Frictionless banking is all about providing a convenient and seamless way to access money and services and to conduct financial transactions, all in the name of improving the customer experience. Indeed, “removing friction from the customer journey” is the number-one priority for financial services organisations, according to recent research.
Now that banking customers are demanding better digital services and innovative fintech companies (including virtual banks like ePayments) are offering an alternative, frictionless banking is gaining greater traction.
Frictionless banking requires straightforward and hassle-free authentication, with biometrics becoming an increasingly powerful means of achieving this. But there also needs to be some limitations, as there are numerous regulations to contend with, and some transactions that require more than a thumbprint to be sufficiently secure.
Bearing this in mind, we’ve taken a look at the technology and initiatives that have the potential to advance the cause of frictionless banking further in the coming years.
The potential of APIs
In the UK, the Open Banking initiative is aimed at opening the way for new products and services to help customers get a better deal, as well as help them make the most of their money. It also has potential to reduce friction in some areas of banking.
For example, alternative loan platform Zopa plans to use APIs to reduce friction in the loan applications process by automatically populating much of the applicant’s details. It will also use these APIs to make better lending decisions – by replacing credit scoring with new technology built from new datasets made available via Open Banking.
A greater understanding of the different use cases and value they can bring will lead to more innovation, according to Hiroki Takeuchi, cofounder and CEO of payments provider GoCardless.
For example, GoCardless is looking at using APIs to check whether a payer has sufficient funds, helping customers and businesses avoid failed-payment fees.
New approaches to personalisation
Personalisation of the banking experience for consumers is another aspect of providing a frictionless experience. By ensuring customer preferences are already in place – and that they can be easily changed – managing of finances becomes more straightforward.
For example, Poland’s Alior Bank was recognised by the Efma and Accenture's innovation awards programme for its current account that enables account holders to select and change the benefits they want through the online banking portal or mobile app.
Among the 30 benefits customers can opt in or out of are free withdrawals from ATMs, free instant transfers, a higher interest rate on savings accounts, or a short-term interest-free overdraft facility. Customers can also choose to be members of a loyalty programme, enrol in the bank’s travel insurance or add emergency assistance for car, medical or home insurance.
Artificial intelligence and advanced analytics are going to play an increasingly important role in reducing friction in banking. Their main uses are likely to be in several key areas: mortgages, wealth maximisation, fraud detection, and complaints and customer services.
In mortgages, selection/payment/settlement could see an experience built around having a new house, as opposed to the lengthy mortgage application process. AI could allow lenders to measure risk and calculate credit scores in advance so the application is virtually complete before the customer has to input anything. Customers could then have a range of provisional quotes to choose from, before they enter the formal applications phase.
AI could also be applied to wealth generation, by optimising income, savings and taxation to boost total wealth. Customers could set the system up to achieve an objective for the end of the year, monitor what’s needed and provide advice – for example, how spending patterns could be improved.
Fraud detection is already benefiting from the use of AI with real-time checks on fraud patterns. Decisions on whether fraud is taking place are based on historical patterns, supporting a much quicker response to activities that require investigation.
This emerging form of analytics could identify when a customer is going to complain, highlighting internal issues and allowing organisations to be aware of or fix issues before they’re flagged by a customer. This would help mitigate account closures due to dissatisfied customers.
With mortgages, machine learning could be trained with behavioural patterns from the historical customer loan book, including information on customers who have defaulted. Banks could then recognise similar behaviours before anything untoward takes place and take preventative measures, such as engaging with the customer to enact solutions.
Analytics could also be used to give call operatives guidance on how to deal with a customer in response to their personality or emotional state.