Lessons from Inside the Banking Machine: Using Banking Insights to Help People Choose Better in a Noisy World

A Personal Finance Reimagined Origin Story

This article is a personal and professional retrospective from Jeff Hulett, a career banker, behavioral economist, and founder of Personal Finance Reimagined (PFR). It explores how decades spent optimizing financial decision systems for large banks ultimately led to a mission shift: empowering individuals to become better decision-makers themselves. Rooted in classical liberal values and sharpened by behavioral economic insights, this piece reveals the decision gap that many consumers face—and how PFR is working to close it through education, technology, and choice architecture.

Table of Contents

1. Part I: The Billion-Dollar Banker

   A look at Jeff Hulett’s career optimizing decision systems inside major banks.

2. Part II: The Fox and the Hen House

   An honest reflection on the misalignment between bank incentives and customer outcomes.

3. Part III: The Decision Gap

   How the asymmetry of decision-making—not just information—hurts consumers.

4. Part IV: Turning the Table

   The founding of Personal Finance Reimagined to close the decision gap.

5. Part V: My Economic North Star

   How Hayekian, behavioral, and classical liberal thought guide PFR’s philosophy.

6. Part VI: The Road Ahead

   A vision for empowering decision-makers across all stages of life.

7. Final Confession

   A candid conclusion on shifting from profit optimization to decision empowerment.

About the author: Jeff Hulett leads Personal Finance Reimagined, a decision-making and financial education platform. He teaches personal finance at James Madison University and provides personal finance seminars. Check out his book -- Making Choices, Making Money: Your Guide to Making Confident Financial Decisions.

Jeff is a career banker, data scientist, behavioral economist, and choice architect. Jeff has held banking and consulting leadership roles at Wells Fargo, Citibank, KPMG, and IBM.

Part I: The Billion-Dollar Banker

I have spent most of my career in banking. I am also a behavioral economist. And—like the action hero character Liam Neeson played in Taken—I have a particular set of skills, not for hunting down villains, but for decoding how people think about money and shaping the financial systems that profit from it.

My toolkit blends math, finance, economics, and data science, with a strong minor in neurobiology and behavioral psychology. Think of me as a social science surgeon—someone trained to deeply understand how people think when making financial decisions, particularly around consumer banking products like mortgages, home equity lines, auto loans, and credit cards. Over time, those financial products expanded to investment and real estate.

I led analytic and credit decision teams at Citibank, Wells Fargo, Capital One, KPMG, and IBM. Our mandate was clear: use data and decision systems to maximize the profitability of the bank. And we delivered. In fact, we added billions—yes, with a capital “B”—in incremental revenue.

How? Through machine learning–enabled decision systems. These systems predicted human behavior with a high degree of operational precision—not perfect, but good enough to inform confident decisions in the messy world of social science. We understood the limits: even with thousands of data variables, we could not fully anticipate our customers' life changes or economic shocks. But the systems were robust, enabling timely adjustments, prudent capital reserves, and a business resilience framework that evolved as new information surfaced.

Our machine learning–enabled decision systems used:

• Manual learning: Human quants coded the model rules and logic.

• Supervised learning: Human-guided, AI-enabled systems that learned from labeled data.

• Unsupervised learning: The system detected patterns in data via various algorithm sets without explicit human instruction.

In the main, we used manual and supervised machine learning techniques. Unsupervised learning was primarily implemented in a support and research capacity.

Our models ranged from old-school linear regressions to advanced neural networks and genetic algorithms. But no matter the model, the goal was the same: optimize decisions that drive shareholder value.

This work was intellectually thrilling. Every day, I explored new frontiers in data, decision science, and applied economics. We were architects of a decision infrastructure shaping tens of millions of customer outcomes. And we were really good at it.

But at some point, I began to question the consequences.

Part II: The Fox and the Hen House

Banking is a stakeholder business. Shareholders, customers, employees, and regulators all demand management attention. But when business trade-offs had to be made, the customer's interest was not always primary.

Our systems were elegant, scalable, predictive, and efficient—all leading to good decisions and fast decision implementation. But our customers' decision process? Often the opposite.

Via focus groups and emerging behavioral economics research, we observed that the customer decision-making process generally lacked structure. They relied on heuristics—mental shortcuts that simplify complex decisions. For example:

• “My friend just got this loan, so it must be a smart move.”

• "Lower monthly payment = better loan."

• "If I can get approved, I should take it."

• “I’ve used this bank before, so I can trust this offer.”

Heuristics can be useful. But they can also slide into cognitive biases:

• Sunk cost fallacy: Continuing with a poor borrowing decision because time or money has already been invested.

• Overconfidence: Believing repayment will be easier than it is.

• Confirmation bias: Seeking information that justifies taking the loan while ignoring red flags.

• Present bias: Ignoring future costs for short-term gains.

