Manage Risk Using Data Science and Machine Learning Capabilities
Data science might seem to be an interesting subject, and you may wonder that wonderful results are obtained while data science is being used. However, as a matter of fact, 80% of the work involved in data science is all about cleaning, collecting and organizing data. That doesn‚Äôt seem to be very interesting now‚Ä¶ Does it?
Data science holds importance in the area of risk management. Technology studies have included machine learning along with data science to analyze the processes and challenges that can come up, trying to reduce the risk situations. Before data science was introduced, companies spend a lot of time, effort, and even money in data collection and preparation.
Data science has always been an intensive process where data is processed, and repetitively analyzed and predicted. Until now, the process of data gathering and processing has been manual; however, now most of it is automated. Most of the tasks that involve audit procedures, security, and fraudulent practices are all automated making them safe and easy to manage.
Usage of manual or labor-intensive processes can expose the system to various risk-based scenarios leading to damaged reputations, data losses, penalties, and cash leakage. The finance and operations of any organization is at a greater level of risks such as frauds, errors, policy violations, and compliance.
Risk management deals with the advanced version of data science‚Äôs cognitive computing capabilities. Some examples of cases in risk management include, identifying duplicate invoices, unusual credit terms, high-risk user access, ghost employees, and so on.
Machine learning is a technological form of Artificial Intelligence in which computers observe the data patterns from either direct experience or through instructions, and make informed decisions themselves. Just like humans, computers tend to learn from their past experiences, and gradually function independently without any human intervention.
With the combination of data science and machine learning, hundreds of transactions and activities can be closely monitored and the authorities can be alerted about any highly suspicious transactions. A user-friendly dashboard can help to record and rectify the user conflicts and violations.
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