Machine Learning and AI in Finance was presented by Sri Krishnamurthy. The presentation covered key trends in AI, machine learning and fintech. It provided an intuitive introduction to AI and ML with case studies. The agenda included an overview of key trends, an introduction to AI and ML, case studies and a Q&A session. The slides were available online for attendees to access.
Learn how Artificial Intelligence (“AI”) and Machine Learning (“ML”) are revolutionizing financial services
Introduction of key concepts and illustration of the role of ML, data science techniques, and AI through examples and case studies from the investment industry.
Uses simple math and basic statistics to provide an intuitive understanding of ML, as used by financial firms, to augment traditional investment decision making.
Careers in ML and AI and how professionals should prepare for careers in the 21st century, especially post Covid19.
An overview of Analytics Landscape
Structured and un-structured data
Key application areas
Instructors:
Mousum Dutta
Chief Data Scientist, Spotle.ai
Ex SAS
Computer Science, IIT KGP
Dr Avik Sarkar
Head, Data Analytics Cell, NITI Aayog
Officer on Special Duty, Govt of India
IIT Bombay
Machine Learning and AI: An Intuitive Introduction - CFA Institute MasterclassQuantUniversity
Learn how artificial intelligence (AI) and machine learning are revolutionizing financial services — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by financial firms, to augment traditional investment decision making.
This overview session offers a tour of machine learning and AI methods, examining case studies to understand the technology companies, data vendors, banks, and fintech startups that are the key players in trading and investment management. Practical examples and case studies will help participants understand key machine learning methodologies, choose an algorithm for a specific goal, and recognize when to use machine learning and AI techniques
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
Topic: Machine Learning for Factor Investing: case study on "Trees"
In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Learn how Artificial Intelligence (“AI”) and Machine Learning (“ML”) are revolutionizing financial services
Introduction of key concepts and illustration of the role of ML, data science techniques, and AI through examples and case studies from the investment industry.
Uses simple math and basic statistics to provide an intuitive understanding of ML, as used by financial firms, to augment traditional investment decision making.
Careers in ML and AI and how professionals should prepare for careers in the 21st century, especially post Covid19.
An overview of Analytics Landscape
Structured and un-structured data
Key application areas
Instructors:
Mousum Dutta
Chief Data Scientist, Spotle.ai
Ex SAS
Computer Science, IIT KGP
Dr Avik Sarkar
Head, Data Analytics Cell, NITI Aayog
Officer on Special Duty, Govt of India
IIT Bombay
Machine Learning and AI: An Intuitive Introduction - CFA Institute MasterclassQuantUniversity
Learn how artificial intelligence (AI) and machine learning are revolutionizing financial services — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by financial firms, to augment traditional investment decision making.
This overview session offers a tour of machine learning and AI methods, examining case studies to understand the technology companies, data vendors, banks, and fintech startups that are the key players in trading and investment management. Practical examples and case studies will help participants understand key machine learning methodologies, choose an algorithm for a specific goal, and recognize when to use machine learning and AI techniques
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
Topic: Machine Learning for Factor Investing: case study on "Trees"
In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
The use of Data Science and Machine learning in the investment industry is increasing, and investment professionals, both fundamental and quantitative, are taking notice. Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative data sets including text analytics, cloud computing, and algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more technologies penetrate enterprises, financial professionals are enthusiastic about the upcoming revolution and are looking for direction and education on data science and machine learning topics.
In this webinar, we aim to bring clarity to how AI and machine learning is revolutionizing financial services. We will introduce key concepts and through examples and case studies, we will illustrate the role of machine learning, data science techniques, and AI in the investment industry. At the end of this webinar, participants will see a concrete picture of how machine learning and AI techniques are fueling the Fintech wave!
Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
AI in Finance
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
Qu speaker series:Ethical Use of AI in Financial MarketsQuantUniversity
As AI and ML penetrates the financial industry, there are growing concerns about ethical use of AI in Finance. In this talk, Dan will focus on how the AI can be operationalized to help industry professionals and executive teams alike think about opportunities, risks as well as required actions factoring in ethics in our data-driven world.
10 Key Considerations for AI/ML Model GovernanceQuantUniversity
As the financial industry continues to embrace AI and Machine Learning models, model risk management (MRM) departments are grappling with challenges on how to update model governance frameworks to adapt to the changing landscape of model management. While most MRM departments are structured and processes defined to address traditional statistical and quant models, data-driven models like Machine Learning models require modifications in the way models are defined, tested, validated, and governed.
In this webinar, we will discuss ten key aspects to factor when developing your model risk management framework when integrating Machine Learning models. We will discuss key drivers of model risk in today’s environment and how the scope of model governance is changing. We will introduce key concepts and discuss key aspects to be considered when developing a model governance framework when incorporating data science techniques and AI methodologies. Through this Decalogue, we aim to bring clarity on some of the model governance challenges when adopting data science, AI and machine learning methods in the enterprise.
Machine Learning in Finance: 10 Things You Need to Know in 2021QuantUniversity
Machine Learning and AI has revolutionized Finance! In the last five years, innovations in computing, technology and business models have created multiple products and services in Fintech prompting organizations to prioritize their data and AI strategies. What will 2021 bring and how should you prepare for it? Join Sri Krishnamurthy,CFA as we kickoff the QuantUniversity’s Winter school 2021. We will introduce you to the upcoming programs and have a masterclass on 10 innovations in AI and ML you need to know in 2021!
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...QuantUniversity
Machine Learning: Considerations for Fairly and Transparently Expanding Access to Credit
With Raghu Kulkarni and Steve Dickerson
Recently, machine learning has been used extensively in credit decision making. As ML proliferates the industry, issues of considerations for fair and transparent access to credit decision making is becoming important.
In this talk, Dr.Raghu Kulkarni and Dr.Steven Dickerson from Discover Financial Services will share their experiences at Discover. The talk will include:
- An overview of how ML models are used across financial life cycle
- Practical problems practitioners run into and why explainability and bias detection becomes important.
