The postings on this site are my own and do not necessarily represent the postings, strategies or opinions of my employer.
- On LLM Series.
- On Cloud Series.
- On Platformization of Banking Series.
- AI and Data: The Powerhouse for Banks
- Reimagining Recommendations: LLMs as a New Frontier (LLM Part 12)
- Uplift Bank Profits. AI to Maximize Customer Lifetime Value (Product Holding Ratio). (LLM Part 13)
- AI-Driven Customer Lifetime Orchestration for Banks (LLM Part 14) - This is co-authored with Quynh Chi Pham and Amelia Longhurst .
- Augmented Intelligence (LLM Part 15) in banking
The Ever-Evolving Concept of Intelligence
"The only true wisdom is in knowing you know nothing."
This quote, often attributed to Socrates, speaks to the ever-evolving nature of knowledge and, by extension, intelligence. What we consider "intelligent" is not static; it's a dynamic process of adapting to new information, solving novel problems, and accumulating wisdom over time. How do we draw on decades of experience to make a critical business decision? These seemingly different acts both rely on intelligence, but distinct types of it.
There is fascinating interplay of different forms of intelligence, particularly the distinction between fluid and crystallized intelligence. Fluid intelligence represents our capacity for quick thinking, problem-solving in unfamiliar situations, and processing new information rapidly. Crystallized intelligence, on the other hand, embodies our accumulated knowledge, skills, and experiences – the wisdom we gather throughout our lives. Understanding this distinction is crucial not only for navigating our own cognitive development but also for appreciating the potential of AI to enhance various aspects of our lives, including the banking industry.
This interplay of fluid and crystallized intelligence has profound implications for industries like banking. Imagine AI systems capable of both rapid data analysis (akin to fluid intelligence) and the application of vast amounts of historical financial data (similar to crystallized intelligence). Such systems can revolutionize areas like customer engagement through real-time analysis, and personalized financial advice by drawing on vast datasets of market trends and individual customer behavior. By understanding the core components of human intelligence, we can develop AI solutions that not only enhance efficiency but also create more meaningful and personalized experiences for customers.
Understanding the Two Pillars of Human Intelligence
Our cognitive abilities are not monolithic; they are a complex interplay of different processes, each with its own neural underpinnings and developmental trajectory. Neuroscientist Daniel Levitin, in Successful Aging, highlights the crucial distinction between two fundamental forms of intelligence: fluid and crystallized. Understanding these two pillars is key to appreciating both human cognitive potential and the possibilities of AI in fields like retail banking.
Fluid Intelligence: The Brains Dynamic Problem Solver
Fluid intelligence represents our capacity for thinking on our feet—solving novel problems, adapting to new situations, and processing information rapidly. Neuroscientifically, this relies heavily on the prefrontal cortex (PFC), the brains executive control center. The PFC orchestrates higher-level cognitive functions like working memory (holding information in mind for short periods), attention, and logical reasoning. Efficient communication between neurons within the PFC, facilitated by strong white matter connections, allows us to quickly analyze new information and generate solutions.
For example, a retail banking customer encountering a new online banking interface relies on fluid intelligence to navigate the unfamiliar layout and complete a transaction. Similarly, a bank employee using a new data analytics tool to identify emerging up-sell patterns is exercising their fluid intelligence. This type of intelligence tends to peak in young adulthood and gradually decline with age, reflecting changes in PFC function and neural connectivity.
Crystallized Intelligence: The Reservoir of Accumulated Knowledge
In contrast to fluid intelligence, crystallized intelligence represents the accumulation of knowledge, skills, and experiences we gather throughout our lives. This type of intelligence is rooted in long-term memory systems, particularly semantic memory (factual knowledge) and episodic memory (personal experiences). Neuroscientifically, the hippocampus plays a crucial role in forming new memories, which are then consolidated and stored in various cortical regions. Repeated use of this knowledge strengthens neural pathways, making retrieval faster and more efficient.
