Data Analysis Beyond the Numbers

ArticleFebruary 2012

Insurance / Legal / Forensic Investigation

Simon Oddy, Partner in the New York office, highlights the value that can be gained from including a forensic accountant with data analytical skill set in your team.

One of the challenges lawyers, claims professionals and forensic accountants are increasingly facing is how to tackle the mountain of information which is provided either in the early stages of a claim or through the discovery process.

As forensic accountants, lawyers and other litigation professionals, more and more we are provided with a vast amount of information and the suggestion from the Plaintiff’s Lawyers or Insured Representatives that this documentation supports their claim.

As part of a team, our aim is to make sense of this data through analysis and in doing so provide tools to the claims professionals which will allow them to determine their strategy fully armed with the knowledge that the information provided has been considered. And furthermore, has been considered in a cost effective manner.

In this discussion, we aim to highlight the value that can be gained from including a forensic accountant with data analytical skill set in your team.

This discussion does not address the work that will be performed on the narrative, correspondence and factual document review that the legal team will need to undertake. What it does address, is what can be achieved through a detailed analysis of the financial data contained within a large document production.

As well as increasing in volume, this data is being provided in various formats and from various sources – excel, access databases, hard copy pdf documents as well as internal accounting system information. With this comes the need to organize, analyze and understand the content of this array of information, in what is usually a short timeframe.

Managing Case Strategy with Data Analytics

Traditionally, the Forensic Accountants contribution to a litigated case is the damages piece. What are the damages relating to the case? However, the role of the Forensic Accountant has expanded far beyond this role particularly in the area of data analytics.

Data analytics is an approach that can be applied to a large volume of data to provide information against which decisions on further analysis and case management can be made (see sidebar). Consequently, the initial analysis of such financial data is critical to address the areas of focus and the case strategy.

It is likely that in the not too distant future, if you are not already doing so, you will be handling a case in which ten thousand documents are provided as support for a damages claim. With the changes in the ability of companies to process millions of transactions daily, in multiples of what could have not been performed several years ago and the courts allowing data extracted files to submitted into evidence, the question for the litigators becomes how well do they understand and know the data?

Document production can contain tens of thousands of documents, especially in cases with a large number of claimants, large Multi District Litigations and Class Actions or spanning a long period of time. This may mean that the individual claim and its supporting documents may be easy to understand and review, however a large product liability case affecting thousands of people will amount to millions of dollars in claims, and large volumes of data. Both financial and non financial data.

Drawing from experiences, an initial review of the analysis of the data contained within such production can be incredibly informative and provided a wealth of useful information. With this knowledge and understanding of the data, the strategy of the case can be planned – this of course is of no surprise to attorneys. The problem arises because Attorneys are confronted with time and cost constraints so simply reading every line item is often an unrealistic goal. This is where you’re forensic accountant with a good understanding of data analytics can change the case management strategy.

From the data analysis, the forensic accountant has an ability to provide key statistics and exceptions within the data. Much like when we analyze financial data pertaining to a vendor fraud scheme, patterns, trends and anomalies appear. These same patterns, trends and anomalies are also relevant in other data sets that might be submitted in court. Such data sets might include names, addresses and zip codes, product (UPC) codes, installation dates, failure dates, claim descriptions, descriptions on the fault reported or other failure criteria. Note that these categories all appear non financial in nature. However, it would be short sighted to consider that only financial data needs an analytical review. The findings from a review of non financial data, can significantly impact the case.

An examination of some examples will illustrate the types on findings and the impact they can have on a case:

  • In an MDL pharmaceutical product liability case knowing that 30% of the claims have attributes that do not qualify them for the MDL. It seems obvious but perhaps not if the details are buried in medical records and the only way of figuring it out is to create a database of information on each claim.
  • Identifying that 5% of the transactions have time stamps that do not match the date of the transactions. In fact they were entered just as the company realized that it may be facing some serious litigation following a product recall. This fact proving that the transactions were not maintained in a contemporaneous manner and therefore the exhibit does not qualify as a business record resulting in the judge denying the evidence.  
  • In a case where 1000’s of hacked trades resulted in significant losses for the company knowing that 5 of the unauthorized trades were discovered before the insurance case contract was issued thereby resulting in the insurance contract being denied.

Unique Aspects of the Modern Complex Fraud Case

Unraveling Complex Analysis

In addition to the analysis of financial and non financial data in say a large products liability claim or MDL, there is value to be gained from having a forensic accountant on the team in a fraud case

Fraud cases are becoming more and more complex and the scale of the frauds and the number of transactions under pinning the activity is increasing dramatically

There is a subtle difference in the data analysis needed. Here, an analytical review of claim supporting data and the underlying transactions relating to a fraud is often needed to gain a full understanding of the fraud and the claim presented.

In this instance, much of the analytical review is of transactional and financial data. This is often necessary at the outset to understand the claim presented and to determine the extent to which the claim might be supported and covered.

All the traits of an overall analytical review remain – the data set, either created by the forensic accountant or provided as part of the case management needs to be reviewed, analyzed and decisions made based on the results of that analysis.


In conclusion, data analytics is becoming an increasingly powerful and necessary tool in quality case management. The ability to be confident that the document production has been considered thoroughly for both its financial and non financial attributes is important.

The analytical review of a document production containing 2000 medical files, the result of a product failure affecting many individuals, can unlock many strategic doors to the management of the case. And this can be performed in a cost effective manner.

The Forensic Accountant with a data analytical mindset can enhance the litigation team’s knowledge of the case and the underlying facts. This extends beyond simply the traditional financial aspects of the case and includes analysis of non financial data sets. This in turn enables the right decisions to be made about the relative strengths and weaknesses of a specific case.

What is data analytics – the process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making.

Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes.

Armed with this information, how to select your data analysis team:

  • Recognize the difference between the skill set of an IT Forensic Expert, Data Analysis Expert and a Forensic Accountant Expert. Experts can specialize in one or more of these skills so it is important to understand their unique skill set. A strong forensic accountant may not be a strong data analyst. You are looking for a good balance.
  • Ensure that you ascertain an expert who has the experience in creating concise charts and graphs. This will ensure that the finding can be easily understood by juries and judges. Identify those who can take a complex analysis, and simplify for ease of understanding
  • Know what software your data analysis expert specializes will work with. The use of Excel and Access will potentially cost you more time compared to the experts who use much more powerful software analytical tools. Thought still fairly powerful tools, they require a lot of manual data processing.
  • Identify a data analyst / forensic accountant that is flexible is there analysis. A useful team member will be aware of the context of the analysis in the underlying case and will be capable of adapting the analysis as the direction of the case and the information available changes.
  • Ensure the accountant is current with software developments which will enhance efficiency and understanding of the case and enable the data analysis via an array of software available. This efficiency should result in the funds available for consultants being spent in the right place – the analysis, not the data creation. This is where the most value will be gained. Up to date software, will also make a thorough analysis possible, while ensuring the results are simplified to a level that can be understood by all.

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