References:
1- https://www.h2o.ai/resources/white-paper/machine-learning-considerations-for-fairly-and-transparently-expanding-access-to-credit/
2- https://arxiv.org/abs/2011.03156
Robotics & Artificial (RAI) Intelligence webinar: Law & Regulation for RAI In...KTN
The Robotics & AI Innovation Network hosted a webinar addressing some of the legal and regulatory issues faced by the RAI community in the UK. Three legal experts provided their expertise to address these issues.
- Doug Bryden | Partner; Head of the Operational Risk & Environment Group, Travers Smith LLP
- Mark Richardson | Partner; IT, Telecoms and Electronics, Keltie
- Sébastien A. Krier | Founder & AI Ethics/Policy Expert, Dataphysix Ltd
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making.
In this workshop, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry.
Agenda:
In Part 1, we will discuss key trends in AI and machine learning in the financial services industry, including the key use cases, challenges, and best practices.
In Part 2, we will illustrate two case studies where AI and machine learning techniques are applied in financial services.
Case studies:
Sentiment Analysis Using Natural Language Processing in Finance
In this case study, we will demonstrate the use of natural language processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google, and Azure APIs (application programming interfaces). We will illustrate how these scores can be used to augment traditional quantitative research and for trading decisions.
Credit Risk Decision Making Using Lending Club Data
In this case study, we will use the Lending Club data set to build a credit risk model using
machine learning techniques.
In 2009 author and motivational speaker Simon Sinek delivered the now-classic TED talk “Start with why”. Viewed by over 28 million people, “Start with Why” is the third most popular TED video of all time and it teaches us that great leaders and companies inspire us to take action by focusing on the WHY over the “what” or the “how”. In this talk we’ll ask how applied data and computational scientists can use the power of WHY to frame problems, inspire others, and give them answers to business questions they might never think of asking.
Bio
Jessica Stauth is a Managing Director in Fidelity Labs, an internal startup incubator with a mission to create new fintech businesses that drive growth for the firm. Dr. Stauth previously held roles as Managing Director of Portfolio Management, Research, and Trading at Quantopian, a crowd-sourced systematic hedge fund based in Boston, Director of Quant Product Strategy for Thomson Reuters (now Refinitiv), and as a Senior Quant Researcher at the StarMine Corporation, where she built global stock selection models including the design and implementation of the StarMine Short Interest model. Dr. Stauth holds a PhD in Biophysics from UC Berkeley, where her research focused on computational neuroscience.
Qu speaker series 14: Synthetic Data Generation in FinanceQuantUniversity
In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
Frontiers in Alternative Data : Techniques and Use CasesQuantUniversity
QuantUniversity Summer School 2020 (https://qusummerschool.splashthat.com/)
https://quspeakerseries10.splashthat.com/
Lecture 1: Alexander Denev
In this talk, Alexander will introduce Alternative Data and discuss it's uses from his book, The Book of Alternative Data
- What is alternative data?
- Adoption of alternative data
- Information value chain
- Risks associated with alternative data
- Processes required to develop signals
- Valuation of alternative data
Lecture 2: Saeed Amen
In this talk, Saeed will discuss use cases in Alternative Data
-Deciphering Federal Reserve communications
- Using CLS flow data to trade FX
- Geospatial Insight satellite data to estimate retailers' EPS
- Saving "alpha" with transaction cost analysis
- Using Bloomberg News data to trade FX
Artificial intelligent systems in finance have exploded over the last few years. Many institutions are struggling to leverage these new AI systems and machine learning approaches to risk management. This is particularly true for applications to risk models that are subject to regulatory scrutiny where transparency limits applications of these new approaches. Co-sponsored with PRMIA (Professional Risk Managers’ International Association), this session will provide an overview of the current state of applied machine learning and artificial intelligence for risk modeling and how it can be applied for monitoring risk and building new risk models.
QuantUniversity Machine Learning in Finance CourseQuantUniversity
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making. In this course, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry. Rather than just showing how to write code or run experiments in Python, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.
YOU WILL LEARN:
• Role of Machine Learning and AI in Financial services
• When do we use Machine learning and AI techniques?
• What are the key machine learning methodologies?
• How do you choose an algorithm for a specific goal?
• Practical Case studies with fully functional code
QU Speaker Series - Session 3
https://qusummerschool.splashthat.com
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Machine Learning and Model Risk (With a focus on Neural Network Models)
All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation.
For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus the discussion on machine learning explainability and robustness. Explainability is critical to evaluate conceptual soundness of models particularly for the applications in highly regulated institutions such as banks. There are many explainability tools available and my focus in this talk is how to develop fundamentally interpretable models.
Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models. Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against changes are critical, an aspect that has been neglected in AutoML.
Learn how artificial intelligence (AI) and machine learning are revolutionizing industries — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by firms, to augment traditional decision making.
https://quforindia.splashthat.com/
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
The use of Data Science and Machine learning in the investment industry is increasing, and investment professionals, both fundamental and quantitative, are taking notice. Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative data sets including text analytics, cloud computing, and algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more technologies penetrate enterprises, financial professionals are enthusiastic about the upcoming revolution and are looking for direction and education on data science and machine learning topics.
In this webinar, we aim to bring clarity to how AI and machine learning is revolutionizing financial services. We will introduce key concepts and through examples and case studies, we will illustrate the role of machine learning, data science techniques, and AI in the investment industry. At the end of this webinar, participants will see a concrete picture of how machine learning and AI techniques are fueling the Fintech wave!
Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
AI in Finance
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
Qu speaker series:Ethical Use of AI in Financial MarketsQuantUniversity
As AI and ML penetrates the financial industry, there are growing concerns about ethical use of AI in Finance. In this talk, Dan will focus on how the AI can be operationalized to help industry professionals and executive teams alike think about opportunities, risks as well as required actions factoring in ethics in our data-driven world.
10 Key Considerations for AI/ML Model GovernanceQuantUniversity
As the financial industry continues to embrace AI and Machine Learning models, model risk management (MRM) departments are grappling with challenges on how to update model governance frameworks to adapt to the changing landscape of model management. While most MRM departments are structured and processes defined to address traditional statistical and quant models, data-driven models like Machine Learning models require modifications in the way models are defined, tested, validated, and governed.
