The combination of data usage and analytics will enable banks to fine-tune their processes and customize their products to deliver tailored experiences.
It’s a fact: consumer expectations aren’t the same as they used to be. From the speed at which groceries arrive from the supermarket to how an algorithm can recommend music matching a playlist. Users expect different things from the companies they interact with in their everyday lives.
The same applies to banks. This is why organizations are rethinking—and should be rethinking—how to complement or replace traditional practices with predictive models that help them better serve their customers.
By leveraging a combination of tools such as Artificial Intelligence (AI), automation and data science, institutions have the ability to become smart banks.
In these digital times, banking must be an effective point of entry and usage for customers, driven by the desire to learn from them and provide them with answers in a more unique and powerful way than the competition.
To achieve this, data—the basic but essential unit of customer information—remains at the forefront of the strategy.
But the data won’t bear fruit if it’s used in isolation or left to grow old on bank servers. It must be constantly analyzed and managed in order to equip the institution with the necessary skills for action.
The focal point of this perceptive movement is the creation of satisfying navigation experiences, born from processing large lakes of information and making decisions using intelligent software. This allows robots to automate processes and respond to specific requests if they have the necessary information to execute these actions.
Likewise, this intelligence can’t rest on static information. On the contrary, it must be day-to-day, allowing this personalization of the experience to be executed at the most appropriate moment for the user and to evolve with them.
The driving force behind an intelligent organization is the data analysis that allows us to improve our decision making and, therefore, be truly useful to our customers at the moment they need it. However, this analysis won’t be effective if it is not translated into products that deliver an insightful experience.
Digital payments are one of the features that currently need more work, but a solution on its own doesn’t add any value to the user.
However, say, for example, we use innovation and automated learning tools to create alerts for when unusual or bigger-than-normal payments are made, among other capabilities. In this case we are adding value for the user in two ways: so that they don’t exceed their monthly budget and to alert them of any overcharges during a payment.
Another of the features gaining relevance within smart finance is the addition of a wealth management option but with chat features or contact with advisors in real time. These are among the solutions most sought by users, according to research by data analytics consultancy JD Power.
This is an area of enormous potential because, according to the same report, few wealth management applications provide the level of interaction with advisors that clients are seeking.
Automation and AI are also aiding the fight against financial crime. Customer data must be properly protected against third-party interference, so it is becoming increasingly important to encrypt the connection and control access to sensitive information.
A smart bank must authenticate with the appropriate combination of user security factors before disclosing sensitive information.
In this regard, it is important to design apps and other digital channels in such a way that, if the device is lost or stolen, the risk to customers and the bank is minimal. One of the solutions gaining momentum is biometric security, such as fingerprint and facial recognition.
In addition, the use and recognition of usage patterns with AI to trigger alerts in case of irregularities.
For smart banking, AI and data analytics play a role in not only improving the user experience but also increasing customer retention, thereby boosting profitability.
The combination of both tools allows banks to generate not only new information but also business opportunities, since they acquire a structured knowledge base that integrates complex data and its subsequent analysis, thanks to automated and deep learning.
AI is an increasingly established proposition in the financial industry. A Capgemini study shows that 85% of banking executives believe that AI—and AI-driven automation—will be their top technology priority in the next 12 months.
These new technologies enhance the capacity for intelligent banking by generating learning based on the capture of more relevant information from users, which allows the financial institution to obtain a more refined and valuable understanding of its customers.
This not only improves the bank’s offering with customized and useful products and services but also boosts internal productivity by automating cumbersome processes.
The result is a response that’s geared to the needs of each profile, meeting the new digital expectations and positioning the bank as a financial ally that provides information tailored to the context and daily life of the customer.
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