Augmenting the reliability and fairness of AI in financial services
How AI propels business innovation and efficiency in financial services
Financial institutions’ AI adoption journey is driven by their business model, product portfolio, innovation ambition, and regulatory status. With growing reliance on AI in several domains, it has become necessary to establish a robust AI governance framework that involves oversight of the AI model lifecycle, enterprise policies, and data governance norms. As part of their AI strategies, banks need to establish well-defined policies to augment transparency and facilitate AI risk management.
The adoption of AI has added new risks that need to be addressed by financial institutions.
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Input data related risk: Poor data quality or gap in coverage
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Data privacy risks: Sharing of personal data with TPPs, usage of client data beyond specified purpose
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Security risks: External attacks that may contaminate data and systems
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Bias and lack of transparency: Bias from uneven classification or inaccurate inferences
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Compliance risks: Low compliance with transparency, third-party dependencies
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