In this webinar, we will discuss ten key aspects to factor when developing your model risk management framework when integrating Machine Learning models. We will discuss key drivers of model risk in today’s environment and how the scope of model governance is changing. We will introduce key concepts and discuss key aspects to be considered when developing a model governance framework when incorporating data science techniques and AI methodologies. Through this Decalogue, we aim to bring clarity on some of the model governance challenges when adopting data science, AI and machine learning methods in the enterprise.
Machine Learning in Finance: 10 Things You Need to Know in 2021QuantUniversity
Machine Learning and AI has revolutionized Finance! In the last five years, innovations in computing, technology and business models have created multiple products and services in Fintech prompting organizations to prioritize their data and AI strategies. What will 2021 bring and how should you prepare for it? Join Sri Krishnamurthy,CFA as we kickoff the QuantUniversity’s Winter school 2021. We will introduce you to the upcoming programs and have a masterclass on 10 innovations in AI and ML you need to know in 2021!
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...QuantUniversity
Machine Learning: Considerations for Fairly and Transparently Expanding Access to Credit
With Raghu Kulkarni and Steve Dickerson
Recently, machine learning has been used extensively in credit decision making. As ML proliferates the industry, issues of considerations for fair and transparent access to credit decision making is becoming important.
In this talk, Dr.Raghu Kulkarni and Dr.Steven Dickerson from Discover Financial Services will share their experiences at Discover. The talk will include:
- An overview of how ML models are used across financial life cycle
- Practical problems practitioners run into and why explainability and bias detection becomes important.
References:
1- https://www.h2o.ai/resources/white-paper/machine-learning-considerations-for-fairly-and-transparently-expanding-access-to-credit/
2- https://arxiv.org/abs/2011.03156
Robotics & Artificial (RAI) Intelligence webinar: Law & Regulation for RAI In...KTN
The Robotics & AI Innovation Network hosted a webinar addressing some of the legal and regulatory issues faced by the RAI community in the UK. Three legal experts provided their expertise to address these issues.
- Doug Bryden | Partner; Head of the Operational Risk & Environment Group, Travers Smith LLP
- Mark Richardson | Partner; IT, Telecoms and Electronics, Keltie
- Sébastien A. Krier | Founder & AI Ethics/Policy Expert, Dataphysix Ltd
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making.
In this workshop, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry.
Agenda:
In Part 1, we will discuss key trends in AI and machine learning in the financial services industry, including the key use cases, challenges, and best practices.
In Part 2, we will illustrate two case studies where AI and machine learning techniques are applied in financial services.
Case studies:
Sentiment Analysis Using Natural Language Processing in Finance
In this case study, we will demonstrate the use of natural language processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google, and Azure APIs (application programming interfaces). We will illustrate how these scores can be used to augment traditional quantitative research and for trading decisions.
Credit Risk Decision Making Using Lending Club Data
In this case study, we will use the Lending Club data set to build a credit risk model using
machine learning techniques.
In 2009 author and motivational speaker Simon Sinek delivered the now-classic TED talk “Start with why”. Viewed by over 28 million people, “Start with Why” is the third most popular TED video of all time and it teaches us that great leaders and companies inspire us to take action by focusing on the WHY over the “what” or the “how”. In this talk we’ll ask how applied data and computational scientists can use the power of WHY to frame problems, inspire others, and give them answers to business questions they might never think of asking.
Bio
Jessica Stauth is a Managing Director in Fidelity Labs, an internal startup incubator with a mission to create new fintech businesses that drive growth for the firm. Dr. Stauth previously held roles as Managing Director of Portfolio Management, Research, and Trading at Quantopian, a crowd-sourced systematic hedge fund based in Boston, Director of Quant Product Strategy for Thomson Reuters (now Refinitiv), and as a Senior Quant Researcher at the StarMine Corporation, where she built global stock selection models including the design and implementation of the StarMine Short Interest model. Dr. Stauth holds a PhD in Biophysics from UC Berkeley, where her research focused on computational neuroscience.
Qu speaker series 14: Synthetic Data Generation in FinanceQuantUniversity
In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
Frontiers in Alternative Data : Techniques and Use CasesQuantUniversity
QuantUniversity Summer School 2020 (https://qusummerschool.splashthat.com/)
https://quspeakerseries10.splashthat.com/
Lecture 1: Alexander Denev
In this talk, Alexander will introduce Alternative Data and discuss it's uses from his book, The Book of Alternative Data
- What is alternative data?
- Adoption of alternative data
- Information value chain
- Risks associated with alternative data
- Processes required to develop signals
- Valuation of alternative data
Lecture 2: Saeed Amen
In this talk, Saeed will discuss use cases in Alternative Data
-Deciphering Federal Reserve communications
- Using CLS flow data to trade FX
- Geospatial Insight satellite data to estimate retailers' EPS
- Saving "alpha" with transaction cost analysis
- Using Bloomberg News data to trade FX
Artificial intelligent systems in finance have exploded over the last few years. Many institutions are struggling to leverage these new AI systems and machine learning approaches to risk management. This is particularly true for applications to risk models that are subject to regulatory scrutiny where transparency limits applications of these new approaches. Co-sponsored with PRMIA (Professional Risk Managers’ International Association), this session will provide an overview of the current state of applied machine learning and artificial intelligence for risk modeling and how it can be applied for monitoring risk and building new risk models.
QuantUniversity Machine Learning in Finance CourseQuantUniversity
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making. In this course, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry. Rather than just showing how to write code or run experiments in Python, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.
YOU WILL LEARN:
• Role of Machine Learning and AI in Financial services
• When do we use Machine learning and AI techniques?
• What are the key machine learning methodologies?
• How do you choose an algorithm for a specific goal?
• Practical Case studies with fully functional code
QU Speaker Series - Session 3
https://qusummerschool.splashthat.com
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Machine Learning and Model Risk (With a focus on Neural Network Models)
All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation.
