Harnessing the power of risk analytics

By Farzaneh Golkhoo | Monday, February 12, 2024

Because conventional risk management relies on past experiences and expert judgment, bias and human error can creep into risk assessments; experts unconsciously seek out information that confirms pre-existing beliefs or expectations and are inclined to interpret data in a way that aligns with their initial assessment, potentially overlooking contradictory evidence. This confirmation bias can lead to an underestimation of certain risks or an overemphasis on familiar risks, hindering the objectivity of the assessment process.

Consider a construction project where the project team, influenced by optimism bias, might underestimate the time required for certain tasks or overlook engineering challenges. This bias can lead to overly aggressive project timelines and budgetary constraints, creating the potential for delays and cost overruns.
Individuals within an organization may interpret the same data differently, leading to inconsistencies in risk evaluation, and conventional risk management may neglect to consider emerging and complex risks: the risk of using a new technology—including evolving regulations and unforeseen technical challenges, geological, logistical, and environmental risks—tends to be more reactive in nature.

Be agile. Be adaptive. Be proactive.

In today's rapidly evolving business landscape, where new and unexpected risks can quickly emerge, relying solely on subjective judgments can leave organizations ill-prepared to address these challenges. To address this issue, many businesses are incorporating data-driven and objective risk assessment tools such as predictive modeling, scenario analysis, machine learning, and artificial intelligence (AI) to complement conventional methods, striving for a more comprehensive approach. Due to the increase in storage capacities, computing capabilities, and cloud services, organizations have encountered an explosion in the quantity and variety of data sources. But big data on its own isn’t useful unless it’s processed to extract value where advanced analytics comes in; in other words, it’s not enough to simply collect data. The data needs to be interpreted in order to be used effectively.

By applying advanced analytics techniques on stored historical datasets or real-time data generated by operations, risk managers can transform risk management into an objective and evidence-based practice, helping organizations make informed decisions and reduce the influence of subjective factors. This minimizes potential losses and helps organizations seize growth opportunities, including stakeholder engagement, adoption of innovative technologies, and quality assurance measures.

Project risk. Financial risk. Operational risk.

Risk analytics services can vary by sector. A tailored approach offers advantages by identifying, assessing, mitigating, and monitoring risk, whether it's transaction fraud in the case of a credit card company; default risk for a mortgage firm; project delay risk for a construction company; hazard and operability analysis for a planned or existing process/operation; cybersecurity for a digitized business operation; business impact analysis; or premium calculations for businesses or insurers.
In the realm of financial risk, risk analytics encompasses delinquency modeling, credit worthiness assessment, transaction fraud detection, billing fraud identification, predictive underwriting, and risk-adjusted premium calculations. Within operational risk, risk analytics involves the implementation of early warning systems, managing supply chain risks, addressing retail shrinkage, and assessing project-related uncertainties.
With cyber threats on the rise, organizations of all sizes are at stake. Risk analytics tools monitor network activity, detect vulnerabilities, and assess the potential impact of cyberattacks. This helps in fortifying cybersecurity defenses and responding swiftly to breaches.

What’s next?
Hatch has been at the forefront of innovation in risk management. By applying advanced data science, probabilistic models, and AI, we’re complementing instinct-based risk management with data-driven risk management, where subjective estimations can be supported by credibility and data-based validation. This enables risk managers to better manage potential risks and opportunities, giving our clients a significant competitive edge.

Our innovative solutions continue to improve current risk management practices and address the existing gaps and challenges in business interruption analysis, asset failure prediction, and qualitative project risk analysis. These solutions enable our clients to:

  • Quantify the impacts of natural and non-natural catastrophes on their operations in terms of business interruption and revenue loss.
  • Have an effective business continuity and disaster recovery planning.
  • Gain insight into their business interruption insurance coverage and assess the controls that contribute to operational resilience.
  • Enhance the safety of their systems by prioritizing mitigation actions based on their global impact.
  • Capture the interdependencies among projects risks and quantify their global impact.
  • Assess the effectiveness of control measures by running “what-if” scenarios.

Contact us to find out how our team provides decision-makers with valuable insights and enables them to allocate resources efficiently and choose the most effective course of action.