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  • CogniCor

Should Banking and Financial sector deploy cognitive virtual agents?

A set of creepy looking machines begin to unleash a reign of terror in the city. Without breaking a sweat, they overpower the city. Like an epidemic, they began expanding the boundaries of their junta until the whole world comes under their mis-engineered, mechanized feet. Then, the hero makes his grand entry. Even though he suffers some major setbacks in the process, he salvages the human race by wiping out the diabolical machines from the face of the earth. Artificial intelligence taking the wrong turn has been for long a staple for Hollywood sci-fi flicks. AI may not have acquired the power to conquer the world as yet. But, it certainly holds the power to transfigure financial services providers into futuristic business organizations.

The failure of chatbots, a word which almost has become synonymous with AI technologies, has done multiple rounds on online discussion platforms. Yes, there have been some high-profile failures last year, including Facebook’s M and Tencent’s BabyQ, which inspired many eulogies. Despite all these setbacks, 57% of the companies globally, according to a Forrester report, are piloting or actively considering implementing one soon. The trust displayed in AI technologies is encouraging. But trust and confidence alone will not be enough to benefit out of the same.

Start by defining success

Innovation is a collaborative effort. The silo mentality will dampen the prospects of an organization looking to innovate. A multidisciplinary digital innovation team in collaboration with other key stakeholders should come up with a list of clearly define objective before kickstarting vendor scouting and the deployment processes. A cognitive conversational agent is not a magical unicorn that decides its purpose on its own. Rather, you should let your business acumen define a purpose for it.

Singapore-based OCBC bank was very clear about the purpose of their Virtual Assistant, named Emma. As of November 7, 2017, Emma helped OCBC to sell $70m worth home loans (Click here to read the report) Indian financial service provider Tata capital implemented a cognitive virtual agent to offer level 1 customer support to its customers. This has helped them in reducing the volume of the calls to the call centre and also offering incredible customer experience for its customers. Later Tata Capital expanded the scope of the virtual agent to include lead generation. Yes, to be successful you must define what success looks like.

A ready, fire, aim attitude doesn’t go well with virtual agent deployments. Cognitive agents just like humans require time before it could start solving problems for the customers. A pilot offering of the agent to a restricted audience who are foretold about the experiment will help in further fine tuning the agent before a grand rollout. Doing a reality check on the goals set is also recommended. Setting unattainable goals for the project makes it a failure, right from the outset. By clearing the misconceptions about AI and with a focused and disciplined approach financial organisations could tap into the its transformative powers.

The following are the reasons for them to deploy an AI-powered virtual agent.

1.Helps to reduce customer effort

Customers are increasingly becoming open to non-bank service providers says CGI’s 2017 global financial customer surveys. In the face of fierce competition from banking and non-banking players, accelerated services and innovation agenda is required for the banks to retain and grow its customer base Removing customer-brand interaction hurdles is one of the keys towards achieving the same. With smartphones and internet technologies becoming ubiquitous, text-based chat proves to be an easily accessible and highly usable medium.

2. Puts a cap on call volumes

Call Centre lines of major financial services organizations are almost always busy. The reason as highlighted by our survey is that the service lines are clogged by customers seeking level 1 support. Mostly these requirements could be fulfilled effectively by an AI-Powered chatbot. Building a frontline chatbot that could address level 1 queries could not only remove the obstacles by providing instant, 24/7 support but also would lower the number of call landing the call centers. Considering that one service call costs approximately 1$ to the brand huge savings is up for the stake if level one support can be diverted to a channel which is more effective.

3. Make you customer service ready for millennials

By this year Millennials is predicted to become the group highest purchasing power. In the United States their spending power will hit $200 billion this year. They are a tricky band of customers. Customer service glitches may prompt them to switch brand easily. At the same time, they can better experience customer satisfaction than baby boomers and are more likely to become brand evangelists that was revealed by a consumer survey by the U.S-based Marketing consultant J.D. Power.

4. Taps into social media opportunities

62% of the millennials prefer brands interacting with them over social media. And customers across generations spend considerable amount of time on social networks. By deploying a conversational agent social media pages can be converted into customer service channels or may be lead generation magnets. For financial service organizations looking to rewire their DNA to adapt to the customer-brand engagement behavior of the millennials a text-based conversational agent is an ideal tool to capitalize on it.

5. Facilitates deeper customer engagements

Cognitive virtual agents can help a brand to build a deeper bond with the customers by providing them a conversational self-service experience. Instead of creating a labyrinth of customer service channels with customers have to switch between them to get their problems solved AI technologies help a brand in offering omni-channel experiences through seamless integration of channels.

6. Helps you become data-driven

A digital customer-bank interaction channel generates a huge volume of actionable data. This would be helpful in creating event clusters and forecast downstream issues for each cluster. This would step up the forward resolution capabilities bringing down the calls per event considerably. All this depends on your conversational agent’s ability to perform deep analytics. Analysing customer data will also helps the banks and financial service organisations to understand the ever-changing needs of the customers. Customer Intelligence is critical for the success of business.

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