In earlier blogs, human capital risk was defined as the risk inherent in an organization and workforce. Broadly, this risk includes risk factors emanating from people behavior, organizational culture and workforce mix (e.g., employee vs. contractor or third-party partner).

The implications of human capital risk include reputational damage (e.g., discriminatory employment practices), employee and public safety risk, gaps in regulatory compliance, executive criminal prosecution, information security breaches, employee fraud, intellectual property (IP) theft, poor business performance and business resiliency concerns. For example, in the financial services sector recent regulatory fines have ranged from $30M to $3B as a result of employee actions and organizational practices in violation of regulatory guidelines.

Broadly the downstream impact of human capital risk are grouped in the categories below:

  1. Financial risk such as multi-billion-dollar government regulator fines, costly organization-wide overhauls in risk management controls. Risk probability can be assessed using predictive models of misbehavior such as instances of insider trading or likelihood of IP theft.
  2. Legal and Reputational risk such as potential criminal and civil prosecution against the organization and executives. Similar to financial risk, analysis of behavioral patterns and predictive models can help identify situations that can eventually lead to employee litigation, misconduct and discriminatory actions which can have significant negative impact if not addressed. More often than not, there is information building up in internal systems, and/or sentiment embedded in structured and unstructured data that can be continually analyzed for indicators of legal or reputational risk.
  3. Strategic Transformation risk such as internal resistance to change and/or lack of key talent to enable broad enterprise level transformation. Organizations can use Artificial Intelligence (AI) driven assessment tools such as Transformation GPS1 to assess their capabilities to implement successful change, and tools such as Accenture’s Digital Dexterity Assessment (DDA) to assess future readiness of their workforce. The DDA tool measures key components of digital fluency – mindsets, behaviours, and practices to assess an organization’s readiness for digital change, helping leadership teams define digital goals with a view of leading industry practices and enhance productivity and performance. Collectively these tools provide critical insights into strategic human capital drivers of transformation risk.
  4. Compliance and Cyber-Security risk such as failure to meet regulatory requirements, internal security guidelines and consent order remediations. Organizations can track employee compliance metrics and related Key Control Indicators that indicate employee non-adherence, and generate alerts to managers of emerging trends which enables proactive decisions.
  5. Safety and Wellness risk such as risk to employee and customer health and well-being. Organizations can generate alerts on safety risk using AI tools which have been successfully deployed in workforce segments such as oilfield engineers, field crews, transportation services, etc.
  6. Operational risk such as safety hazards and disruptions to business operations due to employees’ failure to follow safety protocols.
  7. Environmental/Sustainability risk resulting from failure to enforce or track employee actions, or from failure to implement the right people practices and incentives, that directly impact an organization’s carbon footprint and use of water, energy and other non-renewable resources.

Detection, Prevention and Root Cause Analysis of Human Capital Risk

An effective risk detection and mitigation (or prevention) framework leverages AI and advanced analytics to continually capture and analyze data from multiple sources to detect signals that indicate high probability risk events and estimated severity of those events. These signals can be analyzed in real time across various risk categories to guide management actions to proactively prevent, mitigate or optimize such risk and create insights for further improvement of the firm’s risk management framework.

Methods underlying a Risk Analytics capability typically include the following:

  • Expert Rules which can be set up for known patterns of risk (e.g. compliance, work hours, absenteeism, etc.)
  • Anomaly Detection identifies unusual patterns embedded in data that can help assess previously unknown high-risk behaviors
  • Predictive Models are trained on large data sets to develop alerts based on predicted risk against what are typically known risk areas
  • Text Mining and Sentiment Analysis using Natural Language Processing (NLP) methods can help identify signals generated in large volumes of data and generate alerts to risk managers. An Accenture white paper2 on the applications of NLP methods cites several examples of NLP based analytics that can identify risky behaviors, dis-engaged employees and potential actions that can compromise a firm’s reputation or market position.
  • Organizational Network Analysis (ONA) looks at patterns of interaction across employees, vendors and customers to identify patterns that are predictive of risk

Realtime analysis of data with advanced platform analytics, visualization and reporting tools is key to effective deployment of these solutions. Widely accessible AI driven platforms and advanced visualization tools creates an opportunity to reimagine the possibilities for organizations to predict and proactively manage human capital risk with data captured in real time.

Building an integrated, multi-source data capability is feasible in cloud enabled analytics environments such as the Accenture Insights Platform (AIP) and the Solutions.AI engine. These solutions are supported with data governance and security protocols. Advanced reporting tools enable predictive models with risk alerts that can be embedded into management dashboards to prompt preventive or mitigation action.

As depicted in Figure 1, data from multiple internal and external sources is leveraged for a comprehensive and predictive view of human capital risk.

Figure 1: Human Capital Risk Inputs

Click/tap on image to enlarge.

Cybersecurity and Human Behaviors

Today, the impact of cybersecurity is not just limited to systems. It impacts every facet of the organization and has a direct impact on business performance.  Approximately 60% of all cyber-attacks can be directly attributed to human behavioral vulnerabilities.

Click/tap on image to enlarge.

To prevent and protect themselves from potential cyber threats, organizations are seeking to understand and assess employee behaviors that can result in security risks. Accenture’s Cybersecurity Behavior Assessment (CyBA) Global Research finds that:

  1. Right cybersecure behaviors drive positive cybersecurity outcomes, but only 34% of employees globally demonstrate cybersecure behaviors.
  2. There are significant opportunities for improvements in cybersecure behaviors related to device securement, phishing vigilance, etc. within most organizations.

What is CyBA?

Cybersecurity Behavior Assessment (CyBA) is a data driven and scientifically validated solution that helps organizations to identify the weakest link of employee cybersecurity behaviors and provides actionable interventions to achieve cyber resilience.

Building an Enterprise Risk Analytics Capability

Organizations today have the opportunity today to re-think risk from a broader lens that includes people and organizational elements. Moreover, there is a significant opportunity to leverage advanced data and AI/Analytics to significantly improve the effectiveness and value proposition of the risk management function.

We recommend that organizations enhance their Enterprise Risk Management (ERM) function with an Advanced Analytics capability that can continually generate insights on multiple risk drivers to situationally assess potential impact, leveraging platforms that enable rapid analysis of structured and unstructured data. Steps towards building an analytics capability includes building a partnership with your organization’s Enterprise Data and Analytics team as well as adding data science skills to the ERM function.

For additional information on how Accenture’s analytics capability can help your organization, please reach out to our team at


  1. “Decoding Transformation – From Evidence to Value”, Accenture Strategy, 2019. Access at:
  2. “Top Natural Language Processing Application in Business – Unlocking Value from Unstructured Data”, Accenture, 2019. Access at:

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