We are excited to share our exclusive interview with Shobha Iyer
John Bensalhia speaks to Merck`s Shobha Iyer about the benefits, challenges and security practices of using AI in finance.
Speaking about Building Enterprise Data Mesh on Cloud at this year's Big Data & AI World Frankfurt show is Shobha Iyer. Shobha works as a Lead Architect for the finance domain in the Group Functions of Merck.
While her academic background is in chemistry, Shobha comes from a strong investment banking background. “I worked as an SME for variety of Global Enterprise organisations, ranging across retail & investment banks, stock brokers and hedge funds in London, New York, Paris, Netherlands and Germany.”
“Building solutions for complex trading applications, cyber security and NLP automation, my career organically moved towards Big Data and AI.”
When discussing the merits and issues with respect to using AI in the world of banking, Shobha says that there are many challenges. “Challenges include dealing with sensitive and confidential data; and dealing with Data Bias. As well as this, there is a model risk when using AI in banking. Plus, adopting the right security measures and at the same time building Federated Data governance, given the complexity of products.”
Despite these challenges, there are also benefits to using AI in the field of banking.
“Some benefits from AI to state include deriving data insights to make informed decisions in a timely manner; reducing operational cost; and predicting market trends and leverage trends for growth.” Shobha adds that AI can also help with assistance in assessing risks, and improving efficiency and productivity while reducing costs.
Security remains a high priority when using AI in finance. Shobha comments: “Security issues, to name a few are cyber-attacks, handling sensitive and confidential data.”
“To overcome risks, enablement of data culture in an organisation is a must,” continues Shobha. “Data culture can ensure a timely response to guarding data against cyber security threats. Implementing effective data governance also protects against potential security risks to adhere to compliance and audit requirements. It is also important to define, develop, and implement transfer, termination, and retirement processes for AI systems and to continuously monitor the quality of AI and ML models.”