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One of the hottest and most contentious issues facing banks today is how and when to utilise Artificial Intelligence (AI) within a business. AI has transformed many industries and consumers everywhere are becoming increasingly used to the idea of driverless cars, conversational chatbots and suggestive recommendation services.

While AI is relatively new in the financial industry, there are significant concerns and limitations that banks must get their heads around. For example, there is much fear surrounding the integration of AI in workplaces as people believe it will result in job losses and ‘robots’ ruling the world. Even the Bank of England has expressed concern, with their Chief Economist predicting a disruptive fallout from the rise of AI that could make many jobs obsolete.

But when applied in the right way, AI can bring endless opportunities, taking away tedious tasks and amplifying what we do as humans. Tanmaya Varma tells us more.

 Where does AI fit?

Discerning how best to use AI, without alienating customers or employees, is a complex issue. Within the finance sector, AI is already being implemented to support with tasks such as fraud detection and management, and credit card and loan risk assessments. JPMorgan Chase, for example, uses image recognition software to analyse legal banking documents. It is efficient and accurate, extracting information and clauses in seconds compared to the 360,000 hours it takes to manually review 12,000 annual commercial credit agreements. This sort of capability could transform the lives of many banking employees as they will no longer be consumed by administrative tasks but can focus on value-added roles instead.

AI is perfectly suited to many straight-forward roles within customer experience. As much as 98% of all customer interactions are simple queries and bots can be used to monitor and streamline these engagements. For example, RBS’s chatbot ‘Luvo’ has the ability to respond to basic customer queries; and can therefore reduce the need for as many customer service employees.

Over the last couple of years, Goldman Sachs, JP Morgan Chase and Charles Schwab have introduced robo-advisers that are able to manage investments, collect financial data and use predictive analytics to anticipate changes in the stock market. While some employees are concerned about competing with this technology, we’re already seeing the use of bionic advisers in the finance sector. These combine machine calculations and human insight to provide a more efficient and comprehensive analysis, whilst also still maintaining the superior customer service clients have come to expect from their financial adviser.

The robots’ limitations

AI has such great potential but there is still one key thing missing – emotional intelligence (or EI) and when customers are involved, this really matters. Where a bank might pay less for a fully automated interaction, the justification for paying more for the human touchpoint is the real value of emotional intelligence, something that computers can’t really provide… yet.

Responding to the emotional cues that your customer displays is an extremely important part of a business relationship, and the ability to read and comprehend these signals plays a huge part in tailoring the customer experience. The big challenge for banks now that chatbots are so readily available is to consider when and where this key human trait is required.

Chatbots can’t easily detect a shift in tone or tension in a conversation and aren’t able to quickly appease a customer. For example, while a robo-adviser is great for an inexpensive and basic service, the issue comes when you have a more unique or sensitive financial situation such as debt or divorce. In this sort of more complex circumstance, a human adviser is perfectly positioned to respond to the nuances of the conversation.

Collaboration is key

There is a great opportunity for AI to go hand-in-hand with human employees - chatbots can be used to streamline the experience, deal with straightforward customers and put more complex enquiries through to the most suitable team member. In this way, banks can bring humans and technology together to provide a superior customer service.

Another example of AI working in tandem with human employees is Relationship Intelligence technology. With thousands of contacts on a database, no adviser can possibly be expected to remember what stage each customer interaction is at and build strong relationships with all of them. Instead, AI can provide insights into who your prospects are, which ones are most beneficial to pursue and when the right time to get in touch is. It can instantly make available information and data from all over the internet about any potential prospect from just a name and email address.

As technology advances, banks are having to walk a fine line between looking for cost-saving efficiencies and smarter ways of working, while ensuring their customers continue to receive excellent and personal service. They also do not want to alienate their workforce and create panic that long-standing staff are slowly being replaced by robots. AI can offer a lot but it doesn’t have the human’s ability to build and maintain vital relationships and collaboration between technology and humans is key here. The successful adoption of AI in the workplace is the issue and opportunity of the moment and one that banks will be contemplating for years to come.

 

Today's typical business intelligence (BI) user increasingly prioritizes mobile, fast, and customizable platform options, and platform providers are feeling the pressure to evolve quickly to meet the demand from customers.

A new survey from Clutch finds that 70% of data analytics users consider a mobile application crucial to their use of BI software. Even those users who said they don't consider a mobile application crucial have taken notice; nearly 60% of those users said they recognize mobile BI applications as increasingly important to their business.

User emphasis on mobility has grown significantly in a short amount of time. Clutch's 2016 BI survey found that only 41% of data analytics users even used a mobile phone or tablet to access their BI data--now almost double that number believe mobility is crucial to their use of the software.

Hyper-paced work environments, the need to perform complex analytics 'on-the-go,' and the stronger processing power of smartphones and tablets have all led to greater demand for easy-to-use and powerful, mobile BI platforms. Users now look for platforms that seamlessly transition between desktop and mobile, beyond the basic mobile capabilities that many BI organizations already provide.

"Simple dashboards on mobile exist in most BI tools out there," explains Yair Weinberger, CTO and Co-Founder of Alooma, a data warehousing and analytics platform. "But mobile BI software for data analytics users who want to research deeper, drill down into the data, or split the data according to some parameters or features, is still lacking."

Data analytics users who increasingly see BI as a fast, mobile, and constantly available tool, are also concerned with the reliability of speed and simplicity when they access their data. Accessing their data is "not simple" according to 31% of users, while 36% say they wait, on average, more than a day to gain access to their data.

Less than 20% of respondents say they typically only wait a few minutes to gain access to their data. In the business intelligence industry, a time delay of hours or more to access data can pose a problem for employees attempting to collaborate quickly and efficiently with colleagues.

The desire to have more control over data accessibility may be why 85% of data analytics users are likely to use open source software in the future, according to the Clutch survey. Open source software offers users the opportunity to operate on the cutting edge of BI technology and play a more direct role in their data analysis.

However, experts say they are confident that commercial options will remain competitive as a time-saving, safer, more supportive software option. "The open source software community doesn't have an advantage when it comes to the constant, quality support that commercial options offer," says Derick Bai, Global Vice President of Engineering at DrivenBI.

(Source: Clutch)

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