Analytics & Predictive Analytics

Due to the digitalization of business processes and the intensification of information technology, the volume of data within companies is constantly growing. The networked storage of business management and system data enables past, present and future-oriented evaluation of data in order to make well-founded business decisions. 

For the instrumentalization of data analyses and the implementation of future-oriented data analyses, relevant data sources have to be identified and assumptions for the creation of algorithms have to be developed in cooperation with companies in order to derive recommendations for action or to uncover customer needs based on behavioral patterns, dependencies and the relationship structure. Through the targeted combination and analysis of business management and system data, optimization potentials and trends in the business processes of a credit institution become visible and responsibilities are enabled to decide on preventive or mitigating measures.

Use Case 1 | Batch Processing Pattern Recognition

  • Banks and financial institutions have multiple streams where they transform data into information (mainly historical batch processes).
  • ML/DL can find patterns in the data flow to predict the data quality of a single transaction. 
  • In addition, ML/DL can advise how to solve the data issue (spanning from personalized emails up to automated corrections).
Book: The Impact of Digital Transformation and FinTech on the Finance Professional

» OriginalPaper

Book Series - Articles

The Digital Journey of Banking and Insurance, Volume I
» Project report on practice-oriented implementation
​​​​​​​» AI for Impairment Accounting​​​​​​​

Use Case 2 | Real Estate Risk

A comprehensive approach to risk management and tools for a modern automation of information processing of unstructured risk information.

Buch: The Impact of Digital Transformation and FinTech on the Finance Professional

» OriginalPaper

Use Case 3 | Value-Driver-Oriented Planning

  • By identifying key value drivers and streamlining the planning process, FSI organizations can focus on key drivers to optimize its business model. 
  • Using sensitivity analysis and approaches from the field of predictive analytics, ifb leverages its learnings to develop a key KPI’s library.
Book: The Impact of Digital Transformation and FinTech on the Finance Professional

» OriginalPaper

Book Series - Articles

The Digital Journey of Banking and Insurance, Volume I
» Value-Driver-Oriented Planning – Management-Oriented Design and Value Driver Identification    ​​​​​​​

Use Case 4 | Financial Navigator

  • Reference Framework for Stress testing in a multiperiod risk analysis 
  • Enables a much better understanding and reflection of complex chain of effects in the capital planning process dependent on client needs
  • Provides a comprehensive view on simulated forecasts for the economic capital using modern technology and modern methodologies (e.g. open source software, machine learning algorithms, Agent-based-modeling)
    Facilitates the ad-hoc capital planning
  • Allows to compare the impact of different models on the results
Book: The Impact of Digital Transformation and FinTech on the Finance Professional

» OriginalPaper

Use Case 5 | Rating DL/ML

  • Machine Learning models can add significant improvements to the prediction power by using non-linear modeling.
  • Using Tensorflow opens up for feature to improve the rating accuracy.
Book: The Impact of Digital Transformation and FinTech on the Finance Professional

» OriginalPaper

Book Series - Articles

The Digital Journey of Banking and Insurance, Volume I
» Financial Navigator – A Modern Approach to Analytical Banking
 

Use Case 6 | Reduction of test data through representative identification

  • The forecast of a bank's RWA (and in combination with capital development also the CET1 ratio1) has become a central management task.
  • The (normative) capital planning requires projections of the RWA development over several years. A flash report becomes the first indication of RWA and thus the CET1 ratio for the current month.
Book: The Impact of Digital Transformation and FinTech on the Finance Professional

» OriginalPaper

Use Case 7 | Non Financial Risk

  • The use case also shows a practical application of graph databases and graph algorithms.
  • The use case NFR shows how the different risk categories can be analyzed in a connected view, including risk driver analysis and compact management presentation.
Book Series - Articles

The Digital Journey of Banking and Insurance, Volume I
» Breaking New Grounds in Non-Financial Risk Management

The Digital Journey of Banking and Insurance, Volume II
» Sentiment Analysis for Reputational Risk Management
» Use Case – NFR – Using GraphDB for Impact Graphs 
» Use case – NFR – HR Risk 

The Digital Journey of Banking and Insurance, Volume III
» Graph Databases

Use Case 8 | Data Science & Machine Learning - Insurance

  • What are challenges and obstacles in fraud prevention in insurance claims management.
  • The common algorithms (like autoencoder) and more complex anomaly detection algorithms can support to provide an optimal solution 
Book Series - Articles

The Digital Journey of Banking and Insurance, Volume I
» Actuarial Data Science

The Digital Journey of Banking and Insurance, Volume II
» Use Case – Fraud Detection Using Machine Learning Techniques

The Digital Journey of Banking and Insurance, Volume III
» Special Data for Insurance Companies

Use Case 9 | Machine Learning & Deep Learning

  • Machine learning techniques are concerned with general pattern recognition or the construction of universal approximators of relations in the data in situations where no obvious a priori analytical solution exists. 
  • As a new trend of machine learning, deep learning showed up at the end of last century and became one of the most efficient learning algorithms.
  • Many of the popular Machine Learning & Deep Learning Frameworks are based on open-source.
Book: The Impact of Digital Transformation and FinTech on the Finance Professional

» Mathematical Background of Machine Learning
» Deep Learning: An Introduction 

Book Series - Articles

The Digital Journey of Banking and Insurance Volume II
» Open-Source Software

The Digital Journey of Banking and Insurance Volume III
» Overview Machine Learning and Deep Learning Frameworks 
» Methods of Machine Learning

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