Risk Management
Risk management is an integral part of any institution. All organizations face different risks, and data science can help any business with their risk analysis practices with the goal to maximize the return on investments (ROI) of enterprises and reduce their losses.
A financial institution generates large amounts of data - from banking transactions, consumer behavior, market price fluctuations to economic data. Data analytics can be useful when exploring and analyzing huge datasets to reduce risk. Such analysis can provide real-time information and enable risk managers to identify potential risks and act faster and more effectively to mitigate those risks.
Five Steps for a Successful Risk Analysis
Risk identification
For companies, risk can arise from internal and/or external environments. These risks can prevent a company from growing and achieving its objectives. Due to the fact that industries and economies around the world are getting larger and more complex, the volume and data variety have diversified unprecedentedly. With the advent of big data, it has become possible to integrate internal and external data points to identify emerging risks that may be latent.
Risk assessment and prioritization
Risk assessment and prioritization are crucial to ensure that critical areas are adequately addressed by management. Aligning data with risk profiles and indicators allows for effective risk profiling in terms of impact and likelihood. In this way, analytical models can be developed to identify potential risks, fully assess their financial and other impacts in order to mitigate and fully manage risks.
Risk response and mitigation
An effective risk response must evaluate the various options available and not only select the most appropriate alternative, but also consider the consequential impacts from the selected option. By integrating different data points, various risk modeling techniques can be adopted to analyze past occurrences of risk and their future projections to help select the most appropriate response strategy.
Risk monitoring
Timely and solid risk monitoring is critical in today's fast-changing world. The identification of key risk indicators for each of the risks is important for measuring trends and changes in the parameters of the data associated with risks. A technology for effective risk management can be deployed for automatic alerts, notifications and reminders allowing business owners to take actions timely.
Risk reporting
Timely and up-to-date risk reporting can be achieved by embedding the entire risk analytics cycle into an integrated technology platform. Such a platform can help create automatic and on-demand reports providing real-time risk visibility. Data analytics can be efficiently deployed and integrated with such a platform and acts as a data feed layer for each stage of the risk management process.