Card fraud has increased 19% year on year, according to The Nilson Report, accounting for losses of around$21.8 billion, in 2015. France has seen an 8.9% increase in card fraud and the USA, which has the largest fraud/loss ratio, currently accounts for 47.3% of the world’s payment card fraud losses.
The threat to banking is at least in part due to the explosion of data, according to Sopra Banking. It is expected that by 2020 we will be creating more than 44 times the data we created in 2009 - and that fraud will have resulted in losses of $32.8 billion. The storage and transmission of so much offers opportunities for fraud and cybercrime as well as being part of the problem.
The Evolution of Fraud Management
Ensuring that customer protection is paramount, whilst also preventing normal transactions from being interrupted is a fine balancing act for banks. The evolution in handling fraud management can be conducted in a more intelligent manner using big data - or ‘dataprints’.
Alike fingerprints, dataprints give us unique information about a given person, action, place and point in time. Analysing these accurate identifications (transactions, devices, usual patterns) through Artificial Intelligence, provides a warning sign of fraud for banks and customers.
Analyst firm McKinsey in their look at disruptive technologies, predict that neural networks will utilize big data to enable “knowledge work automation”. Learning and applying new and more refined algorithms improves the process’s sophistication and capabilities, making it easier to make data-driven decisions to detect fraud.
It’s all very well to say that data and technology can help prevent fraud - but what does this look like in practice, and how can banks achieve this?
Collection and Centralization of Internal Data
It is necessary to devise ways of collecting and storing big data in a manner that allows you to take full advantage of it when you need it - but also keep it secure.
Normally, data is created and held in silos, in a division/department/business area/type manner and because of this delocalization, it ends up being difficult to collate, distribute and utilize in any sort of global way. Centralizing the collection and management of data means that you can more easily access the data and cross-reference it.
A July 2014 survey of bank respondents by The Economist, found that half had applied centralized analytics to big data management through artificial intelligence software. In turn, these banks had the most holistic approach to risk mitigation and fraud prevention and enhanced their security as a result. It is something the industry needs in order to fight fraud in 2017 and onwards.
The centralization of data and in turn creation of intelligent big data will enable banks to not only mitigate fraud, but service their audience better. The implementation of big data centralization is as much a process as a system and requires synthesis of legal and regulatory compliance, a security and privacy focus, strong management and the best technology.
Leveraging External Data
Big data means information from multiple and often highly disparate sources. One of the new challenges for data collection have arrived in the form of social media platforms like Facebook and LinkedIn. However, external data tracking, can be an extremely useful tool in the fight against fraud.
Analyst firm, McKinsey has shown that the use of external data, such as social media activities, can have up to 35% improvement in areas such as risk mitigation, as well as allowing the development of better insights into customer behaviour and ultimately in fraud behaviour analysis. One of the reasons for lack of uptake in this area is the difficulty of retrieval of such data. Although this is certainly achievable in terms of technology through the use of social graph APIs. However, the consent and release of this data is often a legal minefield and customer privacy worries and media scares themselves can be a hurdle to jump.
Going forward into an era of instant payments, external data tracking that is conducted in a privacy enhanced manner will become even more important. The ability to keep track of these payments, whilst ensuring personal data is obfuscated, all in real-time is a challenging but ultimately empowering new tool for the industry.
Using Behavioural Profiles to Prevent Fraud
Big data is revolutionizing the process of ‘Know Your Customer’ or KYC. As KYC becomes KYCd, or Know Your Customer’s data, a more accurate and in-depth approach to consumer understanding can be rewarded by more impactful anti-money laundering (AML) and other types of fraud detection.
Being able to model patterns of behaviour by using predictions based on internal, external and social big data is transforming banking. It not only gives you insight into normal behaviour, but that baseline then allows comparison and identication of patterns, similarities and differences - and fraud. Technologies such as geolocation, can be added to the arsenal, so those incidents when a customer is interrupted from making a legitimate purchase are greatly reduced, whilst real crime is detected.
However, it can also offer challenges in terms of security and privacy. Customers are now more informed about privacy considerations and have become less happy about sharing their personal data with any company, not just a bank. Sopra Banking Software report found that 80% of customers would be willing to share their personal data, as long as they did so using a consented, ‘opt-in’, approach and in doing so they were incentivized by better rates and so on.
New EU privacy and data protection laws, which are an adaptation of the Data Protection EU Directive 95/46/EC, are due to be finalized this year. The new data privacy laws will be more restrictive and will have focus on, for example, data stored in the Cloud. This requires a Privacy by Design (PbD) approach when creating Cloud based systems, especially those that store, transmit and transact data. Handling these more extensive regulations needs a more rethink in the approach to security and privacy.
Although the collection of data, how to centralize and manage it, how to make it safe and how best to analyse and make predictions from it are all challenges, they also offer huge potential. The digital revolution that has brought us big data can also bring us big banking.