Big Data

SHARE

FOLLOW EBF

FOR MORE INFO CONTACT

Noemie Papp

Legal adviser

Consumer Affairs & Coordinator Digital issues

n.papp@ebf-fbe.eu

www.ebf-fbe.eu

DOWNLOAD

FOR MORE INFO CONTACT

Noemie Papp

Legal Adviser

Consumer Affairs & Coordinator Digital issues

n.papp@ebf-fbe.eu

www.ebf-fbe.eu

FIVE ACTIONS FOR THE FUTURE OF DIGITAL BANKING:

  • 1Boost digital inclusion by developing public-private partnerships between banks and public authorities.
  • 2Organise a full-fledged stakeholders debate on innovative payments and pan-EU solutions with consideration for costs and benefits for all stakeholders.
  • 3Promote a cybersecurity awareness campaign highlighting existing and new threats, making digital finance more secure and building trust.
  • 4Conduct a ‘fitness check’ on existing financial services legislation to adjust to the global market reality and to ensure consistency.
  • 5Conduct a joint assessment by both government and industry on opportunities and impact of crypto-technologies.

FOR MORE INFO CONTACT

Noemie Papp

Legal Adviser

Consumer Affairs & Coordinator Digital issues

n.papp@ebf-fbe.eu

www.ebf-fbe.eu

Data value chain / Big data

Data value chain / Big data

RECOMMENDATIONS

Data value chain / Big data

  • 1 Facilitate interoperability and fair access to digital platforms.
  • 2 Promote the benefits of data analytics and ensure a coherence in their application.
  • 3 Ensure the right balance between data protection and data analytics methods to allow balanced restrictions on profiling (e.g. preserve informed consent but make this provision more flexible for fraud prevention/detection or creditworthiness assessment); allow personalised and anonymised data; and link data ownership to the data analytics capabilities of the company.
  • 4 Allow the full use of data analytics in the assessment of creditworthiness and fraud prevention.

Data value chain / Big data

The use of data is growing exponentially, in terms of use, variety, volume and velocity. Data are at the centre of this digital revolution and consequently the use of data analytics is creating increasingly new opportunities both for consumers, who can benefit from more innovative and tailored products and services adapted to their needs, and for companies able to develop new businesses.

“Data analytics” more commonly called “big data” describe the volumes of data provided by consumers, generated by different business activities and customer behaviour, as well as data collected from new sources such as the social media. Some of these are personal data. If so, they are usually aggregated anonymously or pseudo-anonymised or based on informed consumer consent. Data analytics contribute widely to a better internal understanding of the bank’s activities, a more effective risk management, and an improved monitoring of compliance. They can also contribute to building a stimulating customer experience. This said, banks still face a number of challenges in the technical implementation of data analytics.

Opportunities for banks and customers

The use of data analytics has many advantages from a customer experience point of view: the data collected, based on customer’s informed consent (when required) will improve the understanding of customer’s needs, the quality of products and services and facilitate the development of personalised offers in real time. Consumers will, for instance, be able to benefit from more flexible offers for loan rates’ or a simplified and faster approval of their loan’s request due to a better assessment of the risk profile. Data analytics also offer opportunities to identify potential warning signs in terms of fraud or creditworthiness assessment. Thus, data analytics will create personalised offers for customers and avoid over-marketing of products not needed.
Given the changes in society and the use of social media, the new generations of customers arrive with fresh expectations. They expect banks to take into account the data, already at their disposal, when offering services. Customers are increasingly willing to accept the sharing of data and are inclined to forego privacy either in exchange for more tailor-made products and services, or, for instant access to them. Importantly, consumers expect banks to be able to deal with financial data in a highly confidential and trustworthy manner. Data analytics, generally, contribute positively to maintaining trust, transparency and security.

Banks have a longstanding expertise in dealing with trust, confidentiality and IT security. This ability potentially distinguishes the bank in the services it offers from the new entrants on to the market. Trust in banking services remains a priority for all consumers who seek, at the same time, to take full advantage of the opportunities offered by the new banking environment. Data analytics can contribute positively to maintaining trust, transparency and security.

