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Adoption of AI/Gen AI in Finance with Hariom Tatsat

Through incremental steps and learning from each stage of implementation, organizations can navigate the complexities associated with generative AI, ultimately reaping its considerable benefits.

The rapid adoption of AI and Generative AI in the finance industry is revolutionizing how businesses operate and make critical decisions. This week we have with us Hariom Tatsat who is currently serving as the Vice President in the Quantitative Analytics division of an investment bank in New York. With a wealth of experience in the field, Hariom is a seasoned Quant professional whose expertise spans predictive modeling, financial instrument pricing, and risk management. He has made significant contributions to various global investment banks and financial organizations throughout his career, showcasing his comprehensive knowledge and skills in quantitative finance. He has also co-authored a book called “Machine Learning and Data Science Blueprints for Finance”.

This week’s CDO Insights consisted of Hariom’s views on AI’s contribution to economic growth, its applications in finance, challenges in adoption, shifting industry perceptions, evolving talent requirements, and its overarching significance, all in concise yet thought-provoking inquiries.

AIM: Why has AI gained such importance in driving economic growth, and how is its integration into finance poised to rewrite the history of financial intermediation, risk management, compliance, and prudential oversight?

Hariom Tatsat: The key advantage of AI is that it can process a large amount of data to find useful insights, making operations faster while reducing cost. These features help businesses work more efficiently and increase overall productivity, leading to economic growth.

When it comes to the finance sector, AI has been seamlessly integrated into many applications including customer service, risk management, fraud detection, and many more. With AI, a lot of these processes are becoming more streamlined and efficient. AI algorithms can quickly match borrowers with lenders, predict the best terms for loans, and even automate many transactions, thus rewriting how financial intermediation is traditionally conducted. AI, with its predictive analytics and real-time data analysis, is enabling regulators and financial institutions to manage risks efficiently with fast responses to market changes, and aiding in foreseeing potential risks, which was time-consuming in the past. In the context of compliance, AI can significantly reduce manual monitoring by automatically checking transactions, operations, documents, and ensuring compliance with existing laws. It’s changing prudential oversight from a reactive practice to a proactive one, where potential issues are identified and addressed much earlier than before, thus ensuring a higher level of stability in the financial system.

AIM: What are the current and near-term applications of generative AI in finance, specifically in terms of augmenting existing processes, and how do you foresee these applications evolving in the near future?

Hariom Tatsat: The ability of alternative AI models to generate data in the form of text, code, etc., is a significant advantage to the finance sector, offering enhanced operational efficiency, risk management, and strategic planning.

On a broader level, generative AI will contribute to making documentation processes seamless and provide coding assistance to technology teams within financial institutions. In retail banking, it will enhance customer service and experiences by making the chatbots even more powerful and accurate. On the sell side, it’s poised to augment areas such as equity research, fraud detection, and credit scoring by streamlining these processes, identifying patterns in data, and generating insightful reports. It will also play a pivotal role in enhancing compliance and risk management, by automating the monitoring and analysis of financial activities and swiftly identifying and mitigating potential risks. On the buy side, particularly in algorithmic trading, generative AI could be utilized for further automating the algo trading process or for finding trading signals from the data. In portfolio management, it’s slated to assist in portfolio allocation and automate the risk tolerance questionnaire, which traditionally has been a manual process.

Thus, the utility of generative AI extends beyond just cost reduction and efficiency improvement; it also has the potential to generate additional revenue across various applications and domains within the finance sector. As generative AI models become more advanced and user-friendly, their applicability and transformative potential in finance are set to soar even higher.

AIM: What critical challenges need to be addressed for the successful adoption of generative AI in finance functions, and how can organizations maximize the adoption and impact of generative AI tools to broaden their application across various business functions, considering the current stability in AI adoption and limited scope of use?

Hariom Tatsat: On a broader level, in my opinion, organizations face three kinds of challenges when it comes to adopting generative AI in finance. Firstly, there may be resistance from senior management or leadership due to various reasons, possibly related to costs and risks. Secondly, a lack of resources can pose a barrier, whether it’s insufficient data, a shortage of skilled personnel, or inadequate infrastructure. Lastly, there are risks involved, such as privacy and compliance issues, or challenges with interpreting AI outputs.

To foster a smoother adoption of generative AI, it’s crucial for organizations to meticulously address these barriers. A pragmatic approach would involve leveraging existing resources to the fullest extent. Many organizations already have teams focused on machine learning, and the resources within these teams can be redirected to support the adoption of generative AI. Initially, embarking on this technological journey doesn’t have to be overwhelming; organizations should start with a small, manageable use case to better understand the technology and its implications. Gradually expanding the scope of generative AI applications, while ensuring that risks are meticulously monitored and controlled, can pave the way for successful adoption and optimization of generative AI in the financial sector. Through incremental steps and learning from each stage of implementation, organizations can navigate the complexities associated with generative AI, ultimately reaping its considerable benefits.

AIM: In the context of historically slower AI adoption in financial services, what specific changes or developments have triggered a shift in perception, leading industry leaders to now view AI as a growth enabler in the sector?

Hariom Tatsat: AI has been employed in the financial sector for quite some time, particularly in customer service, fraud detection, and credit scoring, where the problem statements are clear and the use cases are well-defined. However, the recent surge in generative AI advancements, exemplified by technologies like GPT, has provided industry leaders with firsthand experience of the extensive capabilities of generative AI in automating mundane, repetitive tasks through code, text, and data generation. 

This exposure has sparked a shift in perception among leaders, helping them realize that AI is more than just a buzzword; it holds immense potential in enhancing efficiency, reducing costs, and even opening new avenues for revenue generation. This growing awareness has also instilled a sense of urgency among industry leaders about the necessity to embrace AI; they understand that failure to adapt to this technological evolution could potentially place them in a position similar to that of Nokia during the advent of the iPhone. Through this lens, AI is increasingly seen not merely as a tool but as a crucial growth enabler within the financial sector.

AIM: What are the evolving talent requirements specific to the finance industry in light of AI adoption, and how do you anticipate AI will significantly impact the finance workforce in the coming years?

Hariom Tatsat: Regarding talent requirements, it’s evident that the demand for AI expertise will escalate in the future as AI adoption within the financial sector continues to expand. With AI becoming a part of finance, people who understand both finance and AI will be in demand. Being good at data science, machine learning, and understanding AI ethics is expected to become more valuable. Meanwhile, many financial institutions already have teams of data scientists and machine learning engineers, whose skills can be redirected towards AI or generative AI projects.

As for the impact of AI on the financial workforce, numerous reports suggest a potential reduction in job roles, particularly those involving repetitive or mundane tasks. It will be important for professionals to keep learning to keep up with what’s changing in their field. While some jobs might go away, new and exciting jobs will come up, especially around using AI effectively. These new jobs will need people who are good at working with AI technologies. Thus, while AI may phase out certain traditional roles within the financial sector, it simultaneously creates innovative job opportunities specially for those who will reinvent themselves for this new AI-driven finance world.

AIM: In one word, how would you summarize the pivotal role of AI in the future of finance?

Hariom Tatsat: Transformational.

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