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Propelling business innovation and efficiency in financial services with AI

The panel

  • Alister Pearson, Senior Policy Officer, Information Commissioner’s Office
  • Sholthana Begum, Senior Advisor and Head of Regulatory Technology, Data and Innovation, Bank of England
  • Jody Gerbes, Risk Change, Americas, Deutsche Bank
  • Moderated by: Prab Pitchandi, Global Head, Chief Data Officer Initiatives, Tata Consultancy Services
  • Guest address by: Krithi Krithivasan, President and Business Group Head, Banking, Financial Services, and Insurance, Tata Consultancy Services

In recent years artificial intelligence (AI) has unleashed the power of data across a range of business functions in the financial services landscape. With the increasing adoption and augmentation of AI and machine learning applications comes the growing realisation of new dimensions for risk managers today – notably, bias and unfair outcomes from automated decision systems, as well as low transparency and accountability issues. External unpredictable factors and data constraints play a role too – in 2020, fraud detection systems were shaken by the turbulence of the Covid-19 pandemic, which served as a reminder for firms to enhance AI model design, testing and data governance methods for possible risk scenarios.

In addition to augmenting data governance and recalibration of internal processes considering external risk factors, regulation plays a vital role. Regulators across jurisdictions have been framing governance standards and guidelines for safeguarding risks and vulnerabilities associated with AI and machine learning. The uncertainty now lies in how senior financial services professionals are tackling these multifaceted risk concerns to augment AI capabilities and ensuring compliance for the future.

Amid evolving regulatory standards, this webinar brings together senior industry leaders across banking, capital markets and insurance to discuss critical risk nuances and related data issues:

  • Establishing appropriate risk safeguards as a part of AI-centric programmes
  • Supporting AI programmes to ensure compliance with evolving regulations and standards
  • Regulatory requirements and implementation concerns across jurisdictions
  • Managing AI risk management and governance frameworks to improve transparency and reliability
  • How much data is too much to ensure bias-free training of models.

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