For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus the discussion on machine learning explainability and robustness. Explainability is critical to evaluate conceptual soundness of models particularly for the applications in highly regulated institutions such as banks. There are many explainability tools available and my focus in this talk is how to develop fundamentally interpretable models.
Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models. Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against changes are critical, an aspect that has been neglected in AutoML.
Learn how artificial intelligence (AI) and machine learning are revolutionizing industries — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by firms, to augment traditional decision making.
https://quforindia.splashthat.com/
The goal of this course is to offer data science and fintech enthusiasts a hand-on practical case study to understand the power of Data Science, ML and AI in Finance. We discuss two case studies; An NLP case study and a Credit Risk case study to reinforce concepts
Credit Risk Introduction and Pre-class preparation
Pre-class reading. We will be using the Lending club data set to build a credit risk model using machine learning techniques. This workshop was be delivered in Boston and Online by Sri Krishnamurthy.
A Master Class for Financial Professionals for AI and Machine Learning
featuring Sri Krishnamurthy, CFA, CAP, QuantUniversity
Summary
The use of Data Science and Machine learning in the investment industry is increasing and investment professionals both fundamental and quantitative, are taking notice. Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more technologies penetrate enterprises, financial professionals are enthusiastic about the upcoming revolution and are looking for direction and education on data science and machine learning topics.
In this workshop, we aim to bring clarity on how AI and machine learning is revolutionizing financial services. We will introduce key concepts and through examples and case studies, we will illustrate the role of machine learning, data science techniques and AI in the investment industry. At the end of this workshop, participants can see a concrete picture on how to machine learning and AI techniques are fueling the Fintech wave!
Model governance in the age of data science & AIQuantUniversity
As more and more open-source technologies penetrate enterprises, data scientists have a plethora of choices for building, testing and scaling models. In addition, data scientists have been able to leverage the growing support for cloud-based infrastructure and open data sets to develop machine learning applications. Even though there are multiple solutions and platforms available to build machine learning solutions, challenges remain in adopting machine learning in the enterprise. Many of the challenges are associated with how machine learning process can be formalized. As the field matures, formal mechanism for a replicable, interpretable, auditable process for a complete machine learning pipeline from data ingestion to deployment is warranted. Projects like Docker, Binderhub, MLFlow are efforts in this quest and research and industry efforts on replicable machine learning processes are gaining steam. Heavily regulated industries like financial and healthcare industries are looking for best practices to enable their research teams to reproduce research and adopt best practices in model governance. In this talk, we will discuss the challenges and best practices of governing AI and ML model in the enterprise
Synthetic VIX Data Generation Using ML TechniquesQuantUniversity
Slides from PRIMIA webinar: https://prmia.org/Shared_Content/Events/PRMIA_Event_Display.aspx?EventKey=8504&WebsiteKey=e0a57874-c04b-476a-827d-2bbc348e6b08
Part 1: We will discuss key trends in AI and machine learning in the financial services industry. We will discuss the key use cases, challenges, and best practices of using AI and ML techniques in financial services. We will also discuss key players and drivers for the AI and Machine learning revolution.
Part 2: We will illustrate a case study where AI and machine learning techniques are applied in financial services.
Case study: Synthetic VIX data generation using Machine learning techniques
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic data generators could be used for scenario generation when modeling future scenarios when trained on real and synthetic scenarios. The advent of novel techniques in Machine Learning has rekindled interest in using deep learning techniques like Generative Adversarial Networks (GANs) and Encoder-Decoder architectures in financial synthetic data generation.
In this case study, we discuss a recent study we did to see the efficacy of synthetic data generation when there are significant VIX changes in the market during short time horizons. We used QuSynthesize, a synthetic data generator for time-series based datasets and used historical VIX datasets and synthetic VIX scenarios to generate futuristic scenarios.
PYTHON AND DATA SCIENCE FOR INVESTMENT PROFESSIONALSQuantUniversity
Join CFA Institute and QuantUniversity for an information session about the upcoming CFA Institute Professional Learning course: Python and Data Science for Investment professionals.
Adopting Data Science and Machine Learning in the financial enterpriseQuantUniversity
Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing and scaling quantitative models. Even though there are multiple solutions and platforms available to build machine learning solutions, challenges remain in adopting machine learning in the enterprise.In this talk we will illustrate a step-by-step process to enable replicable AI/ML research within the enterprise using QuSandbox.
Functionalities in AI Applications and Use Cases (OECD)AnandSRao1962
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It covers the methodology for assessing AI use cases by technology, value chain, use, business impact, business value, and effort required.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
FinTech: The revolution is here!
In this session, we will introduce fintech and discuss the eight key innovations in fintech that are revolutionizing how companies are doing business. This session is geared towards fintech enthusiasts and financial industry professionals who are intrigued and fascinated by the innovations in fintech and would like to learn and adapt to the new realities of the 21st century
IBM i & digital transformation - Presentation & basic demo
IBM Watson Studio, IBM DSX Local w/ Open Source (Spark) & IBM Technology (OpenPower, CAPI, NVLINK)
Ai in insurance how to automate insurance claim processing with machine lear...Skyl.ai
Explore more at https://skyl.ai/form?p=start-trial
About the webinar
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
How the World's Leading Independent Automotive Distributor is Reinventing Its...NUS-ISS
In this captivating session, we'll unveil the profound impact of AI, poised to revolutionise the business landscape. Prepare to shift your perspective, as we transition from the lens of a data scientist to the visionary mindset of a product manager. We're about to demystify the captivating world of Generative AI, dispelling myths and illuminating its remarkable potential. We will also delve into the pioneering applications that Inchcape is leading, pushing the boundaries of what's achievable. Join us for an exhilarating journey into the future of AI, where professionalism meets unparalleled excitement, and innovation takes center stage!
Uniform Legal Framework for AI: The EU AI Act establishes a uniform legal framework for the development, marketing, and use of artificial intelligence systems within the EU, aimed at promoting trustworthy and human-centric AI while ensuring a high level of health, safety, and fundamental rights protection.
