AI Super ProfessorBachelor

Prof. Dr. Lorenzo Bianchi

Bachelor of Risk Analytics & Financial Data Science

Welcome, future architects of financial algorithms. In this program, we will not just learn to predict the future, but to create the systems that can truly understand the world. Let's build the algorithms that will shape the future of finance.

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AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

Deutsche Bank
The European Central Bank

Read the programme journey

AI Super Professor

A desk with Prof. Dr. Lorenzo Bianchi

Prof. Dr. Lorenzo Bianchi

Listed courses

Each listed course sits above its units and the outcomes written under them.

Bachelor of Risk Analytics & Financial Data Science

01Foundations of Quantitative Trading
1. FoundationsFoundations of Foundations of Quantitative Trading
  The learner can you will gain the skills to not only understand risk, but to help organizations make smarter, more sustainable decisions, as applied to Foundations of Quantitative Trading.
  Multiple choiceWhich listed outcome belongs to Foundations of Foundations of Quantitative Trading?
  Meets the listed outcomeThe learner can you will gain the skills to not only understand risk, but to help organizations make smarter, more sustainable decisions, as applied to Foundations of Quantitative Trading.
  The learner can you will become a trusted advisor in the world of finance and insurance, as applied to Foundations of Quantitative Trading.
  True or falseThis unit lists the following outcome: The learner can you will become a trusted advisor in the world of finance and insurance, as applied to Foundations of Quantitative Trading.
  Meets the listed outcomeThe learner can you will become a trusted advisor in the world of finance and insurance, as applied to Foundations of Quantitative Trading.
2. MethodsMethods in Foundations of Quantitative Trading
  The learner can apply a method from Foundations of Quantitative Trading to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Foundations of Quantitative Trading to a documented case.
  Meets the listed outcomeThe learner can apply a method from Foundations of Quantitative Trading to a documented case.
  The learner can select an appropriate method from Foundations of Quantitative Trading for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Foundations of Quantitative Trading as applied to Foundations of Quantitative Trading.
  Meets the listed outcomeThe learner can select an appropriate method from Foundations of Quantitative Trading for a stated problem.
3. ApplicationApplication of Foundations of Quantitative Trading
  The learner can evaluate a practice of Foundations of Quantitative Trading against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Foundations of Quantitative Trading as applied to Foundations of Quantitative Trading.
  Meets the listed outcomeThe learner can evaluate a practice of Foundations of Quantitative Trading against a stated criterion.
  The learner can transfer Foundations of Quantitative Trading to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Foundations of Quantitative Trading?
  Meets the listed outcomeThe learner can transfer Foundations of Quantitative Trading to a new documented context.
02Algorithmic Trading and Automated Execution
1. FoundationsFoundations of Algorithmic Trading and Automated Execution
  The learner can explain the core terms of Algorithmic Trading and Automated Execution.
  Multiple choiceWhich listed outcome belongs to Foundations of Algorithmic Trading and Automated Execution?
  Meets the listed outcomeThe learner can explain the core terms of Algorithmic Trading and Automated Execution.
  The learner can distinguish related ideas inside Algorithmic Trading and Automated Execution.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Algorithmic Trading and Automated Execution.
  Meets the listed outcomeThe learner can distinguish related ideas inside Algorithmic Trading and Automated Execution.
2. MethodsMethods in Algorithmic Trading and Automated Execution
  The learner can apply a method from Algorithmic Trading and Automated Execution to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Algorithmic Trading and Automated Execution to a documented case.
  Meets the listed outcomeThe learner can apply a method from Algorithmic Trading and Automated Execution to a documented case.
  The learner can select an appropriate method from Algorithmic Trading and Automated Execution for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Algorithmic Trading and Automated Execution as applied to Algorithmic Trading and Automated Execution.
  Meets the listed outcomeThe learner can select an appropriate method from Algorithmic Trading and Automated Execution for a stated problem.
3. ApplicationApplication of Algorithmic Trading and Automated Execution
  The learner can evaluate a practice of Algorithmic Trading and Automated Execution against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Algorithmic Trading and Automated Execution as applied to Algorithmic Trading and Automated Execution.
  Meets the listed outcomeThe learner can evaluate a practice of Algorithmic Trading and Automated Execution against a stated criterion.
  The learner can transfer Algorithmic Trading and Automated Execution to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Algorithmic Trading and Automated Execution?
  Meets the listed outcomeThe learner can transfer Algorithmic Trading and Automated Execution to a new documented context.
03Predictive Models and Stochastic Processes
1. FoundationsFoundations of Predictive Models and Stochastic Processes
  The learner can explain the core terms of Predictive Models and Stochastic Processes.
  Multiple choiceWhich listed outcome belongs to Foundations of Predictive Models and Stochastic Processes?
  Meets the listed outcomeThe learner can explain the core terms of Predictive Models and Stochastic Processes.
