How Traditional Banks can Stay Ahead of Fintech Firms with Conversational AI


The rise of nimble fintech startups is disrupting the traditional banking sector. Customers today expect personalized digital experiences, seamless cross-channel engagement, and financial insights from data-driven recommendations.

Many established banks are lagging in deploying innovative digital capabilities to meet these new demands. This has enabled fintech challengers to gain market share, especially among digitally savvy younger consumers.

Adopting conversational artificial intelligence presents an opportunity for incumbent banks to regain competitive ground. Intelligent virtual assistants create smoother customer interactions and unlock data insights that better inform financial recommendations.

Banks that strategically implement conversational AI capabilities can boost digital engagement, increase customer lifetime value, and build trust-based relationships grounded in personalization. Let’s examine key use cases where conversational AI can help traditional banks stay ahead of fintech rivals.

Enhancing Digital Banking Experiences

For many consumers, visiting physical bank branches for basic needs like checking balances and making transfers feels antiquated compared to slick fintech apps. Introducing virtual assistants allows traditional banks to similarly modernize digital engagement.

With conversational AI, customers can chat with a virtual assistant 24/7 through the mobile app to manage finances or seek information. Natural language understanding enables accepting commands, interpreting questions, and handling multi-turn conversations.

Personalized updates like “Good morning Matt, your current balance today is $1,425” feel more natural and personal than static app dashboards. Virtual assistants also simplify navigating menus and transactions via voice commands.

The leading examples of AI assistants in banking come from large institutions like Bank of America’s Erica, Wells Fargo’s Eva, and U.S. Bank’s U.S. Bancorp. Early evidence shows customers engage more via conversational touchpoints. This increases digital adoption.

Banks deploying AI assistants at scale report higher online banking usage, improved Net Promoter Scores (NPS), and lower call center contacts after rollout. By matching fintech experience expectations, conversational AI becomes a retention strategy.

Enabling Seamless Omnichannel Orchestration

Fragmented workflows across separate channels like physical branches, call centers, online banking, and mobile apps represent a key friction point. Customers expect unified experiences with context maintained across channels.

Conversational AI acts as the connective tissue tying omni-channel interactions together. A single assistant can engage customers across devices while recalling past conversations and profile data.

For example, a user could ask about mortgage rates via mobile app. The AI assistant can then follow up over phone later that week with a personalized rate recommendation using details captured earlier. This anticipatory orchestration creates a more seamless cross-channel experience.

The AI assistant also enables easily transitioning interactions between channels when appropriate. If the user struggles to apply for a loan online, the assistant can seamlessly open a call center connection to resolve it.

Omnichannel deployment allows conversational AI to transform banks’ historically disjointed workflows into unified engagement pathways. Customers feel known, understood, and assisted intelligently across touchpoints.

Automating Service Tasks to Increase Efficiency

Another benefit of conversational AI is alleviating contact center volume by automated handling of common service requests, informational queries, and simple transactions. Offloading these frequent low-complexity interactions improves operational efficiency.

For instance, instead of calling to check an account balance, customers can ask a virtual assistant through any channel. The AI can address top reasons for checking balances, like recent deposits or upcoming payments, based on analysis of user finances.

For simple withdrawals, transfers, and credit limit increases, the assistant can again handle end-to-end fulfillment after asking clarifying questions. Automating these high-volume tasks reduces call center contacts.

One major UK bank saw a 10% decrease in call volume after launching its AI assistant. Offloading common requests enables agents to focus on higher value service interactions. Analysis of automated interactions also informs continuous assistant improvement.

The key is strategically identifying the right tasks to automate based on call drivers, transaction data, and impact on customer experience. Conversational AI handles high-frequency repetitive interactions, while complex use cases still route to human specialists.

Delivering Personalized, Proactive Recommendations

Access to financial data empowers conversational AI assistants to deliver personalized, timely recommendations. Analyzing spending patterns, upcoming bills, and account activity enables more proactive insights.

For example, the assistant could warn a customer if their income this month appears unlikely to cover approaching credit card payments based on cash flow analysis. Or it may inform customers about bill payment delays to avoid late fees.

Assistants can also recommend transferring funds between accounts to improve interest earnings or avoid overdrafts. These personalized finance recommendations delivered through natural conversation build trust and emotional connections.

