By Brendan O’Brien, Co-Founder and Chief Innovation Officer at Aria Systems
In the rapidly evolving landscape of financial services (FS), the integration of artificial intelligence (AI) has become a pivotal factor in staying competitive. The technology is touted to revolutionize the way companies interact with their customers and deliver deeply personalized services. But it is also set to radically improve the optimization of operations, and also help to navigate increasingly complex regulatory landscapes and maintain compliance.
For FS companies, the use cases have been clear for some time now, but their ability to actually make them happen is what will set them apart. At the moment, companies are either taking a DIY approach to AI solutions, or buying white-label products to support individual functions or use cases, or are looking at an approach which is somewhere in between.
The chosen approach, and the associated software, architecture and investment, will differ from organization to organization, but in a fiercely competitive industry, success will be determined by how quickly and how deeply FS companies realize AI gains.
The personalization imperative
In today’s digital age, where consumers are inundated with countless options and information, personalization has emerged as a key differentiator in the finance industry. Through the analysis of vast datasets, AI systems can discern patterns, preferences, and behaviors, enabling financial institutions to offer highly personalized recommendations and services.
In wealth management for example, AI-powered tools can provide tailored investment advice based on a client’s financial goals and risk tolerance, as well as market conditions. In retail banking, AI algorithms can analyze transaction history to suggest customized savings plans or identify potentially fraudulent activities, enhancing both customer satisfaction and security.
The imperative for the financial services industry to embrace AI-driven personalization is rooted in the evolving expectations of today’s consumers. In an era where customers demand seamless, intuitive, and personalized experiences, AI becomes a strategic asset for financial institutions aiming to stay competitive. Not only will personalization foster stronger customer loyalty, it will also contribute to higher customer lifetime value and average revenue per user (ARPU).
By delivering targeted offerings and services, financial institutions can differentiate themselves in a crowded market and launch ever diversified services. But on the flip-side, those not pursuing hyper-personalization risk being outcompeted by more agile counterparts.
Taker, maker or Goldilocks SaaS
In the realm of large-scale FS entities, the prevalent ‘taker’ models, exemplified by general-purpose solutions like chatbots connected via simple APIs, often fall short of delivering the transformative impact desired for customer experiences. These off-the-shelf solutions run the risk of damaging customer relations and pose security concerns. But an alternative, DIY route also has its pitfalls. Doing it yourself can prove to be resource-intensive, and is often marked by stringent testing and validation requirements that impede the pace needed to win the AI race.
The ideal solution, a ‘Goldilocks’ approach for companies seeking to revolutionize their services with AI, lies in the adoption of a composable Software as a Service (SaaS) architecture. A composable, cloud-native architecture, enables companies to plug together various software modules from different best-of-breed vendors, to seamlessly offer services for new lines of business. No customization of software is required, making it affordable for FS companies of all sizes to diversify their offerings and partner with new businesses.
By upgrading to a composable SaaS architecture, FS companies can introduce AI software into the technology stack and enable true personalization throughout the customer lifecycle. From creating and taking an order, to providing guided support throughout the purchase, troubleshooting and upselling services, AI can give customers exactly what they need, faster than ever before.
This approach has proven popular in the enterprise world for some time now, with various cloud-native software modules working together seamlessly, through a series of APIs. And sectors, such as telecoms, with a similar need to safeguard data are also pursuing innovation through this route.
The race to value
In the fast-paced world of finance, it’s clear that using AI will be crucial in helping companies stay ahead. AI’s magic lies in creating personalized experiences that match what today’s customers crave—seamless, intuitive, and uniquely tailored interactions.
But it’s one thing being able to deliver personalized services, and another being able to monetize them. In this push for customer-centricity, companies already serving a range of customers with different service models – from simple subscriptions to complex consumption and bundled packages – will need to be able to implement some form of autonomous billing to capture value from highly flexible enterprise sales and customer self-build packages.
Thankfully, these tools too are also easily available to those adopting the ‘Goldilocks’, composable SaaS approach for their software needs.
