For the forward-thinking enterprise, data science offers tools that help evaluate and track risk factors and tackle complex business challenges.
FREMONT, CA: Risk management is an integral part of any enterprise. All businesses face a variety of risks, and the risk management practice works towards accelerating the businesses’ ROI and reducing their losses. Anyone who works in risk management is not new to data science. Risk management is highly dependent on data, and professionals have been using analytics for several years. In fact, enterprises are the hub of data and have been pioneers of data analytics. Here is a look at some of the areas of risk management where data science is increasingly being applied.
In enterprises, large amounts of data is created in the form of transactions, consumer behavior, and economic data. Data science can be used to explore and analyze such big data to reduce risks. Since businesses are dealing with huge data sets, data science tools can be really supporting in exploring and analyzing data sets from various perspectives. Such analysis can offer almost real-time intelligence and allow risk managers to identify potential risks and act immediately and more effectively in mitigating risks.
• Fraud Management
In operational risk, data science can be used to create efficient systems to identify and prevent fraudulent activities and regulatory breaches. Legacy, operational risk management methods are slow and need a case-by-case approach to identify fraud, while hackers use newer technologies and strategies to conduct malicious activities. All data sources can be captured and analyzed along with standard data sources to detect fraud before it becomes a scandal.
• Building Predictive Models
Data science can be used to build more robust predictive models to assess customers. Apart from the standard datasets such as customer demographics, new data from sources like social media and marketing data can be included in building models to get increased visibility into customer behaviors. The traditional data, together with new information, can support risk managers develop highly robust risk indicators. Data science can also help banks identify warning signals in their existing business.