How data portability can improve our lives

Two and a half quintillion bytes or 2,500,000,000,000,000,000 bytes of data are generated every single day. The majority of all data in existence was generated within the past few years and is created collectively by individuals, governments and businesses. Data is knowledge but is meaningless until it is processed and interpreted in context at which point it becomes information on which a judgement or decision can be taken. Data is now considered a key ingredient for current business revenue models, a yardstick by which service success is measured. Entities that do not use data effectively and appropriately increasingly risk becoming extinct.

Data is knowledge but is meaningless until it is processed and interpreted in context at which point it becomes information on which a judgement or decision can be taken.

Data is knowledge but is meaningless until it is processed and interpreted in context at which point it becomes information on which a judgement or decision can be taken.

The protection of personal data, consumers and competition in markets is increasingly important for policy makers and competition authorities. Open Banking regulation from the UK Competition and Markets Authority (CMA) and General Data Protection Regulation (GDPR) by the European Union are prime examples of this shift in importance. 

The right to data portability was introduced in the GDPR and enacted in the Data Protection Act 2018. This gives individuals the right to not only obtain a copy of their personal data but also to instruct organisations to transfer these data to third parties. These data must be machine readable and in a common format which renders them useful. This appears to be fairly innocuous, however, represents a radical step in delivering greater autonomy to consumers. This also raises several important questions related to data harmonisation, market effects, organisational processes, and crucially consumer benefit. This article is the first in a series that will examine these issues and the potential impact on business and consumers.

Financial services presents a compelling use case for data given the fundamental nature of banking and finance on our daily lives. The use of artificial intelligence (AI) has the potential to transform the processes, products and services of the industry. For example, AI can analyse millions of data points to detect fraudulent transactions that tend to go unnoticed by humans while simultaneously actively learning and calibrating to new potential threats. Simple risk factor checklists are no longer enough given the fact that global fraud losses were £3.2 trillion in 2018. 

Open Banking is a great opportunity for financial institutions to leverage the masses and masses of consumer data held to generate greater value and new potential revenue streams. However, this is no mean feat given the fact that these data exist in a multitude of formats on difficult to maintain legacy systems installed decades ago and written in code developed in the 1950s or worse, on paper. Unstructured (i.e. not pre-processed) or inconsistently structured streams of financial data poses a risk to the quality of the resultant algorithms. This risk can be compounded by the loss of corporate memory as coders familiar with legacy systems retire. Data harmonisation with the aim of making the structure of all financial data easily analysable by machines will be essential to the removal of current bottlenecks. Cooperation between stakeholders and market actors will be required to achieve this and a diverse workforce with a variety of skills will be required. 

The digitalisation of consumer banking has long been an uphill battle given the legacy of deregulation where the risk taking of securities trading lived in parallel with the culture of caution and conservatism of banking. The consumer trust lost as a result of the financial crisis a decade ago will require more than marketing savvy but a serious revision of the social contract between banks and consumers. 

Kalgera was developed to serve people typically overlooked and this presents a unique challenge. These groups can be difficult to reach, have variable personal circumstances and hold little trust in financial institutions. Multiple human factors were considered in the design process as well as differences in personal relationships with money. For example, older people tend to see money as something to share which enriches the lives of those around them rather than a means of achieving individualistic goals. Using data to better understand the true challenges of the consumer and go on to predict when and how they will require service to meet their challenges unlocks value to the individual and markets. Shifting to truly mission driven data powered services deployed in an ethical way can improve financial services and build social capital.

Further reading

Analytics Comes of Age, McKinsey Analytics, 2018.

The Financial Cost of Fraud 2018, Crowe, 2018.

Artificial Intelligence in Finance, Buchanan, Bonnie, 2019.