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    More than 30,000 professionals make up the ecosystem of Cetif: we facilitate the meeting and exchange between banks, insurers and companies in an academic Center, competent and independent environment to share knowledge, experience and strategies on the most innovative drivers of change.

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    16 Research Hubs focused on dynamics of strategic evolution, regulatory updates, organizational and process practices, and the effects of digitization: we study innovation trends and best practices and share them with our communities.

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Academy
Advisory
academy

DATA & AI 4 BUSINESS

1-4 DAYS DUAL MODE
October 28-30 and November 5-12, 2024
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Early-bird
-100.00 Euro
With enrollment by
30.09.2024
Closing Date for Enrollment: 18.10.2024

Every second, 150,000 GB of data circulate on the Web, and technology now offers the ability to process large masses of information and perform highly complex tasks. Algorithms and artificial intelligence will become increasingly prevalent and support many activities, from automating processes to devising business strategies and maintaining customer relationships. Understanding how algorithms, Artificial Intelligence and the data processing behind this evolution work becomes increasingly relevant for those in policy and control roles in companies to point to the best solutions and identify and solve critical

Date
October 28-30 and November 5-12, 2024
Industry
CROSS INDUSTRY
Subject areas
Digital Innovation & FinTech
Language
Italian
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Registration fee
Adherent Cetif
€2500.00 + VAT*
Non-Adherent Cetif
2700.00 + VAT*
*Quota per participant
Discounts and economic benefits
To whom it is addressed

All those in various business roles who want to increase their theoretical and practical skills on the topics of data governance and exploitation, algorithms, and artificial intelligence for financial business

Formative Objectives.

To provide an overview of the Big Data phenomenon and algorithmic analytical techniques (Advanced Analytics and algorithms, foundations of Artificial Intelligence) in the financial sector and to explore their relevance to different management areas.

Understand the main applications arising from the use of Big Data through Advanced Analytics in the financial sector

The format

Innovative and effective teaching techniques geared toward the transfer of knowledge and behavior through: 

  • theoretical lectures
  • moments of consolidation and critical revision of content
  • exercises and simulations
  • use case and benchmark

The Course will be held in. Dual Mode: the participant at the time of registration will be able to communicate the most facilitated mode of fruition

Course Schedule.

  • Main application trends in the banking and insurance industry
  • The critical issues of machine learning
  • Data quality
  • Correct identification of the training dataset
  • Undertraining
  • Overtraining-Underfitting-Overfitting
  • AI for digital identification
  • Know Your Customer with AI
  • AI-pricing techniques to detect consumer orientations
  • Robo-advisory
  • Algorithms for anti-fraud, HR and compliance
  • From traditional banking/insurance to adaptive lifestile banking/insurance
  • Presentation of Use Cases and Experiments
  • AML customer risk scoring
  • Behavioural Analytical Model: behavioral analysis for network monitoring (managers, promoters, agencies)
  • Data Governance, Data Quality and Regulation
  • The Basics of Data Quality
  • IKQI according to Juran's classification: synoptic overview of indicators, scope and metrics
  • Behind the scenes of the KQIs
  • Monitoring data quality with Shewart's control charts and diagrams
  • The costs of "non-quality" and the ROI of quality: a practical example
  • Set up quality control over data flows to customers and supervisory bodies
  • Designing and interpreting data quality reporting
  • Data Governance and the role of different business functions in Data Governance according to the DAMA model
  • The ethical issues of AI
  • Constraints in the use of data
  • The critical issues of personal data use in the potential of algorithms
  • Algorithmic distortion: what it is, how it occurs, and what it can cause
  • Discrimination risk and how machine learning can discriminate
  • The 10 key principles of ethical AI
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