In retail banking, crystallized intelligence is evident when a seasoned financial advisor draws upon years of experience to provide personalized investment advice, recalling past market trends and individual client histories. A customer service representative quickly resolving a common customer issue by accessing a well-established knowledge base is also demonstrating crystallized intelligence.
The interplay between fluid and crystallized intelligence is essential for navigating the complexities of modern life, including the increasingly digital world of retail banking. While fluid intelligence enables us to adapt to new technologies and solve unforeseen problems, crystallized intelligence provides the context and expertise to make informed decisions and provide valuable services.
For instance, an AI-powered financial planning tool might use fluid intelligence-like algorithms to analyze current market data and project future trends. However, its effectiveness is greatly enhanced by incorporating vast amounts of historical financial data and established financial principles, mirroring the function of crystallized intelligence. This synergy between the two forms of intelligence, both in humans and in AI systems, will continue to shape the future of banking and beyond.
Mirroring Human Cognitive Abilities
AI Agents represent a significant leap in our ability to create systems that can perform tasks that typically require human intelligence. While AI doesn't perfectly replicate the complexities of the human brain, it draws inspiration from our cognitive abilities, particularly the distinct functions of fluid and crystallized intelligence. In the context of retail banking, a prime example of AI in action is the Customer Lifetime Orchestrator (CLO)—a sophisticated recommendation engine designed to maximize customer value throughout their relationship with the bank.
The CLO mirrors aspects of both fluid and crystallized intelligence to achieve its goals.
The CLO exhibits characteristics akin to fluid intelligence through its use of machine learning algorithms. These algorithms can analyze vast amounts of real-time data, including transaction history, website activity, demographics, and market trends (thanks to CDP). Just as the prefrontal cortex allows us to quickly process new information and identify patterns, these algorithms can detect subtle shifts in customer behavior, predict future needs, and identify emerging opportunities. For instance, if a customer suddenly starts making frequent international transactions, the CLO, acting with fluid intelligence-like capabilities, might predict an increased need for travel insurance or foreign currency exchange services. This rapid data analysis (thanks to tools like ElasticSearch or graph databases) and predictive capability reflects the adaptive and problem-solving nature of human fluid intelligence.
The CLO also embodies aspects of crystallized intelligence by drawing on vast stores of historical data and established financial principles. This "knowledge base" includes information about past customer interactions, successful product offerings, and market trends over time. Similar to how our brains store and retrieve long-term memories, the CLO uses this information to inform its recommendations. For example, the system might recognize a pattern of customers who, after opening a savings account, subsequently invest in mutual funds. By recognizing this pattern, the CLO can proactively suggest mutual fund options to new savings account holders, leveraging the "wisdom" gained from past customer behavior. This application of accumulated knowledge mirrors the function of crystallized intelligence.
The true power of the CLO, and AI in general, lies in the synergy between these two forms of "intelligence."
By combining the rapid analytical power of fluid intelligence-like algorithms with the accumulated knowledge of crystallized intelligence-like databases, the CLO can provide highly personalized and timely recommendations that maximize customer lifetime value. It's not simply about reacting to current events; it's about anticipating future needs based on both current trends and historical patterns (empathic banking empowered by AI Agents). This integrated approach, mirroring the interplay of fluid and crystallized intelligence in the human brain, is transforming how retail banks interact with their customers and manage their relationships.
Intelligence in Action
The convergence of fluid and crystallized intelligence within AI systems is revolutionizing the banking sector, moving it from a reactive, transaction-focused industry to a proactive, customer-centric one. While AI is impacting numerous areas of banking, from fraud detection to risk management, its influence on Customer Lifetime Value (CLV) is particularly transformative. CLV, a metric that predicts the total revenue a bank expects to generate from a customer throughout their relationship, is becoming increasingly sophisticated thanks to AI. By leveraging AI's ability to analyze vast datasets and identify complex patterns, banks can not only more accurately predict CLV but also implement strategies to actively enhance it.
