With Q1 closing, AI, data challenges take center stage
By Tracy Lai, MPC Strategist, FinTech Consultant, and Partner at LYSTAR GROUP
It is almost the end of Q1, 2023, closing out the warmest winter in recent memory. With little snowfall in the tristate area and other parts of the world, the threat of global warming looks real. As the world embraces new technology, two emerging trends are garnering attention worldwide.
ChatGPT, the AI chatbot, has been a hot topic this winter, as people openly question what professions are at risk of being replaced. This reminded me of early-day discussions about machine learning, when we were debating the opportunities and risks of predictive analytics.
Edgar Prez, a recognized thought leader in global tech and finance, sees ChatGPT and generative AI as a compelling new approach to idea and content generation. Unlike traditional machine learning, which focuses on classification, prediction and optimization, he noted generative AI enable machines to learn from patterns in data and use that knowledge to create something entirely new. For example, ChatGPT can provide logical, in-depth answers to a variety of topics.
In addition to being a keynote speaker, Prez has published books in multiple languages, including English, Chinese, and Bahasa Indonesia. He has also served as corporate trainer and business consultant for billion-dollar private equity and hedge funds. His opinion are sought on matters relating to quantum computing, artificial intelligence (machine and deep learning), cybersecurity and financial regulation. Previous roles include vice president at Citigroup, senior consultant at IBM and a strategy consultant at McKinsey & Co.
Prez also acknowledged that implementing early-stage generative AI can be complicated, citing the following challenges:
Potential for bias: Because AI is designed by humans, its potential for bias and discrimination can impact accuracy of its results.
Potential for inaccuracy: Because AI consumes large amounts of data very quickly, humans need to use high-quality data to train generative AI system effectively.
Potential for deep fakes: AI systems can potentially produce content that is so realistic that it can be difficult to distinguish from human-generated contents, which raises ethical questions.
What do you think?
Considering how fast ChatGPT is being adopted, it’s worth questioning how generative AI will impact financial services. Could it potentially disrupt existing workflows? What will projects look like? It’s clear we need to address existing challenges of data utilization, especially for banks and financial services institutions.
While some schools have banned students from using ChatGPT to write their assignments. Associate Professor Ethan Mollick of the Wharton School of the University of Pennsylvania, encouraged his students to use ChatGPT, even showing them how to use the tool to generate meaningful content while advocating human and ChatGPT collaboration.
Ongoing discussions
On Feb. 28, 2023, Fintech Nexus hosted an online discussion on modern data challenges in financial services with Doug Lopp, CTO of Bend by FNBO and Chuck Kane, vice president, product management, Precisely. Todd Anderson, chief content officer at Fintech Nexus, moderated the discussion. Participants agreed that technology and business are becoming more aligned as MBAs increasingly guide IT decisions, and shared the following viewpoints:
Build a supportive framework: Builder, buyer, and partner must all collaborate to build a supportive business framework. Technology is just an enabler for success, which requires ongoing collaboration, flexibility and continuous pivoting to meet evolving requirements.
Drive data management best practice: Leveraging customer data, such as address, occupation, and other consumer behaviors, is critical to data accuracy, integrity and usage.
Carefully evaluate tech upgrades: Decisions regarding replacing legacy system vs. implementing new technology need to be carefully evaluated while considering related costs and risks of replacing decades-old legacy systems. Companies can also explore more practical approaches for building and adjusting new technology on top of legacy systems to achieve new features and capabilities.
Drive security and compliance: Resolve cybersecurity issues to ensure compliance and pass inspections and audits along the ever-changing digital journey.
Tracy Lai provides strategic and business development solutions to executives and entrepreneurs. Her professional experience includes working at Fortune 500 companies including Intel and RBS, and startups. Her expertise covers business development, risk management, project management, and investor relations, in Financial Services, FinTech, Innovation, Technology, and Education. As a management consultant and FinTech advisor, she holds multiple senior roles with a focus on cross-border and cross-industry collaboration, including projects in the U.S., Asia, and Europe.