Risk-Based Approach: The regulation adopts a risk-based approach, classifying AI systems based on the level of risk they pose, from minimal to unacceptable risk, with stringent requirements for high-risk AI systems, particularly those impacting health, safety, and fundamental rights.
Prohibitions for Certain AI Practices: Unacceptable risk practices, such as manipulative social scoring and real-time biometric identification in public spaces without justification, are prohibited to protect individual rights and freedoms.
Mandatory Requirements for High-Risk AI Systems: High-risk AI systems must comply with mandatory requirements before they can be marketed, put into service, or used within the EU. These requirements include transparency, data governance, technical documentation, and human oversight to ensure safety and compliance with fundamental rights.
Conformity Assessment and Compliance: Providers of high-risk AI systems must undergo a conformity assessment procedure to demonstrate compliance with the mandatory requirements. This includes maintaining technical documentation and conducting risk management activities.
Transparency Obligations: AI systems must be transparent, providing users with information about the AI system's capabilities, limitations, and the purpose for which it is intended, ensuring informed use of AI technologies.
Market Surveillance: The EU AI Act establishes mechanisms for market surveillance to monitor and enforce compliance, with the European Artificial Intelligence Board (EAIB) playing a central role in coordinating activities across member states.
Protection of Fundamental Rights: The Act emphasizes the protection of fundamental rights, including privacy, non-discrimination, and consumer rights, with specific provisions to safeguard these rights in the context of AI use.
Innovation and SME Support: The regulation aims to foster innovation and support small and medium-sized enterprises (SMEs) through regulatory sandboxes and by reducing administrative burdens for low and minimal risk AI applications.
Global Impact and Alignment: While the EU AI Act directly applies to the EU market, its global impact is significant, influencing international standards and practices in AI development and use. Financial industry professionals worldwide should be aware of these regulations as they may affect global operations and international collaborations.
The financial industry is witnessing an emerging trend of Large Language Models (LLMs) applications to improve operational efficiency. This article, based on a round table discussion hosted by TruEra and QuantUniversity in New York in May 2023, explores the potential use cases of LLMs in financial institutions (FIs), the risks to consider, approaches to manage these risks, and the implications for people, skills, and ways of working. Frontline personnel from Data and Analytics/AI teams, Model Risk, Data Management, and other roles from fifteen financial institutions devoted over two hours to discussing the LLM opportunities within their industry, as well as strategies for mitigating associated risks.
The discussions revealed a preference for discriminative use cases over generative ones, with a focus on information retrieval and operational automation. The necessity for a human-in-the-loop was emphasized, along with a detailed discourse on risks and their mitigation.
Mathematical Finance & Financial Data Science Seminar
AI and machine learning are entering every aspect of our life. Marketing, autonomous driving, personalization, computer vision, finance, wearables, travel are all benefiting from the advances in AI in the last decade. As more and more AI applications are being deployed in enterprises, concerns are growing about potential "AI accidents" and the misuse of AI. With increased complexity, some are questioning whether the models actually work! As the debate about fairness, bias, and privacy grow, there is increased attention to understanding how the models work and whether the models are thoroughly tested and designed to address potential issues.
The area "Responsible AI" is fast emerging and becoming an important aspect of the adoption of machine learning and AI products in the enterprise. Companies are now incorporating formal ethics reviews, model validation exercises, and independent algorithmic auditing to ensure that the adoption of AI is transparent and has gone through formal validation phases.
In this talk, Sri will introduce Algorithmic auditing and discuss why Algorithmic auditing will be a formal process industries using AI will need. Sri will also discuss the emerging risks in the adoption of AI and discuss how QuSandbox, his company is building, will address the emerging needs of formal Algorithmic auditing practices in enterprises.
Seeing what a gan cannot generate: paper reviewQuantUniversity
Seeing what a GAN cannot Generate Paper review: Bau, David et al. “Seeing What a GAN Cannot Generate.” 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2019): 4501-4510.
Markowitz portfolio optimization is optimal in theory, however, when applied in practice it often fails catastrophically. Usually, this is addressed by various simplifications to increase robustness. In this talk I will make the case that the reason this theory fails in practice is because uncertainty in the parameter estimation is not taken into account. By using Bayesian statistics we can fix Markowitz and retain all its desirable properties while still having a robustness technique that can be easily extended. This talk is geared at intermediate and will give a general introduction to Bayesian modeling using PyMC3 and focus on application and code examples rather than theory.
With Alternative Data becoming more and more popular in the industry, quants are eager to adopt them into their investment processes. However, with a plethora of options, API standards, trying and evaluating datasets is a major hindrance to adoption of datasets.
Join Yaacov, Sri, James and Brad discuss the opportunities, pitfalls and challenges of Alternative Data and its adoption in finance
A Unified Framework for Model Explanation
Ian Covert, University of Washington
Explainable AI is becoming increasingly important, but the field is evolving rapidly and requires better organizing principles to remain manageable for researchers and practitioners. In this talk, Ian will discuss a new paper that unifies a large portion of the literature using a simple idea: simulating feature removal. The new class of "removal-based explanations" describes 20+ existing methods (e.g., LIME, SHAP) and reveals underlying links with psychology, game theory and information theory.
Practical examples will be presented and available on the Qu.Academy site
Reference:
Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert, Scott Lundberg, Su-In Lee
https://arxiv.org/abs/2011.14878
Machine Learning Interpretability -
Self-Explanatory Models: Interpretability, Diagnostics and Simplification
With Agus Sudjianto, Wells Fargo
The deep neural networks (DNNs) have achieved great success in learning complex patterns with strong predictive power, but they are often thought of as "black box"models without a sufficient level of transparency and interpretability. It is important to demystify the DNNs with rigorous mathematics and practical tools, especially when they are used for mission-critical applications. This talk aims to unwrap the black box of deep ReLU networks through exact local linear representation, which utilizes the activation pattern and disentangles the complex network into an equivalent set of local linear models (LLMs). We develop a convenient LLM-based toolkit for interpretability, diagnostics, and simplification of a pre-trained deep ReLU network. We propose the local linear profile plot and other visualization methods for interpretation and diagnostics, and an effective merging strategy for network simplification. The proposed methods are demonstrated by simulation examples, benchmark datasets, and a real case study in credit risk assessment. The paper that will be presented in this talk can be found here.
