As the commercial banking sector becomes increasingly crowded with new market entrants, how do banks compete with their new agile, customer-centric competitors?
Customers are becoming more aware of the power, and the value of their data, at a time when competition between players is driving transient banking relationships between banks and the customer. An emergence of unique products and services have proliferated, whereby banks are gaining a better understanding of the customer, their online and payments behaviours, and their household preferences.
However, as part of this, the volume of data collected from multiple customer touchpoints – including, social media, contact centres, ATMs, mobile payments, mobile banking, branch, credit cards and loans – and the frequency at which it’s collected, means that the data types are more contrasting, and its analysis inevitably becomes more complex as a result.
Addressing key challenges in big data application
As is typical with financial services institutions, the current architecture does not match the newer technology, meaning that traditional enterprise architectures are not as adept as big data applications at analysing risk, marrying data sets, and processing data from different external sources.
Integrating customer data from external sources requires the use of identity matching software that seamlessly connect an individual appearing on, for example, Facebook, versus the bank’s records. In order to offer truly targeted and real-time offers, transaction data needs to be integrated from multiple channels, in real-time to meet demand, coupled with the appropriate technology equipped with decision-making software. For example, credit scoring analyses loan application data against credit bureau data, which, in emerging markets is often scarce. The predictive modelling capabilities of big data platforms can fill in the gaps with non-traditional data types, such as, P2P transactional data from mobiles, utility bill consumption, pre-paid mobile contracts and purchase data simulations. Big data innovation allows all of these scenarios and models to be played out, at a relatively low cost.
Evidently, the General Data Protection Regulation (GDPR) is causing concern for the sector. As regulators and governments become more worried about data security and privacy, the growing unease is that consumers could follow suit. Some customers’ distrust and resentment of banks, and their data usage in general, could drive a knee-jerk reaction towards maintaining privacy over their personal data, and as of May, they will be quite within their right to do so.
How are they surmountable?
Big data can be key to many industry data woes. As the rate of data increases, the business analysts require dashboards in order to rapidly combine data sets, generating more established thought and deeper insights. Big data allows for the evaluation of multiple data types in one platform, in larger volumes, and at higher rates. The Centre for Economic and Business Research (CEBR) forecasts that big data will generate more than £26 billion in additional revenues for the UK banking sector alone between 2015 and 2020, plus additional economic benefits of up to £4 billion for insurers. Therefore, despite concern in the banking sector linked to the speed of technological change – with 81% of CEOs being worried by this – and although a challenge, its implementation should certainly be addressed head on, and not pushed down the priority list.
Additionally, banks could take inspiration from other industries and banking players who are using big data well. Leading with a ‘customer first’ approach, are fintech start-ups, that are truly investing in AI, big data, cloud computing and Blockchain as a priority, and will transform the sector as a result. With 25% of the global population already using some sort of fintech innovation, and $40 billion’s worth of funding secured by fintech start-ups since 2015, they present a genuine example for technology and customer experience strategy for the industry.