February 21, 2017

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ai virtual agent, virtual chatbot

Picture this scenario: You are on a two-week long business trip across multiple locations. It’s been five days and you are wondering how much your current trip has cost you already. You tap on your bank’s app on your phone and type in “How much did I spend in New York last week?”
A bot answers “A total of US$343.54” followed by “Would you like to file it under expense?”
You say, yes. It promptly marks it to your expense list to submit to your company and sends you an email report.

You follow up by asking “What’s the status of that loan disbursement? The bot answers, “It’s scheduled to be disbursed this Wednesday.”
Switching to another scenario: You ask the bot for options on investments and the bot responds, “Based on your profile and spending patterns, the gold saver plan is the ideal investment option. I’m assuming you are looking at medium-term options?”

In a more urgent scenario: You notice a transaction was made with your credit card that you did not authorize. You initiate the conversation with the bot: “I did not make the last transaction on my card.” The bot replies, “OK Tim, please do confirm your identity by naming your dog and your first school.”
You: “Buster, St. Marks.”

Bot: “Thank you for confirming your ID. I have marked the last transaction as fraudulent and will update you on our findings. If you still have your card with you, I recommend that you choose the OTP option for all future transactions on your card.”
These interactions between you and your bank bot are extremely personalized and relevant to your needs. More importantly, these show how banking can be made easy and on the go. This is digital banking at its best.

Challenges

Everyone has been talking about the next big IT revolution driven by artificial intelligence (AI)—mainly natural language processing (NLP) and machine learning (ML). Doomsday predictions of mass layoffs and machines taking over jobs are prevalent. However, these predictions are quite misplaced. The technology is here to enable us to do things better, increase efficiency, productivity, and build on the quality of core services overall.

Banking is an area that touches everyone’s lives but it is mostly viewed as a critical and necessary yet intimidating and difficult task. The key reason for this is a lack of understanding of the financial terms, the intricacies in the product or service offerings, and the related processes and norms that need to be followed to make the most of your investment or savings.

Financial institutions, on the other hand, struggle with servicing the multitude of queries and concerns from customers efficiently enough. The pressure of managing new accounts and selling new products and services take precedent over building strong relationships and customer service in the true sense.
IT investments in big data systems have resulted in huge amounts of historical and real-time data that have not been fully utilized to build for scale and efficiency.

Opportunities

Efficiency and scale

On the point of efficiency and scale, a single web assistant implemented in a bank in Europe, for example, achieved an average of 32,000 conversations per month and a first-contact resolution of 78 percent in its first three months, as it handled over 350 different customer questions and answers. And this instance is based on just the supervised learning capabilities of AI. Time taken for customer feedback to get to business leaders went down to milliseconds if not less.

ML technology has advanced rapidly over the last 10 years, and there are now more flexible and cost-effective solutions that banks can implement, even with their often legacy-burdened IT systems. Computers can quickly analyze new information and compare it with existing data to look for patterns, similarities, and differences. By repeating the activity, the machine improves its ability to predict and classify information making it easier to make data-driven decisions.

Fraud checkers get better

Banks and fintech companies already use ML to detect fraud by flagging unusual transactions and for other purposes. It’s far more efficient than manual monitoring and is soon to become the norm in banking and finance. Even with the latest in technology, we still have human-powered customer support centers falling prey to phishers and hackers as seen in this video.
With an AI in the mix, this type of fraud could never be committed, as the algorithm would not allow for any customer data to be given out unless the appropriate verification data is provided.

Why will it work?

Consumers, particularly millennials, increasingly prefer digital instead of phone or walk-in banking, as they are most adoptive of AI in other areas of their lives. Case in point: they have voice assistants on their home devices and phones. AI applied to customer service is also a big opportunity for retail banks to increase automation and reduce the cost of serving customers, which makes it an attractive option. Also, first contact management limitations in terms of peak traffic will become a thing of the past.

For customers, the technology will simplify money management and will also offer suggestions and recommendations on relevant and new services that are matched by algorithms.
Most of the AI implementations behind the scenes are made accessible to consumers via bots. What is not evident to most is the huge technology push towards making the bots smarter by analyzing data, making personal recommendations, and semantically understanding human language and emotion. This leads to even better user experiences across every user interaction touchpoint with the business.

Conclusion

The growth of automated services, AI, and robotics has heightened the need for traditional banks, financial services, and payment providers to work closely with experienced designers, coders, developers, and marketers. This ensures new concepts are identified, developed, and commercialized professionally and effectively.

Banks will need to work on delivering a service that gets the balance right between machines and humans, ensuring human intervention at the necessary points.

  • The bottom line is that the advantages of AI, NLP, and ML are huge.
    Automated conversations can cut costs of human resource-dependent tasks by up to 80 percent while increasing customer engagement efficiency to more than 95 percent.
  • Automation of key workflows offers useful insights into customer needs and preferences, product and service demand, and performance across various geographies and demographics.
  • Automation of backend processes and systems builds efficiencies across service workflows and ensures large operational cost savings.
  • Not to forget savings in terms of reduced fraud-related costs, easier customer onboarding, and more.

The next step? The technology will also enable banks to deploy virtual assistants across websites, messaging apps, and social media like Facebook, Telegram, Skype, Slack, and others. This makes them available to consumers in their preferred medium.

To see how Elafris Virtual Insurance Agents can help your company click here to schedule a free online demo.

This article was originally published on Techinasia

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