Forensics and the Fourth Industrial Revolution

The challenge of growing threats

Beginning about 250 years ago, humankind began to experience a series of industrial revolutions; from the First Industrial Revolution of steam power in the 1700s, to the second which saw the advent of electricity and internal combustion about a century later, and next to the third which benefited from digital technology at the close of the millennium. Then within just a decade, we saw the beginning of the Fourth Industrial Revolution—which represents the technology breakthroughs of the past two decades that are transforming how people live, work, and interact. Protecting data, intellectual property (IP), and finances is a growing priority in business board rooms and the highest offices of governments, as criminals proliferate and adapt to more sophisticated controls and monitoring. As data volumes continue to increase, so does the potential for data to be stolen and misused. An estimated 1.7 megabytes of data will be created every second in 2020, for every person on earth. By 2025, data creation is predicted to reach 163 zettabytes or 163 trillion gigabytes.

Effective antifraud programs, systems, and controls have been shown to significantly reduce fraud losses, but even organizations that have them can encounter investigation hurdles.

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Elements of an integrated approach

Core elements of an integrated, analytics-driven approach include:

Data integration: A fundamental capability of an advanced analytics approach is the integration of structured and unstructured data from internal and external sources into risk models. Structured data alone often provides a severely limited view of patterns that might point to fraudulent activity.

Predictive tools: Predictive tools can help investigators discover the root cause of problems faster and more effectively. Artificial intelligence, machine learning, and statistical concepts of cognitive analytics, in combination with skilled forensic investigation, can unlock secrets to fraudster motives and methods.

Refined risk scoring: Transactions don’t commit fraud—employees, vendors, customers, and other external actors do. Advanced, data-driven models incorporating text analytics and network analysis enable organizations to rank risks at the individual or entity levels, rather than the transaction level.

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A technology/analytic enabled culture drives future challenges

It is ironic that present-day analytic/technology advances have transformed our culture in a way that has brought us full circle back to the start of the Industrial Revolution in the 1700s. The original impact of the Industrial Revolution was to centralize means of production from individual households into centralized facilities like factories. Culture consolidated around cities and towns and away from individualized, agrarian lifestyles.

Today, 21st-century technology has enabled people to once again disengage from a centralized culture, allowing them to work at home or offsite, connected only by the networks they choose or that their current employer promulgates. They are armed with powerful tools that still allow them to impact the economy and the corporations that drive them as individuals as well as collectively through networks. This, in turn, has fostered a culture of data analytics entitlement, where many feel they can act in any way they like, some for social good, many for personal enrichment.

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