Analytics to monitor risks
The fallout from highly visible instances of misconduct—including reputational damage, material losses, and increased regulatory focus—have led financial institutions to treat conduct risk as an important priority. As a risk category, however, conduct has proved difficult to monitor effectively with traditional controls and testing. The varieties of potential misconduct are numerous, and transgressing individuals or whole departments find ever-changing ways to circumvent rules. In addition, sample-based tests such as transactional reviews are not effective in finding isolated instances of misconduct.
The Advanced-analytics solution for monitoring conduct risk report
Advanced analytics and machine learning can help institutions “connect the dots” across customer and other data to detect conduct risk comprehensively and cost-effectively.
Making use of advances in data and analytics, institutions can transform conduct detection and replace extensive manual controls and verification activities. A number of leading institutions have started on this journey, putting in place monitoring analytics that detect infrequent instances of misconduct, such as inappropriate sales, before significant financial and reputational damage is sustained. An effective conduct-risk analytics monitoring program will be defined by the following capabilities:
- Connecting the dots across individual activities
- Finding the needle in the haystack
- Mining customer interactions with natural-language processing
- Employee-conduct transparency
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Report by McKinsey & Co.