By Gerry Gibney, senior strategist, financial services, OpenText

Consumers today expect easy and convenient access to all of their services – and this is no different for their interactions with the financial services industry. Whether it’s useful banking apps which enable easy mobile-based transactions or a quick chat tool to get in touch with a customer service representative, users want smooth, consistent experiences.

Digitally-enabled lifestyles are driving heightened expectations for service providers not only to meet these customer needs, but to anticipate them. While no single easy-fix solution exists to improve a customer’s experience, artificial intelligence (AI) can play a key role here.

Google’s Ray Kurzweil has already estimated that AI will have human-level intelligence by 2029. While we’re unlikely to see robots taking over, we should expect the service that customers receive from their bank, insurance firm, phone company and utilities provider to improve. But how can financial services implement AI technology to specifically improve customer experience and streamline the customer journey?

Applying AI

Across industries, more transactions are moving from manual to digital, while data – both structured and unstructured – is exploding inside and outside the organisation. As a result, financial services are simultaneously facing rising customer demands while attempting to manage and gain from the huge amounts of data available today.

AI has the potential to help businesses do many things better, faster, cheaper and with fewer errors than any manual process. When combined with advanced analytics, it can also deliver insight into what customers are doing today and what they are likely to do tomorrow – turning reams of data into actionable intelligence that can be applied to improve business processes.

A frequently used statistic suggests that it costs five times as much to attract a new customer as it does to keep an existing one. Faced with this, financial services organisations must carefully consider how to implement AI technology and advanced analytics to transform those parts of the business that will have an impact on customer experience.

Organisations must select those processes that are closely aligned to business objectives so they can see maximum impact from the introduction of AI and analytics. In this way, financial services companies can streamline the customer journey to drive customer loyalty and reduce churn.

Boosting efficiency – and customer satisfaction

AI is not a new concept for the banking industry. The technology is already embedded within some key surveillance processes such as fraud prevention and detection. However, the amount and variety of data that must be analysed is new. With advances in natural language processing technology, machine learning algorithms and expert systems, AI is simply getting better and better at automating repetitive, transaction-intensive work processes.

Predictive analytics has also been implemented for business optimisation for some time. However, we now have access to the missing piece of the puzzle – a cognitive approach that can handle the huge quantities of data available today, intelligently uncover trends and patterns, and use the insight to inform better decision-making. Getting useful insights out of this automation is the key value-add.

By combining AI and analytics, banks can take steps to become more customer-centric. This can range from client onboarding and tracking customer sentiment to identifying areas where customers should be involved. Similarly, AI can be implemented to decrease time to a new market, allowing a bank to build upon its successes elsewhere and quickly translate that into another market to meet customer needs. Across the board, AI has the ability to enhance the organisation’s overall operation and make it more efficient, thereby improving customer satisfaction.

Removing data siloes

Financial services face a key barrier when attempting to implement AI and analytics. Their information is often contained in siloes – stored across different systems as well as different types of hardware and software. Furthermore, despite often holding valuable data, these systems may not have been recently updated and may not be readily accessible by employees in other departments.

Overcoming this issue is essential. It is only by tapping into one pool of data across the business that

AI-enhanced analytics can deliver useful insights that take in the big picture of all the available business data and content. Algorithms and cognitive models help identify trends and patterns in the vast amounts of structured and unstructured data while ‘data noise’ is simultaneously filtered out.

Machine learning and natural language processing become more effective with higher volumes of data. So, as more data is analysed, the more accurate the conclusions and recommendations become. As a result, ensuring access to all of the data across a business will generate more useful insights to improve customer experience.

Customers want products and services specifically suited to their requirements that help them achieve what they want to accomplish. While the financial services industry is expanding its digital capabilities to face this challenge, innovation for innovation’s sake is not enough. Implementing AI and analytics to become more customer-centric will be key to ensuring technological innovation in the industry drives results – great user experiences and happy customers.

