The role of Big Data in risk assessment

The application of big data in risk management helps eliminate the culture of visceral decision making by companies. The focus on data-driven decision making helps devise rational decisions while advancing the maturity of the organization’s security culture.

In data-driven risk management, companies collect and analyze data from multiple sources and use it to preemptively identify, anticipate and mitigate problems in business operations. The use of Big Data, AI and machine learning helps companies decide what problems may arise in most business activities to devise solutions before they occur.

What is a risk assessment based on Big Data?

A data risk assessment is the process of reviewing locations that store and manage sensitive data, including intellectual property and personally identifiable information.

Why is data risk assessment important?

A data risk assessment helps business understand all potential threat vectors that can lead to security or privacy breaches by taking a systematic approach, reviewing where sensitive data is located, who accesses it, and any changes made to data access controls.

Also Read: “What business problems can be solved with alternative data?

What parameters can be evaluated?

  • Legal, regulatory and industry compliance posture.
  • The organization’s baselines for risk tolerance.
  • Potential vulnerabilities that increase the likelihood of a data leak or breach.
  • Key security performance indicators.
  • Additional data security investment needs.

Use Cases

Credit risk (predictive power)

Can be used to build predictive models related to future demand and supply. Social media and marketing data, as well as data from all types of transactions, will provide deeper visibility into customer behavior.

Operational fraud risk management

Used to create an effective system for detecting and preventing fraudulent activity and regulatory non-compliance. New technologies analyze standard information to detect fraud before it becomes a problem, helping organizations to be legally defensible, especially in fraud cases, by allowing them to easily collect and access data.

Qualitative risk management

Covers the impact of risk on the business or project in numbers. This numerical information projects the risk analysis according to time and cost contingencies.

Quantitative risk management

Covers the impact of risk on the business or project in numbers. This numerical information projects the risk analysis according to time and cost contingencies.

Stages of risk assessment

Risk identification: For organizations, risk can originate from internal and/or external environments. These risks can prevent the company from achieving its objectives and goals. This tool allows the integration of internal and external data points to identify emerging risks, which may be latent in nature.

Risk assessment and prioritization: Aligning data with risk profiles and indicators allows to effectively profile risks in terms of impact and probability. Thus, analytical models can be built to detect potential risks, fully assess their financial and other associated impacts, and create an analytical framework that can begin to balance financial and strategic impacts with investment to fully mitigate and manage risks.

Risk response and mitigation: Various risk modeling techniques are adopted to simulate “what-if” scenarios by integrating various data elements, which helps decide the most optimal response strategy. Data analysis can also be leveraged to track the effectiveness of the implementation of deployed mitigation plans.

Risk monitoring: Timely and robust risk monitoring is critical in today’s dynamic environment. Identifying key risk indicators for each risk is important to measure trends and movement of data parameters linked to risk.

Risk reporting: At this stage, scheduled and on-demand reports are generated, enabling real-time insight into risks and helping to keep track of past learnings.

At PREDIK Data-Driven we help companies implement and integrate these technologies into the entire risk management process, starting with identification, assessment, mitigation and monitoring.

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