In risk management, the speed and accuracy of the data are of critical importance in effectively managing the risk. With the dawn of new-age technology, Artificial Intelligence (AI), and Machine Learning (ML), a huge stream of complex data can be identified, assessed, and arranged in a systematic format to aid management leaders. A collaboration between AI-ML and Risk intelligence can help firms to face uncertainty with confidence.
Importance of AI-ML in Risk Intelligence
Risk Intelligence is the process of identifying, assessing, and managing the potential threats and risks which may impact the organisation. It entails a broader concept including predictive risk analysis which ensures organisations are future-proof. The traditional Risk Management relies on historical data to manage current risks and has limited predictive capabilities. With the help of technology, one can access complex streams of data, monitor real-time developments, and conduct multiple scenario planning to mitigate risks.
The market share of Risk Management globally is estimated to grow to USD 110 Billion by 2032 and new-age technology is considered to be a major force in the growth. In order for firms to be risk-intelligent, a synthesis of technology and risk management is a must.
Applications of Technological tool in Risk Intelligence
- Data Collection and Analysis
- With the use of AI-ML, vast streams of data from different sources can be aggregated, assessed, and sorted according to their relevance. It helps in real-time monitoring which helps organisations make informed decisions.
- Threat Detection and Prediction
- AI-ML tools can be used to identify patterns using historical data and real-time updates and give alerts about emerging threats.
- Use of OSINT technology to keep track of development on social media, local news, and public records.
- Predictive Analysis
- With the help of AI-ML tools, organisations can identify, assess, and provide mitigating strategies in response to emerging threats.
- For instance, the use of AI-ML tools to track developments in a country with deteriorating socio-political stability can help companies to better gauge their operational continuity and safety in the country.
- Risk Matrix and Risk Prioritisation
- Technological tools can be used to assign risk scores to different emerging threats according to organisational weaknesses. An automated Risk Matrix can help prioritise critical threats on the basis of their likelihood and impact.
- Risk Prioritisation can help in the allocation of limited resources to reduce vulnerabilities and enhance resilience.
- Real-Time Monitoring
- AI-based dashboards can help in monitoring risks by tracking real-time developments and giving alerts for new threats.
- It plays a critical role in mass communication and information dissemination as it helps in alerting various stakeholders in a quick and timely manner.
- Fraud Detection
- AI can help monitor patterns within the operational system and organisational structure to detect any deviations.
- With the help of historical data, it can scan through heaps of data and identify anomalies and inconsistencies.
- Automate repetitive risk assessment
- Technological tools such as AI-ML can help in automating repetitive risk assessments including compliance checks, documentation, and scanning vulnerabilities.
A Right Technological Tool for Your Organisation – datasurfr
Mitkat’s AI-powered datasurfr helps organisations to stay abreast of potential risks and emerging trends. It provides an array of services including Critical Event Monitoring, Data Analysis, and Operational risk reports to make your organisation Risk Intelligent. Datasurfr along with insights from subject matter experts helps the organisation to manage its risks effectively and face the uncertain times with confidence.