The customer is a mid-market investment bank in Bengaluru focusing exclusively on mergers, acquisitions, and financial advisory to public and private companies. The bank’s clientele include an extensive roster of large corporations in India.
The bank specializes in offering value-added advisory services to help its clients understand and set up effective transaction strategies. It has two core teams – asset management and wealth advisory.
A need to spot time-sensitive opportunities instantly
The front client engagement team and asset managers depend on the research analysts to offer sound advice on potential assets and opportunities.
This required granular research into the companies monitored, going through large volumes of public and private sources of information.
To this end, the research analysts constantly looked for new data sources to mine and extract insights measuring investor sentiment accurately. Some of these sources included:
It was challenging to read through all that data – usually time-sensitive – from various sources. The analysts required a tool that helped them synchronize all of these sources and scale their data management efforts.
Such a tool would help them with the following:
1. Organizing data automatically
The research analysts spent 35% of their time manually classifying, editing and updating their positions on stocks. Moreover, researching a single company took anywhere from 1-3 weeks.
This process involved going through 100-page documents to understand specific circumstances influencing a company’s stock, clarifying ambiguous explanations, and then preparing investment-related recommendations.
Hours of grunt work, manual observation, and recording can be tedious, leading to missed opportunities or patterns.
2. Building an institutional memory
A large part of the rationale behind investment decisions and recommendations stayed with individual analysts. If they were to leave the hedge fund, all that institutional memory would be lost.
Since there was no central repository for all investment-related information, losing institutional knowledge was a top concern.
3. Collaborating and sharing information easily
After noticing something important, the analysts had to document their take, attach all relevant documents to an email, and add a note on their analysis before sharing it with others.
Additionally, discussions around data required multiple calls with stakeholders across teams, and until everyone was updated and on board, no decisions were made. This practice needed to be more scalable for a rapidly expanding investment bank.