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Data Analytics for Business Advisory

PRIMARY CONTACTS:
Adam J. Goode CPA

In association with the Automation and Data Analytics Process Team (ADAPT), our business advisory professionals harness the power of data management, data discovery, data visualization, and problem-solving through data analysis to quantify value and identify missed opportunities for our clients.

In today’s fast-moving environment, there is a growing risk that relevant data is not analyzed correctly, misunderstood, or even not considered at all. Our professionals combine decades of industry-specific knowledge with state-of-the-art data analysis tools and techniques to design procedures that efficiently and effectively maximize value and minimize risk.

Our professionals flip the traditional paradigm – instead of spending time sorting and gathering data, we use leading tools and analytic programs to quickly move beyond this stage to gain knowledge and deliver insight.

How can Data Analytics be applied to Forensic & Investigative Accounting?

  • Visualize the relationship between nodes in a fraud network
  • Determine the volume and velocity of movement of financial resources
  • Extract usable data from large sets of raw data (data mining), including structured (i.e. purchase card data) and unstructured (i.e. e-mails, images) data for unusual activity, patterns, correlations, etc.
  • Proactively identify vulnerable transactions and parties based on their relative risk profile

Big Thinking in Action:

  • Used network analysis to visualize the interaction of the flow of funds between various bank accounts
  • Performed “fuzzy matching” analysis to find vendors with similar names or addresses
  • Developed an automated script to evaluate general ledger data and identify transactions-of-interest based on numerous qualitative and quantitative factors
  • Developed a model to determine at-risk purchase card transactions based on the volume of purchases, location data, and the individual’s role within the company

Learn how Schneider Downs used Data Analytics to develop a Pricing Damage Model in our Litigation Support Engagement Success Story.

How can Data Analytics be applied to Litigation Support Services?

  • Aggregate and combine data from disassociated legacy systems to discover new insights and assist in quantifying supportable damage claims
  • Provide well-documented and detailed analyses that are repeatable and can be followed at each step of the process, from the source document to the conclusion
  • Develop procedures backed by statistical methods that are credible and will support expert testimony

Big Thinking in Action:

  • Determined a statistically reliable sample from a population that would serve as a preliminary estimation of the characteristics of the population and recommended a sampling method that would result in a representative sample
  • Designed and implemented a time-sensitive pricing model that calculated profitability and related damages for unique inventory stock keeping units (“SKUs”)
  • Developed an intuitive dashboard that allowed the client to identify specific instances and trends of unaccounted-for employee time through analysis of employee physical location data and timecard data

About Schneider Downs Business Advisory

The Schneider Downs Business Advisory group is experienced in handling business interruption matters with forensic accounting professionals and litigation support specialists. Additionally, our Business Advisory group managed our research and consulting work on the Paycheck Protection Program (PPP), so the team is uniquely qualified to prepare potential claims related to losses from COVID-19.

Learn more about our Business Advisory services at Business Advisory or contact us for more information.

Learn how we’ve Solved Big Problems For our clients

Big Problem: Company Impacted By Ransomware.

Big Thinking: Restore System On-site And Avoid Six-figure Ransom.

Read Case Study

Big Problem: Inefficient Tax Credit Realization.

Big Thinking: Identified A $900,000 Tax Credit, Nearly Twice As Much As Prior Years.

Read Case Study

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