How are banks embracing AI?
Artificial intelligence (AI) is widely regarded as one of the most important technology trends for banks and financial institutions to adopt in the near future.
Banks are increasingly getting in on the game with technologies such as customer-facing chatbots, robotic process automation and predictive data analysis becoming commonplace.
Eleanor O'Neill takes a look at some of the ways AI is being adopted by the banking sector.
Four ways banks are using AI
1. Predictive analysis
Banks are increasingly using big data to analyse and predict consumer behaviour.
Consultancy firm McKinsey cited the case of a bank that used local telecommunications data to improve their underwriting. The behavioural trends extracted from the data acted as a predictive indicator of how consumers would handle credit with the bank.
The American Express Company also claims that, by employing sophisticated predictive models in place of traditional business intelligence-based hindsight reporting, they are able to predict 24% of accounts which will close within four months.
A survey of banking executives, carried out by Accenture, showed that 76% of respondents believe AI interfaces will become their primary point of contact with customers within the next three years.
'Luvo' is an online virtual assistant designed using technology from IBM's Watson AI system. It was recently rolled out by the Royal Bank of Scotland (RBS) and NatWest to interact with customers and handle simple problems via a web chat tool.
Jane Howard, Head of Personal Banking at RBS, reported: "Luvo frees advisors from spending time on simple, easily-addressed queries so they can help customers with more complex issues and questions - improving the experience of both parties."
3. Voice recognition
Santander and HSBC both launched voice banking technology on their mobile apps in collaboration with Nuance Communications last year.
The systems are intended as an additional layer of biometric security for customers and as a management tool for their finances. By analysing over 100 factors, including speed, cadence and pronunciation, individuals can make payments, report lost cards, set up account alerts and answer questions about spending.
For several years, Barclays Wealth customers have been able to identify themselves on phone calls with voice recognition software in place of passwords.
4. Recommendation engines
Personalising the customer's journey is a highly effective way of improving their experience and satisfaction, showcasing key initiatives and driving traffic numbers.
Recommendation engines utilise data science and machine-learning to offer suggestions for related products, new offers and other 'discovery' paths.
Retail sites and search engines are prime users of this kind of software but the advertising opportunities lend themselves to every sector.
There are inherent advantages in the adoption of AI. For example, more efficient processing, heightened accuracy of data, and regular automated analysis.
For banks, in particular, cognitive-based technology solutions benefit fraud detection through modern software which is able to respond in real-time to aberrations in spending patterns, and constantly evolving challenger models respond to new threats and risks.
In terms of added value for the customer, AI allows for the implementation of personalised 'robo-advisers' that produce automated algorithm-based reports and recommendations on wealth and portfolio management.
Through this, communications and offers are increasingly targeted and refined to relevant audiences, allowing organisations to make gains in productivity.
Banking is set to continue to see an increasing adoption of AI as the average consumer becomes more comfortable and ultimately expectant of the services AI can provide.