Reimagine Banking with Conversational AI

COVID-19 has disrupted operations and will have prolonged impacts on continuity of operations, modes of working, and growth patterns. CIOs need to respond to the crisis with both short- and long-term actions to increase resilience against future disruptions and prepare for rebound and growth.

Banks have long had on their agenda to streamline and automate processes. The Banks who were leaning more towards cost-saving were exploring RPA for automation of administrative processes and those banks which were cash-rich and were looking to better their customer experience were looking towards Conversational AI to make workflows more efficient.

Banks, which have typically had a heavy offline interaction with customers, today, face challenges to business continuity as employees are stranded in various locations with uncertain return dates. Apart from this, dramatic changes in customer demand are putting banks under huge stress: sharp declines in demand present serious financial challenges to many businesses, while those facing demand surges and resource shortage risk disappointing and disengaging customers.

And in times like these, the best of business continuity plans and sustainability forecasts have fallen short of making predictions for a global lockdown and disruption. In this white-paper, we’ll walk through the challenges faced by the banking industry and a few solutions to undertake for the short-term and long-term.

Challenges in Banking and Financial Sector

One of the biggest challenges top executives at banks face is the gamut of transformation required. From internal processes to customer-facing processes, all areas require digitization and automation.

As per the research done by our Analyst partner Gartner, data-driven technologies are still seen as a game-changer, but many banks are perplexed on how to move forward?

The 2020 Gartner CIO Survey asked banking IT leadership to identify which technology capabilities they see as crucial to their organization’s evolution. For the 2020 survey, data analytics, and artificial intelligence (AI) were at the top of the list and roughly equal for financial services CIOs (See Below Image).

Game Changing Technologies for Banking and Financial Sector
Game-Changing Technologies for Banking and Financial Sector

During the last few years, there has been much discussion about how customer engagement in banking can better serve and provide new product opportunities to customers, considering all the points of engagement offered by banking institutions. Often-used buzzwords like “omnichannel” or “multichannel” have punctuated this discussion. Often, however, banks find that they are unable to digest and analyze customer data and transactions well enough to support the user interface or customer journeys.

Challenges faced by a bank’s customers:

Customers today are more available on conversational channels such as messaging apps and IoT devices. Whereas banks have successfully transitioned to mobile banking and app-based banking.

Where banks are focussing on icons, menus, and clicks; users have moved to expect a conversational user experience on voice and chat. Where banks are focusing on transactional coverage, customers are expecting transactional, service-related, and even advisory services to be made available, on the channels of their choice.

Gap between mobile banking and Conversational AI Banking
THE GAP BETWEEN MOBILE BANKING AND CONVERSATIONAL AI BANKING

Challenges faced by a bank’s agent:

With business continuity completely disrupted, employees are finding it hard to run the business as usual, for processes that were offline and even for the processes that were online.

Various quarantine measures and travel limitations were undertaken by different countries and cities have created big uncertainty around employees’ return to work dates. Even returning employees are often asked to self-quarantine for seven to 14 days.

Internationally, indefinite travel restrictions by many countries are causing similar uncertainties to business operations. Operations have either been suspended or run on a limited capacity. Since the outbreak, demand for digital collaboration tools has skyrocketed as organizations are deploying these tools so that employees can work remotely.

How should banks approach this problem?

In Banks where remote working capabilities have not yet been established, CIOs need to come up with interim solutions in the short term. They should consider the following:

  • Identify Use-case requirements: These include instant messaging for general communication, file sharing/meeting solutions, and access to enterprise applications such as ERP and CRM. Besides supporting employees, organizations also need to consider supporting customers and partners to an acceptable level of satisfaction.
  • Review Security arrangements to support remote working: As employees are likely to work from public network connections and use personal devices, CIOs should deploy endpoint security management onto employees’ devices.
  • Find vendors and test solutions quickly: Opt-in for ready to implement solutions that are lightweight and can help ease your short-term bottlenecks. It is likely that you will need a combination of tools to cover all your use-case requirements. However, you need to prioritize solutions that are easy to implement. Sometimes, this might mean using consumer-grade applications such as WhatsApp for messaging.
  • Continued Lockdown Readiness: Globally, it is estimated that it could take up to 2 years to return to a new normal. In the interim, it is imperative you establish remote working policies and extend remote working solutions to employees who usually work from the office.