Our teams operated at the intersection of customer psychology and financial performance. We often nudged consumers toward “yes”—even when that answer might not align with their long-term well-being. The classical liberal in me, influenced by thinkers like Adam Smith and Ayn Rand, held firm to the belief that individuals are rational actors. Since banks cannot fully understand a borrower’s motivations, the best approach is to trust that people, when free to choose, will act in pursuit of their own utility.

That belief gave me comfort.

But the behavioral economist in me—trained to spot the predictable inconsistencies in decision-making—knew better. Rationality is not a universal benchmark; it is shaped by individual context. While people possess more insight than any banker into their own preferences and needs, they still face incomplete knowledge—and often make decisions influenced by biases that distort how they perceive their own utility.

Customers were not always deciding. They were reacting. And often, they were reacting to incentives we designed.

Part III: The Decision Gap

The epiphany came slowly but firmly: the real asymmetry in banking was not access to capital. It was access to decision systems.

Banks treated decision-making like a high-performance engine—something to fine-tune, fuel with data, and constantly optimize. Many consumers, on the other hand, seemed to treat decision-making like a leaky faucet—an energy-draining nuisance best ignored until absolutely necessary. While banks relied on teams of data scientists and sophisticated models, consumers were left to navigate major financial choices using raw information tools shaped as much by heuristics and cognitive bias as by facts—Google searches, scattered advice, and credit scores they often misunderstood or misapplied.

Even with disclosures and educational material, the gap persisted—not because consumers lacked information, but because they lacked a structure for turning information into confident decisions. Over time—especially in the wake of the financial crisis—regulators responded by requiring more and more disclosures. But this was like feeding more inputs into a system that neither understood how to process them nor found them helpful. In many cases, the added information created more confusion than clarity.

The result was predictable:

1. The extraordinary volume of disclosures discouraged consumers from reading them, and

2. Even if they did read them, the disclosures were written by lawyers in a difficult-to-understand legalese.

Most people simply sign where the sticky notes tell them to, without understanding what they are agreeing to. Behavioral economists refer to this kind of friction and confusion as sludge.

(Check out our Top 10 Sludge Busters article.)

The proliferation of GenAI tools like ChatGPT made this even more obvious. Today, information is abundant. But decisions are no easier. In fact, the more information consumers receive, the more challenging it becomes to filter, prioritize, and act.

This realization created an urgent question: If banks cannot—due to incentives, regulation, and practicality—close this decision gap for consumers, then who will?

That question led me to found Personal Finance Reimagined (PFR).

Part IV: Turning the Table

PFR is my answer to the decision gap. It is not just a company—it is a mission. A collective of professionals, educators, and technologists dedicated to one goal: helping people become better decision-makers for a lifetime of wealth.

We are not trying to replace financial institutions. We are empowering individuals to interact with them on a level playing field. We believe better consumer decision-making will make for better financial services companies. More intelligent demand will make for better financial supply. Together, we will raise the financial game for us all.

Our core philosophy is simple:

• Information is necessary, but not sufficient.

• Decision-making is a learned skill.

• Behavioral science must guide financial education.

This led to three pillars:

1. Curriculum and Training: We offer high school and college classes, adult seminars, and custom workshops. We help entrepreneurs start and scale.  Our students and clients do not just learn how financial products or financial systems work. They learn how to decide using simulations and real-life choice architecture.

2. Books and Tools: My book Making Choices, Making Money distills three decades of experience into a clear, practical guide. It is not just a financial primer—it is a decision-making manual. Paired with our patented apps like Definitive Choice, users can simulate major life decisions—college, housing, insurance, investing—with guided prompts and weighted criteria.

3. Technology + GenAI: We do not shy away from tech. We embrace it—but with guardrails. GenAI helps filter options, but structured frameworks ensure users retain control. In our view, GenAI should augment human agency, not replace it.

Part V: My Economic North Star

PFR’s approach is deeply informed by my economic worldview.

I am a classical liberal at heart. I believe individuals thrive when free to choose. But I am also a behavioral realist: I know people do not always choose well—especially in complex, high-stakes domains like personal finance.

That is where Hayek’s wisdom becomes crucial. He recognized that information is decentralized, tacit, and constantly evolving—and that no central planner, however sophisticated, can fully grasp it. Today’s AI helps us process the data we do have, but it cannot uncover the data we lack or predict the uncertainty that lies ahead. In a world moving faster than ever, where key variables often remain unseen, effective decision-making becomes not just useful, but essential. Hayek’s insight applies just as much at the individual level: because information is fragmented and rationality varies across people and contexts, each of us must become our own planner—curating what we know and acting wisely amid what we do not.

Behavioral economics sharpens this further. Kahneman, Tversky, and Thaler showed that our brains—while remarkable—are riddled with evolution-enabled cognitive shortcuts and traps. Our speed of evolutionary change ALWAYS lags the speed of cultural change. Our natural decision-making abilities are perfectly suited for the world—as it was about 2,000 years ago! This does not mean we are doomed. It means we need decision tools that close that gap, by leveraging the best parts of our brain and scaffolding us past our brain’s challenges.

