PRACTICAL DATA SCIENCE
IN FINANCIAL SUPERVISION
THIS
TRAINING PROGRAMME IS FOR YOU,
IF YOU….
- Are in a leadership role in any organisation either private or public sector or are an aspiring analyst delving into data science and seeking thorough initiation into this captivating field
- Aspire to drive the strategic agenda, application, and responsible use of data science in addressing real-world supervisory challenges
- Are eager to broaden your understanding and proficiency in data science across various supervisory use cases and technologies
- Seek to enhance your data science capabilities by exploring live data sets, tools, and techniques while embracing interdisciplinary thinking, roles, and approaches
Catch the wave of digitalisation toward the future of data-driven intelligence!
As the tides of the financial sector continue ebbing toward an increasingly digital and interconnected future, the work of financial supervision is progressively converging with that of data science. Faced with a deluge of data, it is imperative that the next generation of leaders at the centre of this digital transformation are able to leverage both the appropriate data science tools and supervisory best practices to effectively keep pace with market developments and understand evolving consumers behaviours.
By pioneering suptech solutions, we can unlock insights from previously untapped data sources. Balancing legacy supervisory processes with cutting-edge data science approaches is key to fueling a sustainable modernisation of supervisory tools and methodologies. This course equips public sector supervisors with practical knowledge of data science best practices, while empowering seasoned, socially conscious data scientists with ample opportunities to create positive impact through innovative suptech applications.
Through a 6-week immersive experience you will walk away with:
- Practical, hands-on familiarity with data science, encompassing advanced analytics, data strategy, and management roles, providing you with the expertise to explore application opportunities beyond the course within the context of financial supervision
- Proficiency in utilising advanced analytics and machine learning techniques to extract meaningful intelligence from raw data, enabling you to make data-driven strategic decisions with precision and confidence
- Enhanced technical skills in data science, including familiarity with relevant programming languages, data manipulation, and statistical modeling, equipping you with the necessary tools to excel in data-driven environments
- A deep understanding of data governance and ethics, ensuring you can responsibly handle sensitive data and comply with regulatory requirements while leveraging data science for improved financial supervision
- The ability to effectively communicate complex data-driven insights to both technical and non-technical stakeholders, enabling you to bridge the gap between data science expertise and practical application in financial supervision
Curriculum
Introduction
Get oriented and inspired
- Develop an understanding of the context and importance of data science in financial supervision
- Identify reasons for data science increasing significance in the field of financial supervision
- Distinguish the value proposition of data science for both financial supervisors and data scientists
- Feel inspired to think beyond the boundaries of existing roles towards an inter-disciplinary perspective
Module 1
1 week
Data science as suptech: a primer
- Describe data science roles, tools, and best practices
- Discuss and explain the SupTech Taxonomy and SupTech Generations models and their application
- Identify key applications of data science in the context of suptech
Module 2
3 weeks
Data science in the wild: hands-on suptech case studies
Three interactive case studies & data gymnasium
- Identify the supervisory value proposition of the data science solution
- Practice via interactive data science notebooks the technical details of the implementation and tools utilized
- Investigate and compare the implications of changing parameters in the synthetic model version
Module 3
1 week
Deploying data science products in the supervisory context
- Define the importance of the responsible use of data science tools and technologies
- Identify the ethical considerations when using data science in financial supervision
- Categorize data science management and team structure in financial supervision
- Weigh opportunities for capacity and skills application within institutional and supervisory contexts
Your capstone
The capstone project offers you a unique opportunity to integrate your learning, showcase expertise, and create tangible value for your organisation. Throughout the project, you will build a conceptual brief on a proposed application of data science in supervision, drawing from the diverse portfolio of solutions provided by the Data Gymnasium. These solutions encompass various models, tools, and techniques such as supervised and unsupervised machine learning, trend analysis, sentiment analysis, network analysis, natural language processing (NLP), and image processing.
Demonstrating your critical thinking skills and applying new analytical frameworks to address supervisory challenges within your organisation, you will make strategic decisions on the deployment of new technologies and data science to effectively tackle these challenges.
DATA SCIENCE CAPACITY
- Actively explore the value of interdisciplinary collaboration between financial supervisors and data scientists and understand why this collaboration is increasingly relevant in today’s landscape
- Gain a practical understanding of the roles, tools, and best practices involved in building and deploying advanced analytics tools, machine learning models, and other suptech data products
- Apply the SupTech Taxonomy and SupTech Generations frameworks introduced in our State of SupTech Report to develop a strategic approach for building data science roadmaps, assembling teams, and creating products that can be utilised in financial supervision beyond the course
INTERACTIVE CASE STUDIES & DATA GYMNASIUM
- Delve into three practical examples of data science in suptech, dedicating one week to each example
- Identify the specific supervisory value proposition of each data science solution, gaining a clear understanding of how it can enhance financial supervision
- Learn the technical intricacies of implementing each solution and become proficient in employing the appropriate tools for data analysis and modeling
- Gain an understanding of the implications of altering parameters within synthetic versions of the models, allowing you to grasp the broader impact of such changes on the effectiveness of the solutions
YOUR CERTIFICATE OF COMPLETION
This training programme offers you the opportunity to earn a certificate of completion from one of the world’s leading business schools – the University of Cambridge Judge Business School. Your certificate will be issued in your legal name and shared with you upon successful completion of the training programme, as per the stipulated requirements.
Tuition Fee
$2,200/person
5 seats minimum
$2,500
Special Discount
Financial assistance might be available for participants from emerging economies and developing countries. If funding represents a challenge, please connect with us.
DISCOUNTS FOR INDIVIDUALS
ENROL IN 2 PROGRAMMES
$2,200*
ENROL IN 3 PROGRAMMES
$2,000*
*TUITION FEE PER PROGRAMME
LEAD THE JOURNEY TO DIGITALLY TRANSFORM FINANCIAL SUPERVISION
The training programme is open to all individuals who currently hold positions such as supervisor, regulator, policymaker, economist, examiner, actuary, analyst, data scientist, or technologist within regulatory, supervisory, or policymaking authorities.
We highly encourage agencies to enrol individuals representative of the various roles relevant for the development of a suptech application, roadmap, strategy, open data project, innovation hub, or digital transformation initiative. Take special note of the discounted tuition fee for enrolment of 5 people or more.
Testimonials
This Programme Is Supported By