How can Yellow Messenger help banks drive business continuity and growth?

Conversational AI channels have the potential to help banks solving this customer interaction conundrum, capitalizing on three major consumer and technological trends:

  • Messaging is now the preferred customer touchpoint: Messaging apps are now the dominant form of mobile interaction, enabling easy, fun interactions on the move. Their simple, intuitive text or voice-based interfaces are loved by Millennials, as well as by consumers typically more reluctant to embrace digital channels too. They’re also AI-ready, offering easy integration with chatbots and cognitive agents.
  • Conversational AI is Ready for B2C enterprises: As conversational AI continues to develop, bots are becoming more human-like in their interactions, and can now be built with self-learning capabilities. That enables not only the automation of repetitive customer care tasks but also low-value advisory services.
  • Mass Personalization and Liquid Expectations: By leveraging new data-driven insights, companies are able to offer unmatchable customer experience and personalized digital services at a mass level. This creates competition across, as well as within industries, as customers’ “liquid expectations” means each digital interaction is expected to be as good as the best last experience, regardless of brand or industry.

Solution 1: Conversational AI for agents in Banking

Using Yellow Messenger’s service desk automation, you can leverage the true power of AI by closing the automated-learning loop between humans, platforms, and bot. Empower your agents to do more with less at hand by:

  • Providing Agent Assistance: Using conversational AI, you can enable your agents to be highly efficient by enabling access to customer sentiment and the past context of the conversation. Integration with CRM and service desk backend tools enables the bot to show relevant data like past case references, customer history, and journey map.
    Agent assist feature searches for past resolutions by agents for similar queries to provide recommended responses which can be audited by the agents and sent across without typing a single letter. Entire agent workflows can also be automated and triggered by the bot on behalf of the agent at the click of a button.
  • Live Agent Transfer: Deploy Virtual Assistants to be the first line of respondents and take the load off ~65% queries which are standard, and the only handoff to a live agent the queries that really need their input. Cognitive agent routing capabilities ensure that each ticket gets assigned to the most relevant agent-based on concurrency, ticket details, agent availability, past conversations, and several other factors. Agents can either chat with the customers or use voice and video calling capabilities to resolve issues on call.
    Our intelligent queue management allows you to manage and allocate ticket flow even when agents are offline. The service desk can be customized to show relevant data using integrations with your CRM, IT requests, and support systems and is available as a mobile app for agents who do not have access to laptops at home.

Solution 2: Conversational AI for customers in Banking

The implications of conversational AI in banking are far-reaching, especially when it comes to Customer Experience. Here’s how Virtual Assistants can help Banks enrich their customer experience:

Banking Virtual Assistant: Virtual assistants can provide a personalized banking experience, on-demand, across channels like Web, App, Messaging, Voice, and more. These virtual assistants can help answer FAQs and even take the role of advisory and upsell products that are more likely to be appreciated by the customer.

Contact Center Automation: No more long wait times on calls. No more repeating your query at multiple handoff points. With IVR automation, customers can reap the benefit of being served in less than 1 minute and banks can enjoy cost and experience efficiencies.

Case Study: Zest Money

Challenges Faced by Zest Money:

Zest Money (ZM) is an NBFC (Non-banking financial institution) based out of Bangalore, India. Zest Money gives out mini loans for consumers buying on e-commerce stores within minutes. The problems faced by Zest Money was around the customer support & engagement with ZM customers:

  • The call volumes on the call centers were high which led to linearly scaling the call centers with the number of customers
  • The resolution time for each customer query was high
  • Re-engagement and reminders for paying back EMI through SMS had less open rates & conversions
  • There was no scalable channel for ZM vendors to reach out for very specific queries when onboarding a new customer. The definition of the vendor for ZM would be stores selling electronics (Reliance Digital, Croma, etc.)

