New technologies such as predictive and advanced analytics, machine learning and AI are already influencing almost every part of the financial sector. Wealth managers will need to follow – and capitalize on – current trends and move towards personalization by offering clients a service that is specifically tailored to their preferences.
Advanced analytics is key for providing smart, personalized products and services. But AI is not only about increasing revenue; it also helps to achieve back-office efficiency. These new technologies are increasing relationship manager (RM) effectiveness, using targeted pricing and strengthening operational efficiency.
The use of advanced analytics techniques has already enabled a more client-centric approach for fintechs, which have managed to design versatile business models with a real, data-centric focus to target the profitable business lines of traditional banking.
To compete with new market entrants, banks will be required to enhance their analytics capabilities and set up the necessary infrastructure to process all their data. Traditionally, this data is stored in siloed systems and used within specific business sectors and jurisdictions. But, while obtaining a holistic view of their clients and developing the ability to integrate and combine many types of data are essential, realizing it from siloed systems poses a significant challenge. When done properly, however, data integration can lead to significantly increased efficiency, business growth and cost and risk reduction.
A healthy client relationship, which is enabled and enhanced via digital services and experiences, is key for every bank to differentiate itself from its competitors. Advanced analytics will help RMs to better engage with clients with smart alerts and individual product recommendations. These services will follow a human-centric design and will be fully embedded and designed together with new engagement and wealth management platforms.