AI + Human Banking Personalization
To explore how AI/digitalization can be combined with a human element to make the banking experience more attuned to individual customer needs to inform the client’s continued research.
AI/Digitalization for Personalization in Banking
- Across the financial services sector, many banking institutions utilize AI/digitalization as cost-savings measures only, and have yet to develop more institution-wide uses for the technologies.
- In fact, very few banking institutions (about 9%) have actually considered consumer preferences when considering AI implementations, and only about 10% see the customer experience as the top priority in these types of implementations.
- Research has proven that top-value customers at financial institutions “appreciate AI-assisted interactions the most,” with experts recommending implementing AI across the customer spectrum to “generate efficiencies and better satisfaction ratings,” as well as a higher ROI. They warn, however, that taking a fully-ROI-focused approach is the wrong way to go, and that prioritizing the customer experience is key to greatest success.
- Deloitte’s research showed that, “Leading companies are recognizing that [AI] technologies are most effective when they complement humans, not replace them.
- Experts at Technology Record note that “capturing customer needs and owning personalized insights” can be key to “an AI-driven end-to-end customer journey” that’s “seamless, engaging, and frustration-free.” AI can be used to complement and encourage visits to bank branches at just the right time for each customer, which can lead to increased business for the bank. AI-powered facial recognition technologies can contribute to serving the right solution to the right customer at the right time.
- Banks can benefit from AI-powered interactions in many ways, including: through chatbots and virtual assistants, which utilize natural language processing/generation and voice/facial recognition programs; for profiling customers in order to present more-personalized options and interactions (through machine learning and behavior pattern analysis); for streamlining internal processes, like having AI-powered back-up for laws and regulations, which could help determine which issues require human interaction; for spotting patterns utilizing machine learning to reduce fraud; as well as others.
- Some examples of financial services institutions utilizing AI successfully include JPMorgan, which using AI-bots to execute trades and Morgan Stanley, which “has an AI fraud detection team.”
Consumer Preferences in AI-Assisted Interactions
- Multiple experts agree that financial services institutions that incorporate consumer preferences into their AI-implementations are most successful, as noted previously. Research into consumer preferences in these interactions show specific inclinations. Capgemini research showed that about 63% of consumers felt that “AI-assisted engagement gave greater control over their interactions,” with the same percentage believing that the 24-hr/7-days-a-week availability of AI-bots offered great convenience.
- The same study also found that more than half (64%) of consumers prefer AI-bots to sound more like humans (than bots), and 57% liked it when they could hold “a sensible conversation” with a bot. Just over half of consumers (54%) want their AI-bots to have personalities, and 55% of them want the bot to be able to display the quality of empathy.
- Nearly three-quarters (71%) of financial services consumers want to be made aware when they are dealing with an AI-bot, while only 66% of general consumers share this concern. Financial services consumers want complete transparency in their financial institutions, including with whom (or what) they are dealing during transactions, as well as how the data collected will be used.
- Notably, with decisions of greater importance, consumers prefer a higher level of human interaction (over AI-bot interaction). Also of interest, the younger the consumer, the greater the preference for AI-human-combination interactions.
Proposed next steps:
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Research showed that only a low number of financial services institutions globally are using AI/digitalization for more than just low-level services or interactions. However, there are best practices recommended by fintech experts on implementing AI solutions across these institutions. We could continue this research by identifying several best practices for AI-implementations, as well as identifying the pitfalls financial services institutions have identified during these implementations.
Alternatively, we could develop 2 – 3 in-depth case studies that outline the successes (or failures) of financial services institutions’ AI implementations.