud. Some of these statistics are averages, quantiles, performance metrics, probability distributions, and so on. For example, the averages may include average length of call, average number of calls per month and average delays in bill payment.


3. Models and Probability Distributions

business models

These models and probabilities can be of various business activities, either in terms of various parameters or probability distributions. Then, within data analysis there is benchmarking used to compare a company’s performance to competitors, industrial standards, historical data and budgeted data. Also, in relation to the fundamental data analytics techniques there are duplicates testing, expressions and equations that can be examined within any company’s numbers. There is the ability for reporting with charts and Excel files for all comparisons that can help to break down all accounting numbers.


4. Computing User Profiles

Computing User Profiles

This also includes matching algorithms to detect anomalies in the behavior of transactions or users as compared to previously known models and profiles. Techniques are also needed to eliminate false alarms, estimate risks, and predict future of current transactions or users.

In addition to overall data analysis, there is the advanced analysis available to work with applied filters of financial records. There are applications such as user-defined criteria, advanced filtering, meta-tagging and more filters that are included with reports that are concluded.


5. Time-Series Analysis of Time-Dependent Data

Time analysis

There is the ability to compare data analyzed throughout points in time, compared to data that occurs free of historical data patterns. With a company found to help with data analytics and advanced analytics, there are a number of methods available to help with the efficiency of accounts receivable, accounts payable, customer payments, vendor payments and many more.

One interesting point found is the evaluation of Frequently Used Values. In this situation there is always the importance of identifying both frequently used values as well as those which appear unexpectedly in accounting reports. Data analytics can help red flag fictitious transactions included in those numbers. In addition, this would help identify transactions that occur during non-business hours or employee vacations.


6. Clustering and Classification

clustering and classification

This helps to find patterns and associations among groups of data. Another set of data analytics that help with the recovery of pure numbers include the Fuzzy Logic and Gaps that can occur in financial reporting. While fuzzy logic may be anything from unclear equivalency in addresses or other information, there are also gaps in the numbers reported whether it be the actual financial amounts, invoice numbers, purchase order numbers or many more.


7. Ratio Analysis

Ratio Analysis

Another useful fraud detection technique is the calculation of ratios for key numeric fields. Like financial ratios that give indications of the relative health of a company, data analysis ratios point to possible symptoms of fraud.

For example, auditors concerned about prices paid for a product, could calculate the ratio. Three commonly employed ratios are:

  • the ratio of the highest value to the lowest value (Maximum/Minimum)
  • the ratio of the highest value to the next highest (Maximum/2nd Highest); and
  • the ratio of the current year to the previous year.

A large ratio indicates that the Maximum value is significantly larger than the second highest value. Auditors and fraud investigators would be interested in these unusual transactions as they represent a deviation from the norm. Unexplained deviations could be symptoms of fraud. In a number of cases, high ratios have identified payments incorrectly made to the vendor.


8. Trend Analysis

Trend Analysis

Analysis of trends across years, or across departments, divisions, etc. can be very useful in detecting possible frauds. Another useful calculation is the ratio of the current year to the previous year. A high ratio indicates a significant change in the totals.

This step or method could be partnered with some graphing techniques to provide a visual representation of the data and can highlight patterns or anomalies that might indicate areas of further examination for future purposes.


9. Duplicate In-depth Analysis

Duplication analysis

Usually, one would assume that account number - vendor number combinations, would be unique. Therefore, the existence of dealings with the same invoice number - vendor number combinations would be an unexpected pattern in the data.

The documentation of possible duplicate transactions would be a possible indication of fraud that should be examined. However, fraud indications are only that – indications - and care should be taken to properly examine the transactions before jumping to inferences. Transactions that look like duplicates may simply be progress payments or equal billing of monthly charges. It is possible to search for duplicates on one or more key fields.


10. Traditional Filtering and Pivoting Method

Traditional Filtering and Pivoting Method

Traditional companies still use this method wherein it only identifies those records meeting user-defined criteria. This method is used to extract transactions outside of expected norm. However, despite of the limitations mentioned above, it can further filter or analyze results using additional analysis techniques.

Also, traditional methods maximize the use of pivot tables to have an interactive data summarization tool that sorts, counts, total, or gives the average data. This has really been useful to show the “big picture” of all the data gathered.


11. Digital Advanced Analytics

Advanced Analytics

Digital Advanced analytics is an advanced application of data analysis, is a new tool for auditors and fraud investigators interested in preventing and detecting fraud. In fact, digital analysis is a case where millions of transactions make the identification of fraud symptoms easier to find then when there are only a few thousand transactions.

The patterns in the data become more obvious and focus attention on the fraud. The good thing with digital and Advanced analytics is that they already have automated systems that would make the analysis processes of your enterprise or company a lot more efficient and faster. There are a lot of Advanced Analytics online and you just need to choose the best company that would best fit your data analysis needs.



At the end of each analysis, the most important thing to consider is the summarization of all the data you’ve gathered. With all of these methods and steps toward fraud detection, and keeping all of your company’s numbers clear, there are still a lot of things to consider including the fact that all of these are moving into the IT-advanced methodology.

There are a number of benefits to working with the technological methods of companies who can help provide the automated continuous methods of data analytics for the benefit of clean records a mentioned above.

There are also a number of benefits that can be received from fraud detection including reduced exposure to fraudulent activities, reduced costs associated with fraud, determine vulnerable employees at risk to fraud, organizational control, improves results of the organization, and gains the trust and confidence of the shareholders of the organization.

Author’s Biography:
Brian O’Donnell is the Vice President – Marketing and Business Strategist of Attunix. He is the Co-founder of Attunix, a Microsoft Gold certified service provider. A results-driven executive responsible for bringing Attunix capabilities to market, strategic planning, and new product & service offering development.