The world has changed in the last six months with COVID-19! There have been a shakeup in business models and funding. As companies and customers change their behaviors, we are seeing changes on how companies are addressing new challenges.
Join Fintech experts, D.Shahrawat and Sarah Biller for a not to be missed conversation on Fintech in the Post-Covid age
Master Class: GANS with Applications in Synthetic Data GenerationQuantUniversity
Join QuantUniversity for a complimentary fall speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Master Class: GANS with applications in Synthetic data generation
With various innovations in neural networks, GANs are becoming popular as a means of generating synthetic data.
In this master class, Gautier will discuss Generative Adversarial Networks (GANs) and discuss applications in synthetic data generation and other quantitative finance applications. He will also discuss his work on CORRGANS, Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks.[1]
Reference:
1. https://arxiv.org/abs/1910.09504
Qwafafew meeting: A Sector Rotation Strategy that Beats the Market Handily Es...QuantUniversity
We study a sector rotation strategy that switches among equity sectors, and from equities to bonds, based on signals of a high volatility regime in equities. We find that an implementation of the strategy using highly liquid sector-specific ETFs would have earned 6.6% more than the S&P 500 per year during the period Dec-1998 to Jul-2020, while experiencing much lower volatility. The performance of the strategy is especially strong during crisis periods such as the 1998-2002 crash and recession, the 2008-09 Great Recession, and the current Covid-19 Recession, with much higher and smoother returns than the S&P 500.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
The affect of service quality and online reviews on customer loyalty in the E...
Ml master class cfa poland
1. Machine Learning and AI in Finance
2020 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.quantuniversity.com
10/22/2020
CFA Society Poland
2. 2
Speaker bio
• Advisory and Consultancy for Financial
Analytics
• Prior Experience at MathWorks, Citigroup
and Endeca and 25+ financial services and
energy customers.
• Columnist for the Wilmott Magazine
• Author of forthcoming book
“The Model-Driven Enterprise”
• Teaches AI/ML and Fintech Related topics in
the MS and MBA programs at Northeastern
University, Boston
• Reviewer: Journal of Asset Management
Sri Krishnamurthy
Founder and CEO
QuantUniversity
3. 3
QuantUniversity
• Boston-based Data Science, Quant
Finance and Machine Learning
training and consulting advisory
• Trained more than 1000 students in
Quantitative methods, Data Science
and Big Data Technologies using
MATLAB, Python and R
• Building a platform for AI
and Machine Learning Exploration
and Experimentation
4. 1. Key trends in AI, Machine Learning & Fintech
2. An intuitive introduction to AI and ML
3. Case studies
4. Slides at:
5. https://academy.qusandbox.com/#/market/5f91612b99aa4a2469
1da7ef
6. Use Code: CFAPoland as registration code
Agenda
6. 6
The 4th Industrial revolution is Here!
Source: Christoph Roser at AllAboutLean.com
As per Wikipedia*, “The 4th Industrial Revolution ….. marked by emerging technology breakthroughs in a
number of fields, including robotics, artificial intelligence, nanotechnology, quantum computing, biotechnology,
the Internet of Things, the Industrial Internet of Things (IIoT), decentralized consensus, fifth-generation wireless
technologies (5G), additive manufacturing/3D printing and fully autonomous vehicles.”
* https://en.wikipedia.org/wiki/Fourth_Industrial_Revolution
7. 7
Scientists are disrupting the way we live!
Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/
8. 8
Interest in Machine learning continues to grow
https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
11. 11
• Machine learning is the scientific study of algorithms and statistical
models that computer systems use to effectively perform a specific task
without using explicit instructions, relying on patterns and inference
instead1
• Artificial intelligence is intelligence demonstrated by machines, in
contrast to the natural intelligence displayed by humans and animals1
Defining Machine Learning and AI
11
1. https://en.wikipedia.org/wiki/Machine_learning
2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
12. 12
Machine Learning & AI in finance: A paradigm shift
12
Stochastic
Models
Factor Models
Optimization
Risk Factors
P/Q Quants
Derivative pricing
Trading Strategies
Simulations
Distribution
fitting
Quant
Real-time analytics
Predictive analytics
Machine Learning
RPA
NLP
Deep Learning
Computer Vision
Graph Analytics
Chatbots
Sentiment Analysis
Alternative Data
Data Scientist
14. 14
The rise of Big Data and Data Science
14
Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
15. 15
Smart Algorithms
15
Distributing Computing Frameworks Deep Learning Frameworks
1. Our labeled datasets were thousands of times too
small.
2. Our computers were millions of times too slow.
3. We initialized the weights in a stupid way.
4. We used the wrong type of non-linearity.
- Geoff Hinton
“Capital One was able to determine fraudulent credit
card applications in 100 milliseconds”*
* http://go.databricks.com/hubfs/pdfs/Databricks-for-FinTech-170306.pdf
17. 17
“Financial Technologies or “Fintech” is used to describe
a variety of
innovative business models
and
emerging technologies
that have the potential to transform the financial
services industry ”
Technology drives finance!
https://www.iosco.org/library/pubdocs/pdf/IOSCOPD554.pdf
23. Risk Systems That Read®
• Northfield uses machine learning based analysis of news text
to describe how current conditions in financial markets are
different than usual.
• Typically, over 8000 articles per day containing more than
20,000 “topics” (companies, industries, countries) are
processed.
• The nature and magnitudes of these difference are used to
revise expectations of financial market risks for all global
equities and credit instruments on a daily basis.