Conversational AI Solutions for Zest Money:

Yellow Messenger designed an omnichannel experience on WhatsApp and Website for both, Zest Money’s customers and vendors. On the customer side, we adopted the strategy to launch a live chat for the first 3 months on their website and app dashboards to help gather data around the types of queries that were coming repeatedly which gave the initial data for automation.

In the live chat implementation, Zest Money was getting 3500 ~ 4000 tickets a day which was answered by 120 agents throughout the clock. Equipped with the queries from users, Yellow Messenger built out customer support conversational AI model that could answer the repeated queries and plugged the same as the first level query resolution on the live chat, the combined solution of Chatbot + Live Chat (For fallback).

On the vendor management, to solve the problem of easily getting connected to Zest Money, we launched a verified WhatsApp channel using the WhatsApp for Business (WABA) API and on the backend connected to the live chat module of Yellow Messenger.

For solving the engagement problem and bringing down the EMI defaulters, we launched a WhatsApp verified account for end customers, and prior to the date of payment, a WhatsApp notification was sent out to the end customer with the payment link. Follow up reminders were also set up to increase the likelihood of payment without any penalties.

Impact of Conversational AI Implementation in Zest Money

The impact of the platform and the solution is multifold:

  • With the chatbot automation kicking in after the 3 months live chat period, with 40 agents, Zest Money was able to handle 5000+ tickets on any given day. The reduction of the number of agents happened due to the COVID lockdown in India, and with the automation in place, ZM was able to manage all the volumes coming in without seeing any drop in the NPS.
  • The query resolution time for the end customer came down by 75% as there was no agent queue, and the bot was able to provide instant responses to the customer queries
  • The NPS for vendors increased drastically and the query resolution time came down by 75% (2 to 3 minutes vs compared to 10 ~ 15 minutes)
  • With the WhatsApp push notification, the payment without penalties increased by 20% and the customer satisfaction with the brand went by up 45%
Result of Yellow Messenger Conversational AI Solution in Zest Money
Result of Yellow Messenger Conversational AI Solution in Zest Money

Case Study: HDFC Bank

Challenges Faced by HDFC Bank: HDFC Bank’s personal loan department had a problem of high drop-offs on the personal loan page when moving from an informational page to an application page for the personal loans.

The other hurdle faced by the personal loan team was a lot of unqualified leads were coming into the system and reducing the productivity of the overall engine as HDFC Bank’s rep had to make each call to understand the requirements of the person applying for loan and basis the information provided were to mark if they were qualified or not.

Conversational AI Solution for HDFC Bank: Yellow Messenger provided a system that can collect, qualify, and push the right leads to the lead management systems through chat & voice automation.

Yellow Messenger deployed a banner bot on the same information page for customers to easily understand the product offerings by asking their questions in realtime to the chatbot (Ex: What is the interest rate for education loan and are there any concessions?).

The chatbot also has an intelligent loopback to get information about prospects’ requirements and contact details. Once the prospect applied for a loan, the Yellow Messenger platform would trigger an automated outgoing call to qualify if the lead is valid or not. Based on the call, the lead would be pushed into HDFC Bank’s lead management system (LMS) wherein the bank reps could work on only qualified leads.

Impact of Conversational AI implementation in HDFC Bank

The engagement on the information page with the banner chatbot grew by up to 30% and the number of leads generated on the same microsite for personal loan grew by 10X for the top of the funnel, and 30X for the qualified leads which were getting pushed into their system. The revenue generated & impacted through the chatbot per month was upwards of $2M.

Impact of Conversational AI implementation in HDFC Bank
Impact of Conversational AI implementation in HDFC Bank

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