Data and information are at the heart of modern Financial Services (FS), empowering knowledge workers with the insight they need to do their job effectively and efficiently, improving the customer experience, increasing retention and having a powerful impact on the bottom line.
Or at least, that's how it should work in theory. The practice is rather different. While most FS firms hold ever-growing volumes of data on their customers and the markets they operate in, relatively few of them are able to extract the desired insight from that data.
One of the key reasons for this is because data is stored in so many different systems and silos across a business. This means that even locating information is a challenge, let alone mining it for meaningful insight. A recent McKinsey report has found that employees spend on average 1.8 hours every single day -- about nine hours a week -- searching for and gathering information. This is clearly untenable and is causing a major blockage in how FS firms operate.
The problem is that enterprise search is based on technology from decades ago, and is now highly unsuited for the way modern FS companies work. How has enterprise search grown so unfit for purpose, and how can FS workers make better use of cognitive technology to actually find the information they are searching for?
The rise of unstructured data
Only a few years ago, most content and information in FS came in just a few simple formats. Now in 2018, there are more file types and formats than ever before, and much of this is 'unstructured data', meaning it is not easily recognised and filed by CRM platforms and other enterprise systems.
For the FS sector, unstructured data can include data sources such as: agent / client correspondence (whether email or call transcripts); news articles relating to clients and the industries they operate in; earnings call transcripts and presentations; and premium data sources, such as Thomson Reuters or Bloomberg.
Users could sometimes be hunting for information that their enterprise does not even know it has. Furthermore, the way in which people search for information at work is changing rapidly. Instead of actively searching for something, we're starting to let the computer, website or AI personality anticipate what we want and give it to us proactively, as is the way with a number of consumer applications.
Smarter approaches to enterprise search
Unstructured data is on the up, causing problems since data entry cannot be automated.
(Image: Mika Baumeister, Unsplash)
So FS firms need to better equip their teams to find the information needed to do their jobs effectively. That's why Google and Microsoft have launched enterprise versions of Google Search and Bing, but these are still not fit for purpose in modern FS.
The principal elements of search were actually mostly developed in the 1980s, but took a long time to come to market. Searching through customer data, industry news, and analytics reports just isn't an efficient use of company time. Intelligent recommendations are the new search results, based on previous behaviours and likely intent.
Search provides isolated pieces of data, but it won't provide the AI engine that delivers automatic context, insights, and next steps. Smart FS workers shouldn't search for information to make decisions on client business: they should just make decisions based on data that they already have in front of them at all times.
The future of search in FS is linked directly to the emergence of cognitive computing, which will provide the framework for a new era of cognitive search. This recognises intent and interest and provides structure to the content, capturing more accurately what is contained within the text.
Context is king, and the four key elements of context detection are as follows:
-- which user is looking for information? What have they looked for previously and what are they likely to be interested in finding in future? Who the individual is key as to what results are delivered to them.
-- the nature of the information is also highly important. Search has moved on from structured or even unstructured text within documents and web pages. Users may be looking for information in any number of different forms, from data within databases and in formats ranging from video and audio, to images and data collected from the internet-of-things (IOT).
-- the timing of the search itself, or the date / time that the information was created will both influence the relevancy and accuracy of results.
-- the location of the user and also of the information -- on-premise, in the cloud, within a database, contained in social media -- make up the fourth element of the context that is such an integral part of cognitive search.
Data and context is actively transforming the financial services industry.
(Image: rawpixel, Unsplash)
Many people still think of search as putting words in a box, but this is hugely limiting. Modern search should include contextualisation of a user's intent and interest, as well as recommendations as to the next best action to meet the user's intent. Instead of the user looking for the most relevant information, it comes to them automatically by cognitively understanding their interests and matching this with the data available.
Traditional approaches to search have passed their sell-by-date and do not reflect modern business. Context is everything and FS firms that persist with older methods could see their own organisation grow as inefficient and ineffective as the search engines they continue to provide for their staff and customers.
— Dr. Dorian Selz, CEO, Squirro