Not If, But When: How to Monitor and Manage Your Cyber Risk

Not If, But When: How to Monitor and Manage Your Cyber Risk

Buoyed by news and social media coverage of online threats and cyberattacks, cybersecurity is all the rage today. Indeed, whether we’re talking about the recent Iranian online assault on worldwide universities or the cyberattack on the city of Atlanta (which shut down Wi-Fi at the world’s busiest airport), cybersecurity is constantly and rightfully in the spotlight.

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New Data Tools for Your 2017 Fraud Examinations

GUEST BLOGGER

Jeremy Clopton, CFE, CPA, ACDA, CIDA
Director, Big Data & Analytics, Digital Forensics
BKD, LLP | Forensics & Valuation Services

“New Year, New You” can be found everywhere from email subject lines to magazine covers to marquees at local fitness center. January is the time to begin new things. With that in mind, here are a few of the new items to consider in your next fraud examination.

First, let’s talk about some new methods to consider:

  • Advanced analytics: Rather than relying on sampling and rules-based queries alone, take your analytics to the next level. Incorporate correlation across disparate data sets, outlier detection based on multiple attributes and look for patterns across data sets that indicate anomalous activity. 
  • Text analytics: Easily one of my favorites and one of the most overlooked. There is a lot of value to be extracted from text —names, places, events, topics and even tones of communication may be extracted. These elements can help build the foundation of a case and enhance interviews and interrogations.
  • Machine learning and artificial intelligence: The more cutting-edge of the recommended approaches, machine learning and artificial intelligence are increasingly valuable in complex and large-scale investigations. These are the foundations for predictive coding, which allows you to review a large set of documents, communications or transactions in a manner that is both efficient and effective. Supervised machine learning allows you to “teach” the computer what to look for and return similar results. Whereas, unsupervised machine learning allows the computer to “teach” you what trends, patterns and anomalies exist in the data set. 

Last, here are some data sources you may not have considered in the past:

  • Communications Data: You’re likely thinking that communications data isn’t something new to consider—  you have used email, phone records, text messages and others for years. Applying text analytics and machine learning to email can help you learn about the dynamics, happenings and relationships in an organization before you interview a single individual. What’s more, leveraging tone detection may uncover the conversation about a scheme that isn’t explicitly discussed as such.
  • Internet of Things: The Internet of Things is all the rage. With robots, voice recognition technology and artificial intelligence being incorporated into more and more products, there is data being captured in places we never thought possible. For example, Amazon Echo’s Alexa was recently subpoenaed in a murder case  in Arkansas. This example shows just how much data we have surrounding us each and every day.

These are just a few of the new items for you to consider as you embark on your examinations in 2017. As the year progresses, I will include posts on each of these in the context of examinations, as they make news and describe how you can incorporate them into your approach. I will also discuss other emerging technologies that may reshape how a fraud examination is performed.