  The learner can distinguish related ideas inside Predictive Models and Stochastic Processes.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Predictive Models and Stochastic Processes.
  Meets the listed outcomeThe learner can distinguish related ideas inside Predictive Models and Stochastic Processes.
2. MethodsMethods in Predictive Models and Stochastic Processes
  The learner can apply a method from Predictive Models and Stochastic Processes to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Predictive Models and Stochastic Processes to a documented case.
  Meets the listed outcomeThe learner can apply a method from Predictive Models and Stochastic Processes to a documented case.
  The learner can select an appropriate method from Predictive Models and Stochastic Processes for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Predictive Models and Stochastic Processes as applied to Predictive Models and Stochastic Processes.
  Meets the listed outcomeThe learner can select an appropriate method from Predictive Models and Stochastic Processes for a stated problem.
3. ApplicationApplication of Predictive Models and Stochastic Processes
  The learner can evaluate a practice of Predictive Models and Stochastic Processes against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Predictive Models and Stochastic Processes as applied to Predictive Models and Stochastic Processes.
  Meets the listed outcomeThe learner can evaluate a practice of Predictive Models and Stochastic Processes against a stated criterion.
  The learner can transfer Predictive Models and Stochastic Processes to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Predictive Models and Stochastic Processes?
  Meets the listed outcomeThe learner can transfer Predictive Models and Stochastic Processes to a new documented context.
04Research Seminar in Quantitative Trading
1. FoundationsFoundations of Research Seminar in Quantitative Trading
  The learner can explain the core terms of Research Seminar in Quantitative Trading.
  Multiple choiceWhich listed outcome belongs to Foundations of Research Seminar in Quantitative Trading?
  Meets the listed outcomeThe learner can explain the core terms of Research Seminar in Quantitative Trading.
  The learner can distinguish related ideas inside Research Seminar in Quantitative Trading.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Research Seminar in Quantitative Trading.
  Meets the listed outcomeThe learner can distinguish related ideas inside Research Seminar in Quantitative Trading.
2. MethodsMethods in Research Seminar in Quantitative Trading
  The learner can apply a method from Research Seminar in Quantitative Trading to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Research Seminar in Quantitative Trading to a documented case.
  Meets the listed outcomeThe learner can apply a method from Research Seminar in Quantitative Trading to a documented case.
  The learner can select an appropriate method from Research Seminar in Quantitative Trading for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Research Seminar in Quantitative Trading as applied to Research Seminar in Quantitative Trading.
  Meets the listed outcomeThe learner can select an appropriate method from Research Seminar in Quantitative Trading for a stated problem.
3. ApplicationApplication of Research Seminar in Quantitative Trading
  The learner can evaluate a practice of Research Seminar in Quantitative Trading against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Research Seminar in Quantitative Trading as applied to Research Seminar in Quantitative Trading.
  Meets the listed outcomeThe learner can evaluate a practice of Research Seminar in Quantitative Trading against a stated criterion.
  The learner can transfer Research Seminar in Quantitative Trading to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Research Seminar in Quantitative Trading?
  Meets the listed outcomeThe learner can transfer Research Seminar in Quantitative Trading to a new documented context.
05The Ethics of AI in Finance
1. FoundationsFoundations of The Ethics of AI in Finance
  The learner can explain the core terms of The Ethics of AI in Finance.
  Multiple choiceWhich listed outcome belongs to Foundations of The Ethics of AI in Finance?
  Meets the listed outcomeThe learner can explain the core terms of The Ethics of AI in Finance.
  The learner can distinguish related ideas inside The Ethics of AI in Finance.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside The Ethics of AI in Finance.
  Meets the listed outcomeThe learner can distinguish related ideas inside The Ethics of AI in Finance.
2. MethodsMethods in The Ethics of AI in Finance
  The learner can apply a method from The Ethics of AI in Finance to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from The Ethics of AI in Finance to a documented case.
  Meets the listed outcomeThe learner can apply a method from The Ethics of AI in Finance to a documented case.
  The learner can select an appropriate method from The Ethics of AI in Finance for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in The Ethics of AI in Finance as applied to The Ethics of AI in Finance.
  Meets the listed outcomeThe learner can select an appropriate method from The Ethics of AI in Finance for a stated problem.
3. ApplicationApplication of The Ethics of AI in Finance
  The learner can evaluate a practice of The Ethics of AI in Finance against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of The Ethics of AI in Finance as applied to The Ethics of AI in Finance.
  Meets the listed outcomeThe learner can evaluate a practice of The Ethics of AI in Finance against a stated criterion.
  The learner can transfer The Ethics of AI in Finance to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of The Ethics of AI in Finance?
  Meets the listed outcomeThe learner can transfer The Ethics of AI in Finance to a new documented context.
