Financial organizations face unique challenges in effectively guarding their data, achieving regulatory compliance, modernizing their systems, and gaining insights from data.
FREMONT, CA: Banks and financial services firms of all sizes are more than ever concerned about risk and compliance management. In responding to the pandemic, global regulators have responded by encouraging financial institutions to support customers in surviving this crisis and land on their feet. To this purpose, many regulatory deadlines have been postponing to reduce the operational burden to some extent. Here is how the current risk and compliance environment for banks and financial institutions, strategies for successfully deploying governance, risk, and compliance programs, and how technology can be used to adopt a holistic approach to risk and compliance m
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Regulated data is everywhere across legacy systems and in modern cloud storage. Conventional technologies that have previously dominated the discovery landscape virtually guarantee that firms will miss dark and sensitive data lurking in the organization. These tools may only see a kind of data, leaving a host of vulnerable data open to risk. A machine-learning method to data discovery upends that uncertainty, making it possible to locate, clean up, and handle decades of legacy data that a financial institution might have and have no idea that they have.
Financial organizations can leverage an ML-based discovery-in-depth method to inventory their data by identity, content, type, and sensitivity, gaining full visibility into all regulated and vital data of all types — from structured to unstructured. Using machine learning for correlation, financial institutions can find and classify data relationships, align data, and identify associated data down to the identity level. From on-prem to the cloud to data lakes, structured to unstructured, financial institutions must take a proactive approach to know their data.
Financial institutions need to maintain a laser focus on reducing risk — and deploy effective risk-reduction across the enterprise. By accurately finding at-risk data such as overexposed, incomplete, or ungoverned data; redundant, duplicate, derivative, or similar data; data movement violations; permissions violations, firms can enable their teams to initiate remediation workflows and take swift action on data breaches.