One of the most significant ways AI enhances CLV is through personalized product recommendations. Imagine a retail banking customer, Sarah, who has been consistently depositing a portion of her salary into a savings account for the past year. Traditional banking approaches might simply send her generic promotional materials for other products. However, an AI-powered system, acting with fluid intelligence-like capabilities, can analyze Sarah's transaction history, identify her saving patterns, and even consider external factors like current interest rates and market trends. It can then predict, with a high degree of accuracy, that Sarah might be interested in investing her savings for higher returns. This prediction isn't based solely on current behavior; it also leverages crystallized intelligence-like knowledge. The AI system has likely analyzed data from thousands of other customers with similar saving patterns and identified a trend of subsequent interest in investment products. This historical data, combined with Sarah's current behavior, allows the AI to generate a highly personalized recommendation—perhaps suggesting a specific type of mutual fund tailored to her risk tolerance and financial goals. This personalized approach not only increases the likelihood of Sarah adopting the new product, thereby increasing her CLV, but also strengthens her relationship with the bank by demonstrating a genuine understanding of her needs.
Furthermore, AI can dynamically adjust these recommendations over time, reflecting changes in Sarah's life and financial situation. If Sarah receives a promotion and her income increases, the AI can detect this change and adjust its recommendations accordingly, perhaps suggesting premium banking services or more sophisticated investment strategies. This continuous adaptation, mirroring the fluid nature of human intelligence, ensures that the bank remains relevant to Sarah's evolving needs throughout her customer journey. By intelligently orchestrating these personalized interactions, AI is not only transforming how banks calculate CLV but also how they actively cultivate and maximize it, fostering stronger, more profitable customer relationships.
Enhancing Customer Lifetime Value with AI: A Retail Banking Perspective
In part "AI-Driven Customer Lifetime Orchestration for Banks (LLM Part 14)" we shared in details about why maximizing Customer Lifetime Value (CLV) is paramount. AI, particularly through sophisticated tools like the Customer Lifetime Orchestrator (CLO), is transforming how banks approach this crucial metric.
The CLO gathers data from a multitude of sources, creating a holistic view of each customer. This includes transactional data (purchase history, payment methods, frequency of transactions), demographic information (age, location, income), online behavior (website visits, app usage, interactions with online content), and even external data sources like market trends and economic indicators. AI algorithms, mirroring fluid intelligence, then process this vast and complex dataset, identifying patterns, correlations, and anomalies that would be impossible for humans to detect manually. For example, the CLO might notice a correlation between customers who open a new checking account and a subsequent interest in personal loans within six months. This rapid data analysis forms the foundation for predictive modeling.
Predictive modeling, a core function of the CLO, uses machine learning techniques to forecast future customer behavior. This is where the fluid intelligence-like capacity of AI truly shines. By analyzing historical data and identifying patterns, the CLO can predict the likelihood of customer churn (leaving the bank), product adoption (taking out a loan, opening a new account), and future spending patterns. Specific examples of predictive models used in CLV calculations include regression models (predicting continuous values like spending amount), classification models (predicting categorical outcomes like churn or product adoption), and time series analysis (forecasting future values based on past trends). For example, a survival analysis model might predict the probability of a customer remaining with the bank over a specific period, a crucial component of CLV calculation.
The insights gained from predictive modeling are then used to create personalized offers, services, and financial advice, mirroring the application of crystallized intelligence. The CLO draws upon its "knowledge base" (aka KAG) of past customer interactions and successful campaigns to tailor recommendations to individual customer needs and preferences. For instance, consider a customer who frequently travels internationally. The CLO, recognizing this pattern, can proactively offer travel insurance, a premium travel rewards credit card, or preferential foreign currency exchange rates. This personalized approach, based on both current behavior (frequent travel) and historical data about similar customers, significantly increases the likelihood of the customer taking up the offer, thereby boosting their CLV.
