Webinar Report - Big Data Analytics in Digital Finance: Opportunities, Risks and Approaches for Data Protection

Aug 13, 2019 | Global; Online


Traditionally, financial institutions have sought to protect and use their own customer’s data for both business and regulatory objectives. The emergence of “big data analytics” implies that a broad range of financial and non-financial data are being shared across a wide set of parties. While enhanced data capacity and analytical capability holds great promise for customers, providers and supervisors of financial services, this can only be properly harnessed if consumer and data protection risks are addressed.

This webinar discussed how “Big Data Analytics” is being applied in Africa to extend financial services to a customer segment that have been historically been left out, as well as the benefits and risks associated with data-intensive financial services. It also provided recommendations to support regulators in establishing enabling frameworks for the responsible use of consumer data and automated decision-making in financial services.

The webinar took place on April 4th, 2019 at 1:00pm GMT 


  • Abdelkader Benbrahim, Associate Financial Sector Advisor, Making Finance Work for Africa
  • Dr. Alexander Dix, LL.M, Vice Chair of the European Academy for Freedom of Information and Data Protection
  • Tim Ohlenburg, Researcher on financial computing, University College London


The speakers introduced the subject by making the distinction between the following three technologies as they are applied differently in the field of “Data Science”.

  • “Big data”: Large and/or complex data sets that exceeds the capacity of traditional relational databases to capture, manage and process data with low latency.  
  • “Data Analytics”: The use of advanced techniques (mining, natural language processing) to analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in faster and better decisions.
  • “Machine Learning”: A form of artificial intelligence that enables a system to learn from data through algorithms rather than explicit programming. 

Drivers affecting the development of Big Data Analytics

  • Limited proven use-case for third-party data
  • Organisations are pursuing partnership models in lieu of transactional relationships
  • Most organisations are still testing, refining and experimenting with big data analytics
  • Employment of big data analytics requires strategy, leadership support

Usage of personal data in digital financial services

Data (personal data stored) is used by financial service providers for data analytics, e.g. credit scoring based on automated decision making/ AI.

Data Intensive Financial Services presents opportunities and risks. Some opportunities are the expansion of financial inclusion to previously unserved customers, service cost reduction, better customer understanding, and services more adapted to customers specific needs. 

The potential risks include: the loss of privacy, inaccurate profiling leading to fewer and costlier offers (unfair discrimination), cybersecurity risks, data breaches. 

Recommendations for policy makers and regulators in establishing responsible usage of personal data and artificial intelligence in financial services

  • Demonstrate leadership in data protection with the right regulatory mix
  • Collaborate to uphold privacy in digital age with co-regulation and consultation
  • Enhance data awareness to develop a culture of data protection
  • Empower customers to be the sovereigns of their data
  • Hold providers accountable for more transparency and to limit the risk of discrimination
  • Enforce secure data storage


The questions mostly focused on the role of regulators and how they should promote the use of big data while ensuring the protection of consumers.