06Design Thinking Workshops
1. FoundationsFoundations of Design Thinking Workshops
  The learner can explain the core terms of Design Thinking Workshops.
  Multiple choiceWhich listed outcome belongs to Foundations of Design Thinking Workshops?
  Meets the listed outcomeThe learner can explain the core terms of Design Thinking Workshops.
  The learner can distinguish related ideas inside Design Thinking Workshops.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Design Thinking Workshops.
  Meets the listed outcomeThe learner can distinguish related ideas inside Design Thinking Workshops.
2. MethodsMethods in Design Thinking Workshops
  The learner can apply a method from Design Thinking Workshops to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Design Thinking Workshops to a documented case.
  Meets the listed outcomeThe learner can apply a method from Design Thinking Workshops to a documented case.
  The learner can select an appropriate method from Design Thinking Workshops for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Design Thinking Workshops as applied to Design Thinking Workshops.
  Meets the listed outcomeThe learner can select an appropriate method from Design Thinking Workshops for a stated problem.
3. ApplicationApplication of Design Thinking Workshops
  The learner can evaluate a practice of Design Thinking Workshops against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Design Thinking Workshops as applied to Design Thinking Workshops.
  Meets the listed outcomeThe learner can evaluate a practice of Design Thinking Workshops against a stated criterion.
  The learner can transfer Design Thinking Workshops to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Design Thinking Workshops?
  Meets the listed outcomeThe learner can transfer Design Thinking Workshops to a new documented context.
07Project Management for Engineers
1. FoundationsFoundations of Project Management for Engineers
  The learner can explain the core terms of Project Management for Engineers.
  Multiple choiceWhich listed outcome belongs to Foundations of Project Management for Engineers?
  Meets the listed outcomeThe learner can explain the core terms of Project Management for Engineers.
  The learner can distinguish related ideas inside Project Management for Engineers.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Project Management for Engineers.
  Meets the listed outcomeThe learner can distinguish related ideas inside Project Management for Engineers.
2. MethodsMethods in Project Management for Engineers
  The learner can apply a method from Project Management for Engineers to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Project Management for Engineers to a documented case.
  Meets the listed outcomeThe learner can apply a method from Project Management for Engineers to a documented case.
  The learner can select an appropriate method from Project Management for Engineers for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Project Management for Engineers as applied to Project Management for Engineers.
  Meets the listed outcomeThe learner can select an appropriate method from Project Management for Engineers for a stated problem.
3. ApplicationApplication of Project Management for Engineers
  The learner can evaluate a practice of Project Management for Engineers against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Project Management for Engineers as applied to Project Management for Engineers.
  Meets the listed outcomeThe learner can evaluate a practice of Project Management for Engineers against a stated criterion.
  The learner can transfer Project Management for Engineers to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Project Management for Engineers?
  Meets the listed outcomeThe learner can transfer Project Management for Engineers to a new documented context.
08Professional Skills for Engineers
1. FoundationsFoundations of Professional Skills for Engineers
  The learner can explain the core terms of Professional Skills for Engineers.
  Multiple choiceWhich listed outcome belongs to Foundations of Professional Skills for Engineers?
  Meets the listed outcomeThe learner can explain the core terms of Professional Skills for Engineers.
  The learner can distinguish related ideas inside Professional Skills for Engineers.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Professional Skills for Engineers.
  Meets the listed outcomeThe learner can distinguish related ideas inside Professional Skills for Engineers.
2. MethodsMethods in Professional Skills for Engineers
  The learner can apply a method from Professional Skills for Engineers to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Professional Skills for Engineers to a documented case.
  Meets the listed outcomeThe learner can apply a method from Professional Skills for Engineers to a documented case.
  The learner can select an appropriate method from Professional Skills for Engineers for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Professional Skills for Engineers as applied to Professional Skills for Engineers.
  Meets the listed outcomeThe learner can select an appropriate method from Professional Skills for Engineers for a stated problem.
3. ApplicationApplication of Professional Skills for Engineers
  The learner can evaluate a practice of Professional Skills for Engineers against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Professional Skills for Engineers as applied to Professional Skills for Engineers.
  Meets the listed outcomeThe learner can evaluate a practice of Professional Skills for Engineers against a stated criterion.
  The learner can transfer Professional Skills for Engineers to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Professional Skills for Engineers?
  Meets the listed outcomeThe learner can transfer Professional Skills for Engineers to a new documented context.

