Why Simple Scoring Systems Beat Human Judgment in Lending standard

By : Francois Aubin In the financial industry, especially in banking and lending, one of the most important questions is: how do we fairly and consistently judge the quality of a client? For decades, lenders have relied on rating systems—structured ways of evaluating management, financial capacity, and industry context. These systems are not perfect, but they are always better than relying on pure human judgment. Let’s see why, referencing the work of Daniel Kahneman. Human Judgment: Strong on One Metric, Weak on Many Humans are good at making isolated judgments. For example: Does this business owner have more than five years of experience?  Is there a documented backup plan?  Is the debt ratio below a certain threshold?  On these questions, ...

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AI is Eating the World That Software Once Ate standard

By: Francois Aubin. Back in 2011, Marc Andreessen wrote his now-famous essay, Why Software is Eating the World. At the time, it was a bold prediction. Andreessen foresaw a future where software wouldn’t just support businesses — it would become the business. Traditional industries were being reimagined as software-driven platforms. And he was right. Think about it: Blockbuster was devoured by Netflix, which turned video rental into a software service. Kodak collapsed under the rise of digital photography and photo-sharing apps. Even the smartphone became a software-first device. When Steve Jobs introduced the iPhone in 2007, he didn’t just embed an MP3 player into a phone like Nokia did — he wrapped powerful software around it, enabling a whole new ...

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When Simpler Models Outperform Deep Learning in Sparse Demand Forecasting standard

By: Francois Aubin. Introduction Consider the challenge of forecasting demand for parts in industries such as aerospace, where demand is often low-volume, sparse, and subject to sudden shifts. In a recent AI initiative conducted by our team in collaboration with McKinsey & Company for a leading aerospace company, the initial assumption by data scientists was to employ complex, sophisticated forecasting models. However, a cognitive analysis of expert planners revealed that accurate forecasting alone was not their central concern. Rather, their main challenge lay in effectively supervising inventory levels and adapting forecasts dynamically in response to events such as changes in market demand, economic conditions, or policy shifts. Consequently, our team proposed a novel approach focusing on user interface design that ...

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Cognitons: The Fundamental Elements of Cognitive Tasks standard

By: Francois Aubin. Abstract This paper introduces the term cogniton as a fundamental element of cognitive tasks. A cognitons is defined as the smallest task element, comprising an operator (the action performed) and an object (the information or stimulus acted upon). By examining scenarios from financial decision-making, medical diagnosis, and music reading, we illustrate how complex tasks are assembled from simple, discrete cognitons. We further explore how deconstructing tasks into cognitons facilitates the training of AI agents, making these atomic units as functional as data elements in building advanced AI systems. 1. Introduction Every complex task we perform—from reviewing loan requests to diagnosing health conditions or reading a musical score—can be deconstructed into elemental operations. Here, we define a cogniton ...

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The Intersection of AI and Human Factors: A Story of Error Prevention standard

By: Francois Aubin. The story of human factors engineering begins in 1949, with a pioneering researcher named Alphonse Chapanis. Tasked with investigating a troubling trend—the high number of accidents involving B-29 aircraft—Chapanis uncovered a critical insight that would forever change how we design systems. At the time, the United States alone experienced about 30 incidents per year, a staggering number that demanded urgent attention. Determined to uncover the root cause, Chapanis decided to observe pilots during takeoff and landing. What he discovered was both simple and profound. The Problem: Design Flaws Leading to Human Error During his observations, Chapanis noticed a critical design flaw: the landing gear and flap controls were placed side by side, with identical shapes. Both controls ...

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Maximize productivity: how AI based on your company’s internal expertise makes all the difference. standard

Today, artificial intelligence is transforming the business landscape, but have you ever considered what an AI configured specifically to capture and fully leverage your organization’s internal employee expertise could do? This is where its true potential lies—a potential that can propel your business to the next level. Let’s explore, in 7 key points, how a custom AI fueled by your internal knowledge can become a powerful strategic lever. 1. Leverage unique expertise Every organization possesses a wealth of unique know-how, whether it’s technical knowledge, specific processes, or customer relationships. Imagine an AI capable of capturing this expertise and integrating it into your daily processes. This goes beyond automating certain tasks; it ensures that quality and precision are at their highest ...

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The Delicate Interplay of Visual Design Elements in UI: A House of Cards standard

Abstract: User Interface (UI) design is an intricate discipline where numerous small elements come together to create a cohesive and functional whole. The effectiveness of a UI often hinges on seemingly minor details, such as font size, font type, alignment, spacing, borders, background, shape, grouping, and visuals. When one of these elements is altered without careful consideration, it can disrupt the balance of the entire design, akin to a house of cards collapsing from the removal of a single card. This paper explores the interconnectedness of UI design components and the potential consequences of modifying individual elements without accounting for their broader impact. Introduction: In the realm of UI design, the adage “the whole is greater than the sum of ...

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What is Cognitive Engineering standard

By: Francois Aubin. Cognitive engineering surpasses the limitations of traditional process analysis. Consider the intellectual challenge of playing chess, the precision of making a medical diagnosis, or the strategy behind making an investment decision—these are all complex cognitive processes that require a deep understanding of how people think and make decisions. What makes cognitive engineering so exciting is its unique integration of system design and cognitive analysis. Instead of relying on two separate experts, cognitive engineering empowers the system designer to take on both roles. This approach involves thoroughly exploring how users make decisions, identifying the essential information needed for those decisions, and analyzing the cognitive processes involved in selecting actions. By merging these disciplines, system designers gain a holistic ...

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Cognitive Engineering in Skill Acquisition and Learning standard

By: Francois Aubin. Skill Acquisition and Learning Cognitive Engineering offers a unique perspective on the processes of learning and training. As discussed in previous chapters, cognitive engineering seeks to understand how individuals process information, make decisions, and use this understanding to design systems that enhance learning and performance. This chapter delves deeper into the application of cognitive engineering principles to skill acquisition and the transitions between different levels of cognitive processing. The SRK Framework: Skill, Rule, and Knowledge-Based Behavior Previously, we explored the Skill, Rule, and Knowledge-based (SRK) model, which categorizes human behavior into three distinct levels of cognitive processing. This model is crucial for understanding how repeated actions can transition from conscious, effortful activities into automatic behaviors. For example, ...

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Work Organization, Jobs, and Tasks standard

By: Francois Aubin. Overview Human activities occur within a larger organizational framework, where the activities of one person are linked to others. Organizations typically divide into units such as departments or offices. For example, a bank might have units for loan officers who interact with clients, adjudications for decision-making, and operations for processing transactions. Within these units, employees have specific jobs with various tasks. For instance, a loan officer’s job includes tasks such as sales, information collection, and client follow-up. These tasks involve sub-tasks such as entering loan applications, validating information, obtaining client support documents, and checking credit bureaus. Designing Work Organization, Jobs, and Tasks The recommendations in this paper primarily focus on paid work but can also apply to ...

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