Artificial intelligence is at the center of the future business strategy of banks and insurance companies.
One only has to look at the Digital Trends 2022 data from Cetif to see how leveraging data is high on the list of priorities: 83.3 percent of the entities surveyed report "AI for Business" as one of the most interesting trends between now and 2025. That is, precisely the integration of artificial intelligence into their business strategies.
Beyond omnichanneling
Strategies pointing to the concept of opti-channel, which is evolving that of omnichannel and introducing the winning idea of "a right channel at the right mo-ment." Opti-channel development is reported as a trend to 2025 by 82.6 percent of the sample, while 60.4 percent point to it as one of the strong themes as early as 2022.
"Among banks and as-insurance companies," explains Paolo Gatelli, Senior Research Manager at Cetif , "there is now a widespread awareness that there is a need to move from a data-driven to an insight-driven model-lo. And this is a sign of greater maturity in the use of data and artificial in-telligence. The great mag- majority of finance realities have equipped themselves for simple use cases that suggest the best next action, for example, to reduce churn or to send a notification to the customer when a certain condition veri-ficates. Now it is time to move to an insight-driven model, in which we want to com-prehend the passions, habits, and emotions that lead a customer to search for a product or service. Analyzing his profile to indivi-duate the right channel and time to contact him."
Insight-driven emotion
And precisely insight-driven engagement is reported as an ongoing trend by an additional 58.3 percent of the cam-pion. In short, work is being done to understand the customer in different dimensions, further personalizing the relationship. "A non-trivial concept to understand is that of emotion," Gatelli continues, "which in the financial sector can be surprising. I don't have to go looking for an aspect related to the sphere of feelings in a mortgage, for example, but in the context of the customer's life to which that product-to is connected. The person applying for a mortgage is fulfilling a family project, pursuing a dream. He or she is taking an important step, which requires an empathetic relationship logic, supported by information about the customer's needs. Today few banks and companies have reached this point, but the goal is mapped out."
A multi-dimensional customer
And this goal requires consi-dering the customer as a whole. Thus, clustering the clientele not only in a more punctual way, but also in a multi-dimensional way. "Each individual has characteristics that unite him or her with some people, but differentiate him or her from others," comments Gatelli- That is why we talk about Multi-personas. The client must be analyzed under different behavior and needs profiles, which consider all facets of the client. With these insights I can co-construct a personalized and effective relationship. The simplest and most trivial example is the clien- t's predisposition to be contacted on certain channels at different times of the day, based on whether they are at home or at work."
Insights show the way
The customer becomes a multi-dimensional character, with whom to interact according to dynamic rules. "Thanks to these insights, the bank can move from a pull to a push lo-gic," Gatelli continues, "contacting the customer directly with the most appropriate channel. It can send an app notification for a simple need, or signal an oppor-tunity to the branch or advisor network in case of a more sophisticated need. But a lot depends on what the bank can intuit with respect to the customer's profile: it is the insights processed by artificial intelligence that tell which channel, product, and time of interaction is best."
Enriching data
The banking and insurance sectors are believed to be among those with the most customer information-a veritable treasure trove of data on transactions, assets owned, family members. "Actually, even financial companies can be- benefit from data enrichment actions," notes Gatelli, "first of all to get to know their 'historical' customers better. But if they are acquiring new customers, or if they are recent challengers to the market, the information set available is, generally, quite small. There are many ways to enrich it: one can develop part-nerships and ecosystems with third parties; or focus on open banking and access to current accounts at al- three entities. Also, a lot of useful information can come from digi-tal channels, through so-called digital footprints: I'm thinking of geolocation or tracking of subscribed services."
Insights for private banking
The insight-driven model can be extended, with appropriate adjustments, to all customer segments. "Insights can of course be triggered for private clients as well, for example," Gatelli specifies, "although, in many cases, they will cover such important aspects that it would be illogical to handle them with a no-tification or an email. For private banking, artificial intelligence can generate deep insights to support the contact and advisory activ- ity conducted by the banker. In fact, AI can do even more, which is to help the bank discover so-called hidden affluent or hidden pri-vate. Customers that the bank knows little about and fails to place in the right segment: by analyzing data about a customer's behavior and assets, it is possible to give back the right dimension to an individual and strengthen his or her relationship with the bank."