This is the third post of our series on bank and unstructured data analytics that we presented at Qonnections 2014.
In the first two we detailed the current state of affairs whereby 82% of financial services do not combine structured and unstructured data and explained that while unstructured data analytics is still not adopted as a standard, banking executive understand they can extract tremendous value from such solutions.
Today we look in more details at what exact value banks want to extract and how does it translate into use cases.
Figure 1 details the values executive are looking to extract from unstructured data. Not surprising, given the current context, the number one and two concerns are improving risk and compliance monitoring and enhancing fraud detection, which together represent nearly half of the value banks believe they can extract from unstructured data.
Let’s focus on risk and compliance monitoring.
The main challenges are administrative cost control, risk mitigation, malpractices, suspicious conduct, fake transactions and data protection compliance.
Regulatory framework violations can often be traced back to e-mail and chat communication. There is therefore a growing need for highly efficient solutions which analyses these communication channels clusters messages into topics and easily understand communication relationships and patterns within those channels.
This is where the greatest value for unstructured data analytics solutions lies.By understanding regulatory frameworks at a conceptual level solutions like Squirro can spot irregular activities that would not be detected through simple keyword searches.
In the coming weeks we will publish the full detailed research on financial services and unstructured data analytics. If you want to get a copy, please send you request at firstname.lastname@example.org.