The last decade or so has seen a number of technology waves, such as the cloud and mobile, that have really transformed Financial Services (FS), changing the way banking is done forever. But perhaps the biggest impact from technology is yet to come. Artifical Intelligence (AI) is a technology term that has been much debated and discussed, especially over the last 18 months, but it is yet to truly show its full potential.
In fact, technology is a sector that has often been prone of over-hyping the latest trends that emerge within it. The fact that industry analyst group Gartner calls one of its market analyses the ‘Hype Cycle’ further highlights the tech industry’s propensity for hype, and AI certainly falls into that category. Despite the media attention how many firms within FS can honestly say that they are deploying AI within their organisation and seeing genuine business benefits from it?
But AI certainly comes with enormous potential. It can offer an FS firm incredible insight and analytic power, and is a technology on the radar of the world’s biggest and smartest businesses. A recent (July 2017) report from the McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier, revealed that tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment.So how can FS organisations make best use of AI and ensure it truly delivers on its potential?
The one thing that can really help AI emerge from the hype, is simple – data. FS organisations hold more data than ever before, and thanks to the Internet of Things and the connected world we live and work, this data is growing all the time.
Used effectively, data can deliver unparalleled insight into an FS firm’s customers and more, but it has been a challenge until now for organisations to manage this data in a way that enables easy extraction of actionable insight. The answer lies with AI and machine learning (ML).
But before AI and ML can get to work, a big issue for many organisations, is the manner in which data is stored. It isn’t uncommon for a bank to store and manage data in multiple CRM platforms, as well as a number of other repositories – ERP, other databases, on the server and on desktops – across the organisation.
This can arise through M&A activity, expansion or simply inefficient managing of IT resources, but the end result is the same – data held in many siloes. This makes the management and analysis of this data a much harder job than it need be. If you cannot access data than how can you be expected to draw insight from it?
Unstructured data in FS
Furthermore, the sheer volume of big data in modern FS organisations can be bewildering, and it comes in files and formats that most CRM systems are unable to manage effectively. Unfortunately, this data is often the most valuable, containing rich insight into aparticular customer and their specific needs and requirements.
This unstructured data includes: any social content – Twitter, Facebook, LinkedIn, Instagram – by, and relating to that customer; email conversations between the customer and bank; service call scripts that detail any recent or historical issues, and much more besides that doesn’t into the formats used by most CRM systems.
By not deploying unstructured data within a CRM, it can potentially be a major problem in FS, with huge swathes of potential customer insight missing. Utilising technology that captures both structured data in siloes and the masses of unstructured data, means FS organisations can really begin to benefit from AI.
How AI can benefit FS
Used properly, AIcan have a genuine and tangible impact on any business and its customers.For instance, enterprise search is an area that is crying out for the use of AI and ML. Knowledge workers in FS are spending too long searching for information that might not even be filed where they are searching for it.
Using an AI-based cognitive system allows much more complex search queries and can even make relevant and contextual suggestions back to the user. This is AI effectively understanding better than the user what that user is looking for, saving time on searches and delivering better results.
AI can also deepen customer understanding. An issue for larger FS firms is knowing who holds which relationships at a particular customer. A customer may have been contacted by many different individuals, in different parts of the business and even the world.
AI technology can look at mases of unstructured data – all emails sent to an organisation, from and to different people, in different departments and locations – and provide a way for a user to take meaningful action with a customer. This outlines clearly and in real-time who knows who within an organisation, invaluable when enterprise relationships can be so complex.
These are just two examples – the potential of AI can go much further than that and over the next few years will transform many elements of FS. But the key to taking AI beyond the hype lies with data, that’s where the potential is at its richest. By deploying AI and ML, organisationsin FS can collect data from multiple sources and in multiple formats, extracting fresh and insightful meaning from it and helping to deliver a complete view of that customer.
Dorian Selz is CEO and co-founder of contextual insight firm, Squirro