Big Data Analytics for Financial Institutions
This intensive 3-day course provides participants with a comprehensive understanding of essential elements. Through a blend of examples, case studies, and analyses, it explores Big Data Analytics in Banking and Finance, Data visualisation and exploration
Course Objectives
Embark on our interactive training journey in data analytics for finance and business. Through dynamic case studies and hands-on exercises, gain insights into financial analysis, credit risk assessment, and market dynamics. Learn to build robust models for customer creditworthiness evaluation and optimise data workflow efficiency.
Benefits of Attending
This course offers attendees a competitive edge by teaching the essentials of big data analytics. Participants will develop data-driven strategies for short and long-term success, impacting financial stability and business performance. They’ll refine decision-making processes, differentiate efficiency from effectiveness, and gain insights into customer behaviour. Moreover, they’ll learn to predict client creditworthiness, utilize machine learning for outcome forecasting, and make rapid, reliable decisions based on advanced data analysis.
Who Should Attend
Participants of this course can expect to enhance their skills in big data analytics using Sophisticated advanced statistical techniques such as neural networks. The target audience are:
- Credit Analysts
- Financial Analysts
- Banking Advisors
- Dealers
- Client Relationship Managers
- Banking Finance Managers
- Banking Relationship Managers
- Business Controllers
TRAINER PROFILE
Dr. Hussein A. Abdou is a Professor of Finance Banking at The University of Huddersfield Business School, UK. He has massive experience in teaching at higher education level for more than years. He taught in various universities across the UK and worldwide including: The University of Plymouth Management School, UK; The University of Salford Business School, UK; Mansoura University, Egypt and Manouba University, Tunisia. Hussein completed his PhD in Bank Risk Modeling applying Credit Scoring Techniques at The University of Plymouth, UK in 2009. He is a Fellow of the Higher Education Academy in the UK. Hussein’s academic background is in Finance and Banking, including credit risk management in financial institutions,Islamic banking finance, and applications of non-parametric modelling techniques such as Neural Networks and Genetic Programming in Finance and Banking; whilst his professional background is in building scoring models for banks in a number of developing countries including Egypt, Cameroon and India; and evaluating software packages for financial applications. He is a reviewer of ESRC, and member of ESRC Advanced Quantitative Methods AQM assessment panel. He is also external reviewer and marker for CIMA Certificate in Islamic Finance. Hussein has wide experience in delivering training courses in Banking, Finance and Accounting within the UK and worldwide, particularly in Islamic Banking, Finance and Economics; Bank Risk Management, Scoring Modeling Techniques and Innovative Management.
Learning Benefits
This is a highly interactive training course where case studies and hands-on data analytics are used to illustrate key learning points. This course will allow participants to apply concepts acquired during the course to real-life scenarios. The training course will enable participants to:
- Gain insight into financial, credit risk and business performance
- Develop participants insight
- Market the right product to the right client at the right time
- Build models to evaluate customer creditworthiness
- Compare large and small data
- Work efficiently with large data
Training Outline
Overview
- Introduction of Big Data
- Types of Big Data
- Comparison between small and Big Data
- Challenges of Big Data
Big Data analytics
- Different approaches dealing with Big Data
- Conventional statistical modelling techniques
- Sophisticated machine learning techniques
- Evaluation criteria
How to analyse big data: A practical approach
- Applications of Big Data analytics
- Preparing Big Data for analysis
- Coding big data for analysis
- Different software packages
Responding to SupplyChain Risk Management
- Response to Risk
- Alternative Responses (Ignore or Accept Risk, Reduce the probabilityofRisk, Reduce or Limit the Consequences, Transfer, Share or Deflect the Risk, Make Contingency Plans, Adapt to It, Oppose a Change, Move to AnotherEnvironment)
- Choosing the Best Response (Systematic Analysis, Decision Trees, Inappropriate Responses)
- Implementation and Activities