Understanding influences driving noncompliant behavior
Identifying the difference between required and actual behavior will not reveal why people do not comply. Is it because they are not aware of the compliance requirement? Was the compliant choice too complex or were they deliberately noncompliant? This is where the real value of behavioral science kicks in as it uncovers behavioral insights into why noncompliance has occurred, allowing the organizations to learn more about users’ engagement and habits to adopt a more focused approach toward the behavior that is causing such disruptions.
The fundamental shift with this method is that it helps organizations take a more human-centered approach to compliance by analyzing trends and patterns in a specific country, business unit, or region and identifying high-risk teams, individuals, and business units. With the behavioral insights they’ve gathered, risk and compliance leaders can carefully craft interventions at different levels, i.e., policy, process, program, or population, to help the organization reduce liability and high fines.
For example, one of our clients identified suspicious behavior when analyzing patterns on six months of data. It noticed an employee was making facilitation payments to a government official on account of gifts given to third parties. This employee had claimed an allowable gift expense on a travel and expense card. Traditional data analytics did not flag this transaction as noncompliant, as it met the defined threshold and receipt requirements. By discerning trends and the sources of risk, our client could then focus on the right areas to prevent such behavior in the future.
The insights you get from behavioral science and analytics can be applied across the life cycle, right from policy design to enforcement and implementation, depending on what’s encouraging the noncompliant behavior (figure 1). These insights can help risk and compliance leaders design new practices, suggest improvements, and explain why people react in particular ways.