AI will drive hyper-personalization in finance

An opinion piece by the global group head of data and analytics at Envestnet, a unified wealth management platform

June 9, 2022

4 Min Read

An opinion piece by the global group head of data and analytics at Envestnet, a unified wealth management platform

The digital transformation of the banking sector is inevitable, but banks have been slow to adapt to the way consumers are living their lives.

Research shows the average U.S. consumer has five accounts across all types of financial institutions. Consumers are gravitating toward solutions that bring all these different accounts and financial relationships together, but often that’s outside the scope of the traditional bank.

Today, banks are seeing more competition from retailers and tech-savvy companies that put personalization at the center of their business models. Consider Google’s ‘Plex’ product, connected to the Google Pay app, that includes physical and virtual debit cards, peer-to-peer payments, and an associated checking account. Call it what you will – the ‘Netflix’ or ‘Amazon effect’ – but consumers expect how they interact with their banks and other financial institutions to mirror the highly personal, digital experiences and products they enjoy from these and other technology giants.

For financial institutions, this means going far beyond segmenting and microtargeting or even customizing homepage messages and digitizing the customer journey. It requires hyper-personalization, the ability to use data and analytics to develop a deep understanding of each customer’s needs and orchestrate a set of tailored experiences across digital and human channels.

The more information a bank can collect about a consumer − within the confines of evolving and increasingly strict privacy regulation − the easier it is to provide services that are truly relevant to an individual’s specific needs and deliver operational improvements.

Imagine looking for a new home and finding the house of your dreams. What if you could point your phone at the house and talk directly into your banking app? Your bank tells you not only how much your monthly mortgage payments would be but also provides details about local services and taxes by drawing from public information. All the information you need to make a complex financial decision, like buying a house, could be available all in one place on your banking app. Sound farfetched? We’re not there yet, but this is the direction the industry is headed.

In 2022, AI will be harnessed in finance to create even better hyper-personalized and unified customer experiences, inching us closer to that home-buying example. Advances in technology will enable financial institutions to gain a deeper understanding of their customers – their preferences, interests and where they’re at in their life journey. And AI technology allows this to happen at scale, something that wasn’t possible in the days when loans were made on a handshake.

Hyper-personalization benefits

The benefits for consumers are immense, putting them on a financial wellness journey to become better educated about how their daily financial decisions impact their long-term financial goals. This model also offers numerous benefits for financial institutions:

  • They can segment product offerings by market audience and distribute them as part of an integrated, hyper-personalized omnichannel experience for customers.

  • Given ongoing regulatory pressure, financial institutions can utilize AI to improve and automate the monitoring of data quality, especially for product data that is used for regulatory reporting.

  • Financial institutions have better ways to measure efficiencies, service channels and customer satisfaction.

  • Automation, already an important part of consumer banking, will become more pervasive, delivering benefits for a bank’s cost structure.

According to a recent McKinsey’s Global AI Survey Report, nearly 60% of financial-services sector respondents reported that their companies have embedded at least one AI capability. The most commonly used AI technologies are robotic process automation (36%) for structured operational tasks; virtual assistants or conversational interfaces (32%) for customer service divisions; and machine learning techniques (25%) to detect fraud and support underwriting and risk management.

Financial institutions have historically struggled with implementing and scaling AI technologies across their organization, with the biggest obstacle being a lack of clear strategy. But now, more than ever, it is imperative for financial institutions to harness the sophisticated data aggregation and leading-edge AI and machine learning techniques needed for analytics to keep up with other financial institutions and Big Tech companies that are looking to enter financial services. Without it, they risk losing customers to the competition.

AI can enable financial institutions to create a hyper-personalized and unified customer experience and improve and automate the monitoring of data quality, which will only become more important in the years to come. Financial institutions will need to embed AI into their systems to be able to keep up with the future of finance.

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