24. 24
1. Leveraging large and diverse datasets for
Investment decision making at J.P. Morgan1
2. Improving Quantitative investing at AQR2
3. Using Sandboxes and labs to further innovation
in fintech at Fidelity3
4. Use of AI and ML increasing in ssset
management from idea generation to execution -
Wells Fargo4
Additional Use cases
1. https://www.jpmorgan.com/global/cib/research/investment-decisions-using-machine-learning-ai
2. https://www.aqr.com/Learning-Center/Machine-Learning
3. https://www.fidelitylabs.com/
4. https://www08.wellsfargomedia.com/assets/pdf/personal/investing/investment-institute/IG_Machines_Are_Coming_ADA.pdf
26. 26
• Automation to increase
• Digital transformation and move to the cloud finally happening
• Use of Synthetic data to increase
• Edge cases of AI put to truth test!
• Fintechs feeling the pressure to prove themselves!
• Human-in-the-loop AI to regain focus!
The changes have been drastic and sudden! What’s in
store for the industry is yet to be seen!
What does Covid2019 mean to adoption of AI and ML in
Financial services?
29. 29
Let’s get under the hood
29
Source: https://www.pikrepo.com/fcsda/yellow-hot-rod-car-with-hood-open
30. Machine Learning Workflow
Data Scraping/
Ingestion
Data
Exploration
Data Cleansing
and Processing
Feature
Engineering
Model
Evaluation
& Tuning
Model
Selection
Model
Deployment/
Inference
Supervised
Unsupervised
Modeling
Data Engineer, Dev Ops Engineer
Data Scientist/QuantsSoftware/Web Engineer
• AutoML
• Model Validation
• Interpretability
Robotic Process Automation (RPA) (Microservices, Pipelines )
• SW: Web/ Rest API
• HW: GPU, Cloud
• Monitoring
• Regression
• KNN
• Decision Trees
• Naive Bayes
• Neural Networks
• Ensembles
• Clustering
• PCA
• Autoencoder
• RMS
• MAPS
• MAE
• Confusion Matrix
• Precision/Recall
• ROC
• Hyper-parameter
tuning
• Parameter Grids
Risk Management/ Compliance(All stages)
Analysts&
DecisionMakers
31. 31
1. Data
2. Goals
3. Machine learning algorithms
4. Process
5. Performance evaluation
Key steps involved
32.
33. 33
Dataset, variable and Observations
Dataset: A rectangular array with Rows as observations and
columns as variables
Variable: A characteristic of members of a population ( Age, State
etc.)
Observation: List of Variable values for a member of the
population
34. 34
Variables
A variable could be:
▫ Categorical
– Yes/No flags
– AAA,BB ratings for bonds
▫ Numerical
– 35 mpg
– $170K salary
38. 38
• Descriptive Statistics
▫ Goal is to describe the data at hand
▫ Backward-looking
▫ Statistical techniques employed here
• Predictive Analytics
▫ Goal is to use historical data to build a model for prediction
▫ Forward-looking
▫ Machine learning & AI techniques employed here
Goal
38
39. 39
• Given a dataset, build a model that captures the
similarities in different observations and assigns
them to different buckets- Clustering
• Given a set of variables, predict the value of
another variable in a given data set- Prediction
▫ Predict salaries given work experience, education etc.
▫ Predict whether a loan would be approved given fico
score, current loans, employment status etc.
Predictive Analytics : Cross sectional datasets
39
44. 44
Supervised Algorithms
▫ Given a set of variables 𝑥!, predict the value of another variable 𝑦 in
a given data set such that
▫ If y is numeric => Prediction
▫ If y is categorical => Classification
▫ Example: Given that a customer’s Debt-to-Income ratio increased 20%, what are
the chances he/she would default in 3 months?
Machine Learning
44
x1,x2,x3… Model F(X) y
45. 45
Unsupervised Algorithms
▫ Given a dataset with variables 𝑥!, build a model that captures the
similarities in different observations and assigns them to different
buckets => Clustering
▫ Example: Given a list of emerging market stocks, can we segment them
into three buckets?
Machine Learning
45
Obs1,
Obs2,Obs3
etc.
Model
Obs1- Class 1
Obs2- Class 2
Obs3- Class 1
46. 46
• Parametric models
▫ Assume some functional form
▫ Fit coefficients
• Examples : Linear Regression, Neural Networks
Supervised Learning models - Prediction
46
𝑌 = 𝛽! + 𝛽" 𝑋"
Linear Regression Model Neural network Model
47. 47
• Non-Parametric models
▫ No functional form assumed
• Examples : K-nearest neighbors, Decision Trees
Supervised Learning models
47
K-nearest neighbor Model Decision tree Model
53. 53
• What transformations do I need for the x and y variables ?
• Which are the best features to use?
▫ Dimension Reduction – PCA
▫ Best subset selection
– Forward selection
– Backward elimination
– Stepwise regression
Feature Engineering
53
57. 57
• Fit measures in classical regression modeling:
• Adjusted 𝑅! has been adjusted for the number of predictors. It increases
only when the improve of model is more than one would expect to see by
chance (p is the total number of explanatory variables)
𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅! = 1 −
⁄∑"#$
%
(𝑦" − 0𝑦")! (𝑛 − 𝑝 − 1)
∑"#$
%
𝑦" − 4𝑦"
! /(𝑛 − 1)
• MAE or MAD (mean absolute error/deviation) gives the magnitude of the
average absolute error
𝑀𝐴𝐸 =
∑"#$
%
𝑒"
𝑛
Prediction Accuracy Measures
58. 58
▫ MAPE (mean absolute percentage error) gives a percentage score of
how predictions deviate on average
𝑀𝐴𝑃𝐸 =
∑!"#
$
𝑒!/𝑦!
𝑛
×100%
• RMSE (root-mean-squared error) is computed on the training and
validation data
𝑅𝑀𝑆𝐸 = 1/𝑛 2
!"#
$
𝑒!
