The use of artificial intelligence in finance is heading towards a promising future. Now more than ever, providers are seeking more depth in the knowledge of their customers, and the use of artificial intelligence has the potential to garner such comprehensive data.
An advocate for platforms and Open Finance, Vincent Pugliese is Senior Vice President and General Manager, Platform, at Finastra. He supports Finastra in building an open platform that accelerates collaboration and innovation in financial services, creating better experiences for people, businesses, and communities.
Here, Vincent focuses on how a platform-based approach and the use of open APIs can help banks leverage data, innovate more quickly and leverage Artificial Intelligence (AI) and machine learning (ML) technologies to deliver a more personalised service to customers.
It’s evident from Finastra’s recent ‘Financial Services: State of the Nation survey 2021’ that global financial institutions are keen to invest in technology for competitive advantage. In our research, 85% of financial institutions globally agreed that technology and integration should be at the forefront of the financial services industry.
In addition, 78% of respondents confirmed they are looking to use APIs to drive or enable Open Banking over the next 12 months, with 36% already having APIs or in the process of activating.
Having open APIs, cloud, and platform technology in place not only positions financial institutions to derive maximum benefits from Open Banking, but also to prepare for the evolution towards Open Finance, including the impact of new business models such as Banking as a Service.
Our survey showed that Banking as a Service and mobile banking are two areas in which financial institutions plan to improve or deploy technology over the coming year. The other notable priority is artificial intelligence.
Use Cases for AI and Anonymized Data
Before financial institutions can start reaping the benefits of AI, they need to explore the data available to them and how it can best be accessed and managed. Integrating with platform providers and fintechs through open APIs makes it much easier for financial institutions to access and share data, enabling them to leverage the data for innovation. A platform-based approach also makes it easier for financial institutions to access data analysis tools from partners.
Financial institutions can gain a lot of value from anonymized data, for example, using AI and machine learning to analyse data and detect patterns to help tackle specific business problems. Anonymized third-party data can be used by financial institutions to compare their performance in specific areas against peers and use the insights gained to improve business performance.
Being able to aggregate anonymized data across institutions builds a much more accurate picture of where processes are succeeding or failing. The data cannot be associated with individual customers so there is no operational or legislative risk. For example, dynamic peer group benchmarking enables financial institutions to compare their performance across key metrics with their most similar peers, determined by a machine learning model.
The successful aggregation and analysis of masses of data will give financial institutions a deeper and broader view of performance and help them to understand how customers are interacting across channels. It can provide insights and a better understanding of customer engagement, including any points where they become disengaged or where bottlenecks occur.
It can also help financial institutions better understand customer segmentation and how best to target different individuals. With the appropriate customer consent in place, anonymized data can be subsequently re-identified by the financial institution and connected back to the customer to make specific recommendations.
Financial institutions can also use their own aggregated data to provide better results. For example, by identifying the customer behaviour that precedes churn, financial institutions can spot the warning signs and intervene before a customer switches to a competitor.
In a traditional model, a bank would offer a customer improved terms only once they revealed that the customer had plans to leave. But imagine being able to reach out to a customer as soon as they start to feel dissatisfied. A churn model can do just that: examining hundreds of different data points, from the frequency of online logins to transaction volume, to spot when engagement is falling so that corrective action can be taken.
The Journey to Open Finance
There’s really no need for financial institutions to invent everything in-house anymore. Not when it’s easy to work with partners to leverage the latest AI and ML tools to gain greater insight into meeting customer needs.
It’s exciting to see how the acceleration in the adoption of cloud, platforms, and open APIs over the last 18 months is already opening a world of opportunities to financial institutions. The ability to collaborate more easily with third parties, share data, innovate faster, and use artificial intelligence and machine learning to improve the customer experience will be instrumental in enabling the industry to start delivering on the vision of Open Finance for the benefit of all.