Customer Confidence in Banking – Tips on Using Big Data Analytics to Rebuild It

Customer Confidence in Banking – Tips on Using Big Data Analytics to Rebuild It

Dominic Vincent Ligot

Industry Consultant at Teradata

Views 426

Customer Confidence in Banking – Tips on Using Big Data Analytics to Rebuild It

27.10.2016 01:30 pm

The world’s financial infrastructure is collapsing. So they say.

It’s a theme that is oft-repeated. You only have to log on or open your newspaper and stories about problems in the banking world come thick and fast. Fraud, the spats about Brexit, looming financial disasters and misdeeds – it all comes rolling out, spiced up with rumour and gossip, colouring the public idea of the role of banks.

Hardly a surprise then, that despite the industry’s admirable growth and the central roll it has played in society for hundreds of years, the public regards the banking community with suspicion. Issues like security breaches, poor customer service and lack of service evolution all figure heavily in the public mind, while those who work within banking look to the stars and do their best to allay such concerns.

Rebuilding confidence

To win back customer confidence and forge a successful path in the face of unparalleled digital disruption, individual banks (and the whole industry) need to look hard at their long-established business models and operational practices. A few banks have already started out on the digital transformation journey – embracing new technologies and tapping into existing data resources to create improved products and services. Big Data and analytics are the key to all this, but their full potential still remains largely unrealised. Banks now need to take practical steps towards creating data-driven business opportunities from the areas where the public has the wrong impression.

Payments data

Start with the most under-appreciated dataset. Payments reveal a great deal about each user – how much they’ve paid, what they paid for, who was paid, the banks involved, transaction time and location, and so on. In fact, a customer’s payment profile says much more about her, or him, than any social media metric or record. Payments data is highly accessible and can pinpoint lifestyles, detect which companies make up a supply chain, and plot spending trends by time or place. At the same time, although customer data is not as dynamic as payments data, in banking systems it can be attached to other profiles such as payments and credit history to enhance analytics and create successful “Next-Best-Offers”.

Fintech sensibilities

Should banks be worried about the Fintech boom? Not necessarily. Banks have both the resources and the ability to retain their position in a way that start-ups really don’t. They just need to adopt a bit of Fintech thinking. Banks can try some of these simple and practical things in the short term that could make a significant difference:

  1. Play with some data around a recommendation engine – It can be done as an experiment with a few people. Group customers by preference, products by customer, and transactions by pattern similarity. Everyone’s always looking for the elusive ‘Single Customer View’, but guess what? A ‘Partial Customer View’ linking two to three product portfolios is already enough to get started.
  2. Look closer at payment and behaviour data – Payments can help banks understand the sequence of events that leads to somebody leaving the bank. Payments can reveal hidden social networks within a bank’s portfolio. Customer-to-customer, customer-to-merchant, company-to-company, product-to-product – what could you do if you knew these relationships?
  3. Fraud and compliance – As mentioned before, banks are incredibly adept at regulatory compliance and fraud mitigation. But the industry needs to start getting better at text analytics and using web behaviour to detect high-risk patterns. Insights such as ‘who clicked on what before fraud happened’ can be very enlightening. These days, companies can match weblog data with branch data and check the difference between web and in-branch behaviour.
  4. Service experience – In the brick-and-mortar era it was ‘Location, Location, Location’. Now, in the digital era it’s ‘Customer, Customer, Customer’. Use event data to spot processes that are causing problems for your customers and fix them. Contact Centre logs are a hidden source of insight. It doesn’t take much to parse them for sentiment and recurring patterns. There could be new products hiding behind these complaint logs, if only banks were inclined to look.
  5. Improve the mobile experience – Many banks have mobile apps but they usually concentrate on facilitating payments, fund transfers, and account management. What if a local bank’s app could act like Mint and provide the user with cool ways to manage budgets, see financial profiles at a glance, and even offer helpful advice? You can parse those mobile servers for hidden patterns in data (location profiles, IP addresses, mobile browsing, etc) – the ‘fingerprints’ of customer satisfaction.

Okay, these five things won’t turnaround troubled relationships on their own but they could be the first, tentative, steps towards reconciliation.

And once the ‘relevance’ and ‘confidence’ fences have been mended and an enterprise-wide digital transformation strategy embedded, banks can get back to developing meaningful, long-term, data-driven customer relationships instead of settling for a diminishing series of ad hoc, one-night stands.

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