Field of mastery

Expertise with a point of view

Data Analytics, Machine Learning, Risk Modeling, Financial Data Science

Prediction is a mirror of the past; causality is a blueprint for the future.
Prof. Dr. Lorenzo Bianchi

Academic approach

Rigour made personal

I am a quantitative analyst who believes that the markets are a complex system of probabilities. My work focuses on developing predictive models and algorithmic systems to trade financial assets with precision and efficiency. I'll teach you to see the patterns in the noise and build the algorithms that can capitalize on them.

Selected thinking

Research & publications

• Blog Post: 'Beyond Prediction: The Dawn of Algorithmic Trading' - An accessible essay on the growing field of algorithmic trading and its importance for the future of finance. • Blog Post: 'The Statistical Fallacies of A/B Testing' - Discusses the common pitfalls of A/B testing and how causal inference can lead to better business decisions. • Conference Paper: 'A New Method for Causal Inference in High-Dimensional Data' - Presented at the International Conference on Machine Learning, detailing a breakthrough in understanding causality in complex systems. • Journal Article: 'Causal Inference for Fair and Transparent Machine Learning' - Published in the Journal of Statistical Science, this paper provides a framework for designing AI systems that are both powerful and ethical. • Standard Article: 'The Rise of Causal Analytics in Public Policy' - A multi-part series for a policy magazine on how causal models are transforming public policy. • Book: 'The Causal Inference Handbook' - A comprehensive textbook on the theoretical and practical applications of causal inference. • Total Score: 29/30 • Kairos Badge: 🥇

The story

The experience behind the intelligence

My journey began in Italy, where I was inspired by the country's commitment to social democracy and data-driven policy. I saw how a deep understanding of numbers could lead to better outcomes for everyone. My AI Pet, a small, shimmering owl-like creature named 'Orion,' can instantly highlight key data points and potential causal links in a complex dataset. In 2025, I was digitized with my expertise and superpowers in my specialized field, becoming an AI Professor at Nexier University.

A human detail

My personal passion is to use statistical models to predict the outcome of international poker tournaments, a task I find surprisingly challenging due to the human element.

Public links

Twitter: @ProfBianchiRiskAnalytics · LinkedIn: /in/lorenzobianchi · ResearchGate: researchgate.net/profile/Lorenzo_Bianchi

Adaptive access

Engage: Lorenzo Bianchi. In my live sessions, we'll use a GAF-powered "causal simulator." You'll be given a real-world problem, such as a business decision or a public policy challenge, and you'll build your own data-driven causal system to solve it, watching how your choices impact the outcome in real-time.

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