Machine learning brings us a step closer to humanizing responses for user queries. The search interface available on traditional platforms, websites does not meet the speedy, accurate and personalized results. Moreover, the search becomes a lot more cumbersome when it comes to enterprise. The various data sources, structure and legacy applications hamper internal searches.
Current challenges with Asset Management Companies (AMC):
The asset management industry is at a critical juncture in its history. After a decade in which fee compression and the growth of index investing have driven sweeping change, these shifting economics are now converging with the relentless advance of regulation and disruptive technological breakthroughs. In response, asset managers are investing in innovation and reinvigorating their products and processes. Industry pundits point to a set of six challenges asset managers need to address:
- The shift from active to passive investing
- Demographic changes
- Shifts in demand
- Changing regulations
In big house AMC’s, most enterprise data are unstructured. such data manifests as a huge variety and volume of documents making it difficult to search and extract such information needed by different departments such as marketing, sales, compliance & auditing and finance. Most of this unstructured data is spread across organizational silos, to search among them by traditional methods with keyword-based search systems that require business users figuring out the right combination of keywords, and usually return many hits, most of them irrelevant to our query. Huge time spent on information search negatively impacts business productivity, ability to quickly take decisions and growth.