Our choice architecture tools do just that. They:

• Increase decision accuracy by separating useful signals from emotional bias

• Prioritize long-term utility over short-term gratification

• Help people simulate trade-offs before consequences occur.

In that sense, our work sits at the intersection of Hayekian humility and behavioral pragmatism.

Part VI: The Road Ahead

What began as a career in bank profit optimization has evolved into a mission of decision empowerment. I still believe in markets. I still believe in freedom. But freedom only delivers flourishing when people are equipped to use it wisely.

Today, at PFR, we partner with entrepreneurs, schools, universities, nonprofits, and families. We support high school students preparing for college. First-generation college-goers navigating debt. Young professionals weighing housing and job choices. Parents planning for retirement. Entrepreneurs launching their dreams.

What unites them is not their income or background. It is their need for a repeatable, trustworthy decision process.

That is what we offer.

Final Confession

This article has been both a personal reflection and a professional reckoning. You have read how decades spent optimizing decision systems for some of the world’s largest banks ultimately led me to confront a growing asymmetry: institutions had the tools to make smart decisions—most individuals did not. That imbalance became my turning point.

While I still support organizations in building smarter systems—especially for mission-driven entrepreneurs—my primary mission today is empowering individuals to become confident, capable decision-makers. Through Personal Finance Reimagined, we equip people with the frameworks, tools, and behavioral insights to navigate complexity on their own terms.

In doing so, I remain grounded in my classical liberal roots—but now with more realism, humility, and behavioral precision. Because freedom is not just the ability to choose. It is the ability to choose well.

Resources for the Curious

For readers who want to explore the decision systems, economic frameworks, and behavioral insights behind this article, the following resources offer helpful depth and context.

Decision Systems and Machine Learning in Banking

• James, Gareth, et al. An Introduction to Statistical Learning. Springer, 2021.

  Provides clear explanations of supervised learning methods like regression, classification trees, and ensemble models commonly used in credit modeling.
  

• Khandani, Amir E., Adlar J. Kim, and Andrew W. Lo. “Consumer Credit-Risk Models via Machine-Learning Algorithms.” Journal of Banking & Finance, vol. 34, no. 11, 2010, pp. 2767–2787.

  Explores the use of neural networks and ensemble techniques for consumer credit decisions.
  

• Nguyen, Quang H., and Hien T. Nguyen. “Machine Learning in Credit Risk Modeling: A Review.” Journal of Risk and Financial Management, vol. 15, no. 2, 2022.

  Explains the shift from manual and supervised to more advanced and opaque unsupervised learning in finance.

Behavioral Economics and Decision-Making

• Kahneman, Daniel. Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011.

  A seminal work on heuristics, biases, and the dual-system brain.
  

• Thaler, Richard H., and Cass R. Sunstein. Nudge: Improving Decisions About Health, Wealth, and Happiness. Penguin Books, 2009.

  Introduces the idea of choice architecture and its impact on consumer decision-making.
  

• Tversky, Amos, and Daniel Kahneman. “Judgment under Uncertainty: Heuristics and Biases.” Science, vol. 185, no. 4157, 1974, pp. 1124–1131.

  The foundational academic paper explaining cognitive shortcuts and their predictable errors.
  

• Sunstein, Cass R. “Sludge Audits.” Behavioral Public Policy, vol. 3, no. 2, 2019, pp. 177–191.

  Highlights how excessive friction in consumer-facing processes—like confusing disclosures, unnecessary steps, or delayed approvals—can harm decision quality in financial services and reduce transparency.
  

Classical Liberalism and Hayekian Thought

• Hayek, F.A. The Constitution of Liberty. University of Chicago Press, 1960.

  Hayek’s comprehensive articulation of individual liberty, spontaneous order, and decentralized knowledge.
  

• Hayek, F.A. “The Use of Knowledge in Society.” American Economic Review, vol. 35, no. 4, 1945, pp. 519–530.

  Explains the distributed nature of information and why markets outperform central planning.
  

• Sowell, Thomas. Knowledge and Decisions. Basic Books, 1980.

  Bridges Hayekian and behavioral insights, emphasizing institutional incentives and decision-making complexity.
  

Works by Jeff Hulett

• Hulett, Jeff. Making Choices, Making Money: Your Guide to Making Confident Financial Decisions. Personal Finance Reimagined, 2023.

  Applies behavioral economics to real-world financial choices, from banking to investing to career planning.
  

• Hulett, Jeff. “Behavioral Economics: From Neuron to Market Price.” The Curiosity Vine, July 2025.

  Connects neuroscience, heuristics, and market pricing through a multi-layered decision-making framework.
  

• Hulett, Jeff. “The Essential Guide to Partnering with GenAI: Achieve Both Accuracy and Precision.” The Curiosity Vine, January 2025.

  Demonstrates how AI and behavioral design can work together in financial decision support.
  

• Hulett, Jeff. “Seeking What Not to Seek: How to Align Achievement and Happiness.” The Curiosity Vine, December 2024.

  Explores the evolutionary roots of human desire and the importance of structured restraint in pursuit of fulfillment.