Now Financial Models are Going to be More Powerful
In this chart published by the World Economic Forum, the yellow dots indicate areas where more powerful modelling will lead to more powerful results.
Finance is most associated with numbers – a form of structured, or easily organized data. But there’s an art to the wide umbrella of finance as well, in complex decision making, value calculations, strategic analyses… all of this takes the form of unstructured data. Unstructured data is notoriously difficult to quantify, and unfortunately this means it’s given up on or simply ignored.
McKinsey and company states “About a third of the opportunity in finance can be captured using basic task automation technologies such as RPA… capturing the remainder of the opportunity requires advanced cognitive-automation technologies, like machine-learning algorithms and natural language tools”.
In our experience, this has been true. Our clients in finance face a unique set of challenges. Disruption from digital-only banks has spurred an arms race to digitize product offerings, and consumer demand for personalized services has never been higher. At the same time, only the highest standards in security and transparency are acceptable, and financial advisors must be both accurate and cost-effective to win business. Only through some massive technology overhauls will much of this be possible.
The World Economic Forum’s 2018 report The New Physics of Financial Services hails AI as a “critical aspect of the Fourth Industrial Revolution”, asserting that “the very fabric of the financial services ecosystem has entered a period of reorganization, catalysed in large part by the capabilities and requirements of AI”.
At Coseer, we see a gap in actionable information available around the specific AI technologies that are best suited to Finance. We took the WEF’s report to heart, diving deep into each of the 60 use cases across six verticals in finance that will attract $10B in investment through financial institutions by 2021. Staggeringly, we found that 68% of these use cases will fail to achieve their full potential if the financial modelling does not include insight from unstructured data.
We are at the tipping point; our clients have told us that above all else, finance requires:
- Better search
- Actionable insights
- Robust client confidentiality controls.
Finance firms can get 1/3rd of the way there using simple automation tools – but that elusive 2/3rd can only be captured if unstructured data is harnessed. Luckily, Natural Language Search is more than ready to handle the job.
Read our case study and learn more about why NLS is the key to unlock the true value of finance data.