The key is applying analytics to customer financial data to power relevant recommendations that demonstrate the bank’s understanding of individual needs. This differentiates from fintechs lacking long-term customer intelligence.

Analyzing aggregated data across customer bases further enables identifying life event opportunities. For instance, graduation spend patterns may trigger refinancing student loans. Or a major purchase could prompt offering special savings accounts for future goals.

Banks possess enormous financial data assets. Conversational AI unlocks deriving personalized, emotionally intelligent recommendations from this data at scale to drive engagement.

Improving Financial Advisory Service

For wealth management clients, conversational AI assistants augment human advisor relationships. Virtual assistants act as robo-advisors that engage customers between advisor meetings.

The assistant may review retirement plans, college savings, portfolio allocations, tax strategies and other financial goals. Users can inquire about budgeting, debt management and general investment guidance through natural conversation.

Intelligent assistants conversationalize the financial planning process. This relieves advisors from repetitive inquiries on data-driven topics like market performance or portfolio rebalancing recommendations.

Augmenting human advisors with AI assistants creates a differentiated advisory model. Users get 24/7 access to guidance and planning coupled with in-person expertise for major financial decisions.

This hybrid approach brings fintech-style automation together with the human touch needed to manage major wealth transitions and events. Advisors benefit from focusing on the most impactful personal interactions.

Boosting Customer Acquisition

Finally, banks can leverage conversational AI capabilities to improve customer acquisition in multiple ways. Virtual assistants provide immediate support when prospects explore offerings on the website.

Chatbots can also engage visitors browsing finance-related content with personalized recommendations. Additionally, the assistant guides interested prospects through application processes like loans and credit cards via conversational flows.

Conversational analytics identify sales opportunities based on interest expressed in browsing patterns and dialogues. Users who inquire multiple times about mortgage rates may receive proactive offers, for example.

Banks can further embed conversational touchpoints into marketing campaigns across channels. Ad clicks may open a chat window for instant product guidance. Email promos can include dialogue CTAs. The AI assistant therefore becomes an omnipresent sales aide.

These use cases demonstrate how conversational AI allows incumbent banks to counter fintech disruption. Assistants modernize digital engagement, drive efficiency through automation, and generate insights from customer intelligence.

Keys to Successful Implementation

However, simply launching an AI assistant is not guaranteed to counter competitive threats. Success requires thoughtful implementation addressing key areas:

– Multi-channel deployment reaching customers across existing touchpoints and devices.
– Integration with core banking systems for seamless access to account data.
– Conversational design focused on utility over novelty interactions.
– Thoughtful persona development so the assistant’s personality instills trust.
– Ongoing improvement processes using interaction analytics and customer feedback.
– Clear value propositions communicating benefits to encourage adoption.
– Seamless handover protocols for escalating complex requests to human agents.
– Hybrid advisory models blending automation with human expertise.

User trust represents an especially crucial element. The assistant’s communication style and persona should instill confidence while avoiding perceived creepiness or overfamiliarity.

Banks must also ensure transparent disclosures around data privacy and security to build trust. Handling sensitive customer finance data introduces risks if not managed diligently.

Measuring Success
Key metrics for gauging conversational AI success include:

– Digital engagement growth in new vs repeat users
– Omnichannel escalation rates
– Automated resolution rates for routine requests
– Customer satisfaction scores
– Net Promoter Scores
– Adoption across customer segments
– Impact on call center and advisor utilization
– Sales conversion growth

Done right, conversational AI can become a formidable competitive differentiator. But banks need strategies tailored to their strengths, customer base, and deployment capabilities.

No fintech can match the customer intelligence from decades of financial data at traditional banks. Conversational AI unlocks activating these insights at scale. Big tech giants have also failed to disrupt the banking sector so far.

In the end, large banks must evolve digitally to retain consumers in the fintech era. Conversational AI represents a key tool, not a silver bullet. Banks need holistic digital transformation strategies with AI assistants as a critical component.

With thoughtful implementation, banks can leverage conversational AI as a key pillar of digitally-driven experiences. But achieving true competitive differentiation requires combining assistants with improved UX, platform modernization, devops, automation, data analytics, and more.

Conversational AI alone won’t save banks. But neglecting this capability while fintech experience gaps widen risks accelerated customer erosion over time. Weaving assistants into broader digital strategies can help established banks retain relevance amid a rapidly changing financial services landscape.

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