Role of data analytics in risk management

Today, data availability is limitless, technology is getting smarter and sharper, and business dynamics are more challenging than ever before. With this constantly increasing complexity and speed of change, harnessing the power of an effective risk management process has become imperative for the strategic and long-term success of an organization.

Traditional risk management approaches are based primarily on subjectivity and individual perceptions, which may not be the optimal way of dealing with the emerging risk landscape. Hence, the approach needs to evolve, rather transformed from risk management to risk enabled performance management (REPM). With REPM, the focus shifts to mapping the business drivers critical to achieving the objectives and helping business stakeholders identify relevant emerging risk trends and metrices for its effective monitoring. The effectiveness of this approach lies in granulating the business drivers into key strategies and tasks, without losing focus on the macro perspective.

Embedding data analytics and other technologies across the risk management process

With the rise of new risks, the use of data analytics and other advanced technologies has become more important than ever. Incorporating data analytics in risk management is key. The risk management approach must embed these technologies across the entire risk management process, starting from identification to assessment to mitigation to monitoring. Each step of the process presents a great opportunity for leveraging the power of analytics. Let’s look at the application across each of these stages:

• Risk identification: For organizations, risk can originate from internal (inadequate process and controls, business portfolio, lack of funding, etc.) and/or external environments (macro-economic changes, political environment, regulations, climate change, among others). These risks can hinder the company from achieving their goals and targets. Today, industries and economies around the world are becoming bigger and more complex. This has resulted in the creation of data that is unprecedented in terms of velocity, volume and variety. With the emergence of big data, it is possible to integrate the internal and external data points for identifying emerging risks, which may be latent in nature. (Relevant tools and assets - web scrappers, data ingestion tools, analytical tool and visualization platform)
• Risk assessment and prioritization: Assessing and prioritizing risks is extremely important to ensure adequate focus of the management on critical areas. Organizations must extend their understanding of the risk landscape to the data that is readily available and associated with strategic risks, down to the operational and functional level. Aligning the data to the risk profiles and indicators allows an effective profiling of the risks in terms of impact and likelihood. Thus, analytical models can be built to detect potential risks, fully assess their financial and other associated impact and to create an analytic framework that can begin to balance the financial and strategic impacts against the investment to mitigate and fully manage the risks. (Relevant tools and assets - data ingestion tools, data models, analytical tool and visualization platform)
• Risk response and mitigation: An effective risk response must evaluate various available options and not only decide the best suitable alternative but also consider the consequential impacts emanating from the selected option. It also needs to track and monitor the response effectiveness for timely course correction, where required. Various risk modelling techniques can be adopted for simulating “what if” scenarios (based on past risk occurrences and future predictions) by integrating various data elements, which aids in deciding the most optimal response strategy. Data analytics can also be leveraged to track implementation effectiveness of the mitigation plans deployed. This could be particularly useful to alert the mitigation owner in case the timeline of implementation is not on track and needs a re-visit/focus. (Relevant tools and assets - AI/ML models, scenario models, workflow tool, analytical tool and visualization platform)
• Risk monitoring: Timely and robust risk monitoring is critical in today’s dynamic environment. Identification of key risk indicators for each of the risks is important to measure the trends and movement of the data parameters linked to the risks. It is important to identify both lead and lag indicators and identify the most suitable and relevant data source for monitoring these parameters. Risk analytics has an important role to play. An effective risk workflow technology can be deployed for automated alerts and reminders (by linking this with the data source platform), which enables the business stakeholders in timely actions. (Relevant tools and assets - workflow tool, analytical tool, and visualization platform)
• Risk reporting: Timely and relevant risk reporting can be achieved by embedding the entire risk management lifecycle on an integrated technology platform. Such platform can assist in generating scheduled and on-demand reports, enables real time view of the risks and helps in keeping a track of past learnings. Data analytics can be effectively deployed and integrated with such platform and acts as a data feed layer for each of the stages in the risk management process. (Relevant tools and assets -workflow tool, analytical tool and visualization platform)