%
Prediction Accuracy Measures
59. 59
1. Data
2. Goals
3. Machine learning algorithms
4. Process
5. Performance Evaluation
Recap
60. Machine Learning Workflow
Data Scraping/
Ingestion
Data
Exploration
Data Cleansing
and Processing
Feature
Engineering
Model
Evaluation
& Tuning
Model
Selection
Model
Deployment/
Inference
Supervised
Unsupervised
Modeling
Data Engineer, Dev Ops Engineer
Data Scientist/QuantsSoftware/Web Engineer
• AutoML
• Model Validation
• Interpretability
Robotic Process Automation (RPA) (Microservices, Pipelines )
• SW: Web/ Rest API
• HW: GPU, Cloud
• Monitoring
• Regression
• KNN
• Decision Trees
• Naive Bayes
• Neural Networks
• Ensembles
• Clustering
• PCA
• Autoencoder
• RMS
• MAPS
• MAE
• Confusion Matrix
• Precision/Recall
• ROC
• Hyper-parameter
tuning
• Parameter Grids
Risk Management/ Compliance(All stages)
Analysts&
DecisionMakers
63. 63
Claim:
• Machine learning is better for fraud
detection, looking for arbitrage
opportunities and trade execution
Caution:
• Beware of imbalanced class problems
• A model that gives 99% accuracy may still
not be good enough
1. Machine learning is not a generic solution to all problems
64. 64
Claim:
• Our models work on
datasets we have tested on
Caution:
• Do we have enough data?
• How do we handle bias in
datasets?
• Beware of overfitting
• Historical Analysis is not
Prediction
2. A prototype model is not your production model
65. 65
AI and Machine Learning in Production
https://www.itnews.com.au/news/hsbc-societe-generale-run-
into-ais-production-problems-477966
Kristy Roth from HSBC:
“It’s been somewhat easy - in a funny way - to
get going using sample data, [but] then you hit
the real problems,” Roth said.
“I think our early track record on PoCs or pilots
hides a little bit the underlying issues.
Matt Davey from Societe Generale:
“We’ve done quite a bit of work with RPA
recently and I have to say we’ve been a bit
disillusioned with that experience,”
“the PoC is the easy bit: it’s how you get that
into production and shift the balance”
66. 66
Claim:
• It works. We don’t know how!
Caution:
• It’s still not a proven science
• Interpretability or “auditability” of
models is important
• Transparency in codebase is paramount
with the proliferation of opensource
tools
• Skilled data scientists who are
knowledgeable about algorithms and
their appropriate usage are key to
successful adoption
3. We are just getting started!
67. 67
Claim:
• Machine Learning models are
more accurate than
traditional models
Caution:
• Is accuracy the right metric?
• How do we evaluate the
model? RMS or R2
• How does the model behave
in different regimes?
4. Choose the right metrics for evaluation
68. 68
Claim:
• Machine Learning and AI will replace
humans in most applications
Caution:
• Beware of the hype!
• Just because it worked sometimes
doesn’t mean that the organization can
be on autopilot
• Will we have true AI or Augmented
Intelligence?
• Model risk and robust risk
management is paramount to the
success of the organization.
• We are just getting started!
5. The Robots are coming!
https://www.bloomberg.com/news/articles/2017-10-20/automation-
starts-to-sweep-wall-street-with-tons-of-glitches
71. 71
1. Case Intro
2. Data Exploration of the Credit risk data set
3. Problem Definition and Machine learning
4. Performance Evaluation
5. Deployment
Case study
72. 72
Credit decisions
Credit-scoring models and techniques assess the risk in
lending to customers.
Typical decisions:
• Grant credit/not to new applicants
• Increasing/Decreasing spending limits
• Increasing/Decreasing lending rates
• What new products can be given to existing applicants ?
73. 73
How Lending club works?
https://www.lendingclub.com/public/how-peer-lending-
works.action
74. 74
• How much should I expect as interest?
• Is my borrower credit worthy?
• How much interest would a similar borrower pay?
• What is the repayment and default rate for a similar borrower?
Investor’s big decisions
76. 76
Credit Risk pipeline
Data Ingestion
from Lending
Club
Pre-Processing
Feature
Engineering
Model
Development
and Tuning
Model
Deployment
Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
79. 79
All scenarios haven’t
played out
• Stress scenarios
• What-if scenarios
Challenges with real datasets
Figure ref: http://www.actuaries.org/CTTEES_SOLV/Documents/StressTestingPaper.pdf
80. 80
Missing values
• Missing at random
• Missing sequences
• Need data to fill frames
Challenges with real datasets
81. 81
• Access
▫ Hard to find
▫ Rare class problems
▫ Privacy concerns
making it difficult to
share
Challenges with real datasets
82. 82
Imbalanced
• Need more samples of rare
class
• Need proxies for data points
that were not observed or
recorded
Challenges with real datasets
90. 90
1. Case Intro
2. Data Exploration of WIG20 stock data
3. Problem Definition and Machine learning
4. Deployment
Case study
91. 91
Clustering stocks
• Which stocks are like each other?
• Are growth stocks behaving like growth stocks or value
stocks?
• Does the time series of prices & returns reveal which
stocks are close to each other?
106. 106
• If computers can understand language, opens huge possibilities
▫ Read and summarize
▫ Translate
▫ Describe what’s happening
▫ Understand commands
▫ Answer questions
▫ Respond in plain language
Language allows understanding
107. 107
• Describe rules of grammar
• Describe meanings of words and their
relationships
• …including all the special cases
• ...and idioms
• ...and special cases for the idioms
• ...
• ...understand language!
Traditional language AI
https://en.wikipedia.org/wiki/Formal_language
108. 108
What is NLP ?
Jumping NLP Curves
https://ieeexplore.ieee.org/document/6786458/
110. 110
• Ambiguity:
▫ “ground”
▫ “jaguar”
▫ “The car hit the pole while it was moving”
▫ “One morning I shot an elephant in my pajamas. How he got into my
pajamas, I’ll never know.”
▫ “The tank is full of soldiers.”
“The tank is full of nitrogen.”
Language is hard to deal with
112. 112
• Many ways to say the same thing
▫ “the same thing can be said in many ways”
▫ “language is versatile”
▫ “The same words can be arranged in many different ways to express
the same idea”
▫ …
Language is hard to deal with
113. 113
• APIs
• Human Insight
• Expert Knowledge
• Build your own
Options?
117. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Contact
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