The use of Big Data in the banking sector is also attractive from a business point of view: it will develop the performance of banks, banking techniques, such as credit analysis, and create new business opportunities. Data analytics constitute a key tool to understanding a bank’s business and activities more thoroughly. It also contributes to more efficient risk management and compliance. For instance, the tool can be used to monitor and develop financial performances and the risk profile of banks. The use of data analytics represents a competitive advantage that allows banks to run their business more efficiently and at a lower cost. Data analytics will enable banks to adapt to new digital consumer expectations and thus reduce inappropriate marketing expenditure, avoid the development of unnecessary product and services offerings, and focus more effectively on their capacity to innovate for the good of society and its stakeholders.

What is big data analytics?


Big data analytics is the process of examining large data sets containing a variety of data types (‘Big Data’) to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to better customer service, improved operational efficiency, more effective marketing, new revenue opportunities, competitive advantages over rival organisations and other business benefits.

Source: Techtarget - Essential Guide

Barriers to the benefit of data analytics

Data ownership: it might be difficult to identify the legal owner of the data collected as this could depend on where the data comes from, how it is archived, and whether it is linked to intellectual property rights or data protection requirements. It should be noted that big data has no value in itself, it is the algorithm and analytic ability of the bank which produces the end value. Consequently, it depends on the cost and time invested in the collection, organisation and accessibility of the data, as well as the necessary IT infrastructure and cloud-based technologies needed to store, process and analyse it. What is more, the customer who gives his/her informed consent, has the right to access, delete or modify the content of the data stored by the data processor. The EBF underlines the necessity for a common data standard to be shared by all European financial services providers.

Cloud computing challenges: the financial industry is still in the early stages of cloud adoption due to specific important concerns over security. For the banking sector, public breach notification, security incident, data security, malware and hacking are considered critical risks to be avoided. Uncertainty regarding liability issues also need to be clarified. To be noted too, are the requirements imposed by financial industry regulators with regard to outsourcing e.g. cloud computing for audit. Likewise, the international dimension of cloud computing should be taken into account. We observe a lack of level playing field in the storage of data via cloud computing: EU players face certain geolocalisaton and data privacy restrictions whereas US players do not and are able to use data stored on the cloud all over the word.

Pseudo anonymisation or anonymisation of personal data for analytics or processing in cloud computing is more costly, time consuming and complex as data must be pseudo anonymise in the private cloud of the bank´s data center, then uploaded to the public cloud for processing and finally downloaded back to the private cloud for reidentification of the personal data to analyse and propose the necessary services or products. Personal data of European customers have to be geolocalise in European territory. This creates extra burdens and costs to European companies in comparison to players outside Europe that don´t have to comply with the same rules (EU Data Protection Regulation) and hence an unlevel playing field.

To be noted, are the ongoing discussions on the proposal for an EU Data Protection Regulation on a possible restrictive definition of profiling which may limit considerably the possibilities for banks to know their customers better, to conduct creditworthiness assessment, and to prevent fraud. A clear standard that makes the user consent process more transparent, quick and easy for user profiling and analytics, should be supported.

Employees with the right skills and competencies: “handling” and “processing” data rely on innovative solutions involving technological, analytical and interpretation of the data. This requires highly qualified employees (data strategists, engineers, statisticians, data analysts etc.) who need to develop specific analytical skills to deal with complex big data management systems.

Lack of harmonisation among supervisors: it is difficult for banks to benefit fully from the data analytics opportunities as they are subject to specific supervision, whereas businesses not falling under the supervision of financial regulators possess their client’s financial data (e.g. social networks). The European Central Bank has not yet clarified the rules for the use of data analytics by supervised banks and we observe that some national regulators appear to be more liberal than others. Furthermore, there seems to be divergent expectations as to how banks need to deal with data security and data breaches. Hence, a sectoral harmonisation should be favoured.

Legacy of banks’ IT systems’ infrastructure: the growing volume, variety and velocity of data needs to be connected throughout organisations and departments in order to give “ready access” to products and customer information. Some banks may still be working on partly decentralised or fragmented systems. It is important to ensure that banks take full advantage of their infrastructure to share and benefit from internal data across their organisation. Thus, banks should adapt their IT systems according to the expectations of data-driven customers.

What is Pseudo-anonymisation?


“Pseudo-anonymisation” is usually used in the context of profiling and consists in replacing the “most identifying” data by a unique number or name to ensure the data record is “less identifying”, to avoid potential concerns regarding data sharing and data retention. This process is different from “anonymisation” as the latter will not allow a reverse compilation. It can irreversibly prevent identification of the data subject.