The state of AI and information readiness in banking

According to a new report on the global banking industry from Finextra, 45% of retail banks say they can onboard a new customer in under 40…

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Monica Hovsepian

February 27, 20204 minute read

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According to a new report on the global banking industry from Finextra, 45% of retail banks say they can onboard a new customer in under 40 minutes. Yet, only a quarter of respondents felt they could pull front and back end systems together to deliver optimum customer service. As banks look to fully exploit the potential of Artificial Intelligence (AI), the report suggests that breaking down data siloes and managing data from multiple sources remain major challenges to information readiness in the banking sector.

The state of AI in banking

AI, especially machine learning and Natural Language Processing (NLP), is greatly impacting the banking sector. Survey respondents indicated that the effects of AI would be most strongly felt in customer service and retention. However, when the Finextra report drilled down into what this meant in practice, the difficulties of successfully implementing AI started to become apparent.

There has been a great deal said about using AI to deliver a personalized customer experience within banking and while this is certainly the direction of travel, this report revealed a cautious approach amongst banks. In fact, only 7% of respondents put personalization as their bank’s most important consideration – a figure that fell to only 3% for retail bankers.

The importance of customer individualization in retail banking

While banks have spent many decades refining their approach to segmentation, they have yet to take it to its logical conclusion and introduce true customer individualization.

According to Finextra, poor information readiness in the banking sector is one reason for the lack of personalization: “Achieving this at scale…is difficult because it requires combining behavioral, transactional and historical data into a single view of the customer, and a significant re-tooling of all systems from marketing and channels through to the back office

The challenge of information readiness in the banking sector

AI is maturing at a dizzying rate. Survey respondents listed the pace of change as one of their top three challenges when implementing their AI capabilities. However, the main theme of the report – emphasized time and again – was the difficulty in identifying, capturing and managing data to be analyzed and provide insight.

Both personalization and the omnichannel customer experience revolve around one core capability: creating a single view of the customer. This means drawing together data from multiple internal and external sources and governing it in a way that allows full value from the data to be extracted. According to the survey, however, simply accessing data from internal sources is challenging enough. Almost a quarter of respondents rated it as the most severe challenge.

Challenges implementing AI in retail banking

Information governance­ – the use and security of information – is paramount to ensuring AI is ethically implemented. But information governance is currently being held back by legacy systems (72%) and data siloes (63%) that make it difficult to quickly and effectively access the organization’s information. In other words, information readiness in the banking sector is holding back AI implementation.

Finextra puts it bluntly: “The data that can become information all lives somewhere. And despite initiatives over the years to create data warehouses, then data lakes (that can sometimes more resemble multiple puddles), it still often resides in poorly connected legacy systems and data siloes.”

Is platformification the answer?

Banks in every sector are focused on delivering information from their data. The survey found that 80% of respondents aspire to exchange, integrate and leverage underutilized data sitting siloed inside their enterprise’s legacy applications. Platformification is a trend within banking that offers the opportunity to unlock the siloes of data held internally and externally, making this information available to the bank and its partners.

The Finextra survey found that the majority of banks were already using AI and APIs, automation, and information governance to embrace platformification.

Utilizing AI and API automation to embrace platformification

As the importance of information readiness in the banking sector grows, the shift towards platformification is gaining pace and, according to Finextra, it offers banks worldwide much more beside greatly improved data management.

The report states: “By offering themselves as a platform for accessing not only their own banking services, but a range of other tools and services from partners, banks can maintain the important primary customer relationship while increasing convenience for the customer.”

Simon Masterman, Worldwide Financial Services lead at OpenText, agrees but sounds a note of caution. He says: “Opening up our customers information to Artificial Intelligence platforms should only be done in the information is governed correctly, so that the benefit driven from the insights gained are primarily for the good of the customer and to ensure that we are not opening ourselves up to risk from new and ever-changing regulations.”

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Monica Hovsepian

Monica Hovsepian is the Global Industry Strategist for Financial Services at OpenText. With more than two decades of financial industry experience, Monica has become a trusted subject matter expert in the Financial Services Industry, having worked with numerous large and international banks in North America, Europe and Asia.

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