Society is creating and consuming more data than ever, and this tsunami of data promises market, customer and operational insights which financial institutions of all shapes and sizes across the globe must be able to leverage to remain competitive. After all, their customers expect personalized experiences, their employees require self-service access to trusted and timely data and insights to perform their roles more effectively, and their shareholders expect them to remain one step ahead of the competition—most of whom are investing heavily in data-driven digital transformations of their own.
One of the biggest challenges for financial institutions—along with companies across all industries—is how they can scale their data management and analytic capabilities to discover the nuggets of valuable insights that live within the streams of big data. Artificial intelligence (AI) and machine learning (ML) has become a critical enabler to intelligently deliver next best actions, recommendations, and predictive insights to the business. But as data now supports and scales data discovery and cataloging, data integration, data governance, data security, and more AI/ML has also become a required foundation in order to effectively manage the data itself. This is because AI/ML needs data management, just as data management needs AI/ML.
However, those in the financial industry looking to incorporate AI in their digital transformation must also be aware of and be prepared to address and mitigate some risks, because unethical use of big data could undermine many of the social purposes that financial services organizations care about. You want to know how your data governance efforts can ensure that your data and AI strategies continue to uphold your values of honesty, integrity, and respect for your customer’s rights, even as automation that bypasses a required human touch helps to scale your business.
Although customer data in particular is critical to innovation and growth, data misuse risks a loss of trust that could not only destabilize your organization, but the financial services system in its entirety.
Proper use of data and AI has a multitude of business benefits including the delivery of new products and services tailored to individual needs, enhanced customer experiences, financial inclusion for underserved individuals, better risk management, and cost savings from more efficient internal operations.
But poorly governed, unchecked use of data can lead to a variety of concerns, including: financial losses due to fraud, potential concerns around data privacy if customer data is used without consent, possible damage to customer relationships when customers are excluded from products or services due to real or perceived risks, regulatory penalties or reputational damage from misuse of customer data (and resulting loss of customers), operational costs stemming from fraud or cyberattacks, and even market disruption for companies that depend on pooled risk or cross-subsidization.
So just what does it take to be custodians of all this data? A dedicated focus on ethical governance and use of all data that needs to be considered across the entire “Data Supply Chain”