By Gerry Gibney, senior strategist, financial services, OpenText

Consumers today expect easy and convenient access to all of their services – and this is no different for their interactions with the financial services industry. Whether it’s useful banking apps which enable easy mobile-based transactions or a quick chat tool to get in touch with a customer service representative, users want smooth, consistent experiences.

Digitally-enabled lifestyles are driving heightened expectations for service providers not only to meet these customer needs, but to anticipate them. While no single easy-fix solution exists to improve a customer’s experience, artificial intelligence (AI) can play a key role here.

Google’s Ray Kurzweil has already estimated that AI will have human-level intelligence by 2029. While we’re unlikely to see robots taking over, we should expect the service that customers receive from their bank, insurance firm, phone company and utilities provider to improve. But how can financial services implement AI technology to specifically improve customer experience and streamline the customer journey?

Applying AI

Across industries, more transactions are moving from manual to digital, while data – both structured and unstructured – is exploding inside and outside the organisation. As a result, financial services are simultaneously facing rising customer demands while attempting to manage and gain from the huge amounts of data available today.

AI has the potential to help businesses do many things better, faster, cheaper and with fewer errors than any manual process. When combined with advanced analytics, it can also deliver insight into what customers are doing today and what they are likely to do tomorrow – turning reams of data into actionable intelligence that can be applied to improve business processes.

A frequently used statistic suggests that it costs five times as much to attract a new customer as it does to keep an existing one. Faced with this, financial services organisations must carefully consider how to implement AI technology and advanced analytics to transform those parts of the business that will have an impact on customer experience.

Organisations must select those processes that are closely aligned to business objectives so they can see maximum impact from the introduction of AI and analytics. In this way, financial services companies can streamline the customer journey to drive customer loyalty and reduce churn.

Boosting efficiency – and customer satisfaction

AI is not a new concept for the banking industry. The technology is already embedded within some key surveillance processes such as fraud prevention and detection. However, the amount and variety of data that must be analysed is new. With advances in natural language processing technology, machine learning algorithms and expert systems, AI is simply getting better and better at automating repetitive, transaction-intensive work processes.

Predictive analytics has also been implemented for business optimisation for some time. However, we now have access to the missing piece of the puzzle – a cognitive approach that can handle the huge quantities of data available today, intelligently uncover trends and patterns, and use the insight to inform better decision-making. Getting useful insights out of this automation is the key value-add.

By combining AI and analytics, banks can take steps to become more customer-centric. This can range from client onboarding and tracking customer sentiment to identifying areas where customers should be involved. Similarly, AI can be implemented to decrease time to a new market, allowing a bank to build upon its successes elsewhere and quickly translate that into another market to meet customer needs. Across the board, AI has the ability to enhance the organisation’s overall operation and make it more efficient, thereby improving customer satisfaction.

Removing data siloes

Financial services face a key barrier when attempting to implement AI and analytics. Their information is often contained in siloes – stored across different systems as well as different types of hardware and software. Furthermore, despite often holding valuable data, these systems may not have been recently updated and may not be readily accessible by employees in other departments.

Overcoming this issue is essential. It is only by tapping into one pool of data across the business that

AI-enhanced analytics can deliver useful insights that take in the big picture of all the available business data and content. Algorithms and cognitive models help identify trends and patterns in the vast amounts of structured and unstructured data while ‘data noise’ is simultaneously filtered out.

Machine learning and natural language processing become more effective with higher volumes of data. So, as more data is analysed, the more accurate the conclusions and recommendations become. As a result, ensuring access to all of the data across a business will generate more useful insights to improve customer experience.

Customers want products and services specifically suited to their requirements that help them achieve what they want to accomplish. While the financial services industry is expanding its digital capabilities to face this challenge, innovation for innovation’s sake is not enough. Implementing AI and analytics to become more customer-centric will be key to ensuring technological innovation in the industry drives results – great user experiences and happy customers.