FinovateEurope 2022 session: “Keynote: How to use data analytics and AI to create human-centric financial products”
Speaker: Inma Martinez, digital pioneer and artificial intelligence scientist, G7, OECD and EU digital transformation
- Inma Martinez’s keynote highlighted the potential of AI-powered data analytics to create human-centered financial products. AI models can generate deep insights and reclassify customer segmentation in more granular ways than unaided human analysis.
- She highlighted the strengths and limitations of AI and the crucial role humans play in using technology to personalize the customer experience.
- While AI models possess enormous computational power – far beyond human capabilities – to process complex datasets, they currently lack the cognitive power to understand the context for decision-making.
- She noted that big tech companies like Meta are hiring philosophers and psychologists for their machine learning teams to improve their algorithms.
- In a subsequent Q&A session moderated by Insider Intelligence Principal Analyst Eleni Digalaki, Martinez explained that chatbots currently available in customer service provide a frustrating user experience because they are decision tree-based and rely on on predefined rules to conduct conversations.
- Martinez said generative chatbots that produce original conversations are possible today, but no one has implemented them for customer service yet. It would be a labor-intensive business with an uncertain return on investment. It would also require skilled manpower which would be difficult and expensive to gather due to the current talent war.
What does that mean
- Humans play a crucial role in teaching algorithms about the context in which humans operate to enable them to fully understand why they make decisions.
- Algorithms are not psychologically trained and have a hard time understanding the emotion behind decisions because “humans don’t live in a computer world”.
- One example Martinez offered is of humans training computer vision algorithms in online verification processes that ask them to prove their identity by tagging various objects in images.
- While AI-powered automation will replace inefficient processes, such as claims management in insurance, fears that AI will supplant skilled human workers are largely unfounded.
- We expect the next paradigm of AI use cases in financial services to be centered on “human plus digital”: applying human expertise and cognitive functions to the computing power of models of AI to derive large-scale insights from large and complex datasets.
- Finance employees will need to upskill and retrain to identify potential use cases for AI and, as in the chatbot example, learn how to effectively train it to create hypotheses about what drives human behavior. , as well as to understand what lies behind humans’ very nuanced use of language. .
- The potential of AI to improve the client experience is particularly relevant in wealth management: wealth managers can empower their advisors with AI-powered solutions and insights to personalize advisor-client interactions.
- For example, Merrill Lynch’s Client Engagement Workstation centralizes client information from millions of interactions that its advisors can use to personalize client communications and recommend tailored financial products and investment plans.
Go further: Check out some of our articles on AI and personalization