Fintechs and neobanks pose a huge threat to traditional banks. They offer innovative financial services based on new technologies, and it can seem like they’re always a step ahead with digital customer experience.
We know already that customers expect around the clock, fast, convenient and digitally enabled banking.
The Mobile Ecosystem Forum’s 2017 Mobile Money Report found 61 per cent of respondents use their mobile phone to bank, with institutions like ANZ leading the way in Australia by adopting Apple Pay.
Looking abroad to Southeast Asia, there are many of people who don’t have bank accounts but do have mobile phones.
Telecommunications companies have encouraged banking to be driven by the user on the phone service, rather than by the bank.
This is a worry for banks as there is a potential for disintermediation, meaning they will not own those customer relationships in the future.
It’s always been crucial for banks to maintain robust relationships with their customers.
Banks can no longer rely on customer loyalty simply because switching to a competitor is too much hassle.
If banks don’t deliver the digitally-transformed services customers want, these new companies will swoop in and take away their business.
As they are digitally transforming banking services from the ground up, disruptive digital banks like WeBank and Digibank are not slowed down by the challenges of legacy systems such as re-integrating vertical silos of data.
So, how do banks set themselves apart from their competitors, ensure their customers remain loyal, and remain profitable in this increasingly saturated and dynamic new market?
There is a clear way forward. Banks handle an almost inconceivable amount of data, and to better engage with their customers — both digitally and face-to-face through branches — they can harness the huge value of that data to drive a positive customer experience.
This is where the algorithmic bank brings in the power of algorithms, data, and data science to drive better decisions to improve customer satisfaction, profits, and to sustain relationships.
Data can help a bank better understand individual consumer needs and preferences and effectively predict customer behaviour.
With Big Data, banks can build profiling programs for each of their customers.
They can determine how profitable each customer is and what motivates them, in turn offering tailored experiences that anticipate their needs.
Big Data and analytics help retail banks implement better processes that are aligned with the new business drivers of the digital economy.
The insights gained allow banks to deliver personalised and timely offers to their banking customers based on their unique understanding of who they are, how they behave and what they want, which in turn builds a deeper and richer relationship with the customer.
Citibank Asia has recently invested in digital transformation, recognising the need to build “systems for remarkable customer experience.”
The core aim of the bank’s transformation is to provide optimised, quality retail banking services on mobile.
Big data and analytics can help make use of all forms of data — even that which a bank doesn’t directly own.
The rise in platforms like Twitter, Facebook and other social mediums has exponentially increased the publicly available data on a bank that is beyond its control.
Within seconds, any given customer can write a tweet, comment or review that can wreak havoc on a brand’s reputation.
Applying analytics to social media can improve a bank’s ability to manage customer service outcomes.
Social network analysis (SNA) takes what is mostly unstructured data, looks at the patterns of the data and makes sense of it.
For example, banks using SNA can look at the people writing negative comments and try and work out where they’ve been and what type of customer they are, with the ultimate goal of fixing the problem.
By recognising patterns and trends in real-time customer conversations and integrating that with existing data about your customers’ behaviours, accounts and preferences, insights from customers can be extracted to inform a bank’s sales and marketing strategies and tactics.
Imagine how valuable a bank’s data will become if collected and analysed regularly.
Systems of insight that work with a bank’s existing people, processes, and technologies, seamlessly adding new digital capabilities including data virtualisation, data science, machine learning, visual analytics, and streaming analytics when necessary, can be used to build predictive models that optimise customer experience.
To guarantee success, banks must be customer-obsessed, agile, develop new products faster and identify opportunities to shift and learn.
The use of algorithmic technologies can ensure each customer is getting service and experience tailored specially to their needs — this is the future of retail banking.
Robert Merlicek is the chief technology officer of TIBCO in Asia Pacific.