This orchestration of personalized interactions is the key strength of the CLO. It's not just about sending generic promotions; it's about delivering the right offer to the right customer at the right time.
By combining the rapid analytical power of fluid intelligence-like algorithms with the accumulated knowledge of crystallized intelligence-like databases, the CLO transforms CLV management from a reactive exercise to a proactive strategy. It fosters stronger customer relationships by demonstrating a deep understanding of individual needs and preferences, ultimately driving greater profitability and long-term customer loyalty.
The Synergy of Human and AI in Banking
While the Customer Lifetime Orchestrator (CLO) possesses remarkable analytical capabilities, its true potential is realized through collaboration with human expertise.
The synergy between human and AI is not a competition but a powerful partnership.
The CLO excels at processing vast amounts of data, identifying patterns, and generating predictions—tasks that would be overwhelming for human analysts. However, humans provide crucial context, creativity, and ethical oversight that are essential for maximizing CLV. For instance, the CLO might identify a segment of customers at high risk of churn based on declining transaction activity. A human analyst can then investigate the underlying reasons for this trend, perhaps identifying a local economic downturn or a competitor's aggressive marketing campaign. This human insight allows for more nuanced and effective interventions, such as targeted retention offers or proactive customer outreach.
The concept of "human-in-the-loop" is crucial for optimizing the CLO's performance and achieving higher CLV. This approach recognizes that while AI can automate many tasks, human judgment and intervention are still necessary for complex decision-making and ethical considerations.
In the context of the CLO, human-in-the-loop systems allow bank employees to review and refine the recommendations generated by the AI, ensuring they align with the bank's overall strategy and ethical guidelines.
For example, if the CLO suggests offering high-risk loans to a specific customer segment based purely on historical data, a human analyst can assess the broader economic context and potential risks associated with such a strategy. This human oversight prevents the AI from making potentially harmful or unethical recommendations, while also providing valuable feedback that can be used to improve the CLO's algorithms over time. This collaborative approach not only maximizes CLV but also builds trust and transparency, strengthening the relationship between the bank and its customers.
Conclusion: The Future of Intelligence in Banking and Beyond
We've explored the fascinating interplay between human intelligence—specifically the distinct yet interconnected forms of fluid and crystallized intelligence—and its reflection in artificial intelligence systems like the Customer Lifetime Orchestrator (CLO). Fluid intelligence, with its emphasis on rapid problem-solving and adaptation, is mirrored in AI's powerful analytical algorithms, capable of processing massive datasets and identifying intricate patterns. Crystallized intelligence, representing accumulated knowledge and expertise, finds its counterpart in AI's vast knowledge bases and trained models, allowing systems like the CLO to draw on historical data and established principles to inform their actions. In the banking sector, this convergence is revolutionizing CLV management, moving beyond static calculations to dynamic, personalized interactions that foster deeper customer relationships and drive sustainable growth. Looking ahead, we can envision even more sophisticated iterations of the CLO, capable of not only predicting customer needs but also proactively anticipating life events and offering tailored financial solutions before customers even realize they need them. Imagine a CLO that anticipates a customer's need for a mortgage based on changes in their family size and online search behavior for new homes, proactively offering competitive rates and personalized mortgage advice.
The future of intelligence in banking, and indeed across all sectors, lies in the continuous refinement of this human-AI collaboration. As AI systems become more adept at mirroring both fluid and crystallized intelligence, the role of humans will evolve to focus on higher-level tasks such as strategic planning, ethical oversight, and fostering genuine human connection.
We can anticipate the emergence of "augmented intelligence" platforms that seamlessly integrate human insights with AI-driven analytics, creating a powerful synergy that unlocks unprecedented levels of efficiency and innovation.
This evolution will not be limited to finance; it will permeate industries from healthcare to education, transforming how we solve problems, make decisions, and interact with the world around us. What are your thoughts on the future of intelligence in banking? How do you envision the continued evolution of human-AI collaboration shaping the financial landscape? Share your perspectives in the comments below.
