How To Use AI For Rapid And Sustainable Customer Engagement?

How To Use AI For Rapid And Sustainable Customer Engagement?

Banks have experienced disruptions over the past few years due to escalating consumer expectations, cost-competitive digital players, quickening technical development, and tighter regulation. These were added to margin pressures in a climate of low borrowing rates.

To address these challenging issues and remain impactful in the future, established banks started digitizing. Banks reduced the number of their branch locations to reduce expenses as traffic shifted to digital channels.

The banking industry underwent a rapid digital shift as the epidemic started. When branch transactions stopped, it made sense for banks to close more locations because their cost-to-income ratios were twice as high as those of digital-only providers.

Although the Internet and mobile devices are pretty effective for business, they lack face-to-face contact or in-branch interactions. One solution is using data and AI to generate personalized engagement and gain deep customer understanding.

For instance, use conversational AI to enable “human-like” interactions with clients or employ machine learning to analyze internal and external data (transactional, behavioral, lifestyle, and social) to give banks insights that feed into contextual experiences.

What are the possible hurdles to adopting the change?

Banks today have developed their digital infrastructure enough to recognize the value of AI. In a recent survey, 86% of financial services organizations implementing AI stated that success in the following two years would depend heavily on it. However, there is still very little actual use.

The usage of AI technology by banks outside of security and risk management is only 25%, according to our most recent research, Maximizing Digital Banking Engagement.

The issue is with legacy. Despite the rapid advancement of digital technologies, banks are nevertheless plagued by antiquated banking systems. Integrating AI solutions is challenging because they are closed, rigid, and rife with silos.

Significant customer engagements require a thorough understanding of the client, delivering analytical insights to help the customer manage their finances more effectively, and recommending the next steps. Only analytical and AI-based solutions have the speed and scalability to accomplish this.

Do you understand your customers better?

For a complete understanding of a customer, comprehensive data is required. To determine current preferences and predict future needs, banks should implement a platform for obtaining information about their clients’ existing banking and commercial ties, social media activity, and location.

Our research has revealed that only 5% of banks have successfully developed a client engagement strategy. Ad hoc solutions centered on the customer experience are how most businesses respond to changing customer expectations.

Banks may use AI algorithms to create highly individualized, contextual experiences and recommendations that will improve the financial security of their clients.

A bank might advise investing in a particular fund, foreclosing on a mortgage, or reducing discretionary spending depending on a customer’s income and expenditure patterns, life stage, and financial assets and obligations.

Even on impersonal digital platforms, AI and data can support personalizing customer connections. The technologies can, however, bring specific difficulties.

The results depend on the caliber of the algorithms and training data sets, and setup expenses are significant. Furthermore, no one answer fits all problems. Banks should, therefore, base their decisions on their unique context, considering factors including corporate objectives, customer segmentation, and technological readiness.