About the company
Industry: Financial services | Team size: 30 | Established in: 2001 | Headquarters: Bengaluru
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.
Background: 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:
- Exploring brokerage firm reports sent only via email
- Tapping into tips from a private network of journalists, senior executives, and other industry experts
- Scanning PDFs, such as notices and circulars, from exchange websites like BSE
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.
A centralized, cloud-based workspace to organize, search and access all information in real-time
The best way to spot investment patterns and opportunities quickly is to set up a centralized information management and collaboration system that:
- Automatically classifies and organizes data
- Simplified search and information discovery
- Enabled seamless information sharing and collaboration
How Needl.ai helped research analysts start their days with less noise
The research analysts wanted a mechanism that made it possible to start their day with less noise and more focus.
So, they chose Needl.ai to set up an intelligent, real-time information repository that offered:
1. Faster research and time-to-insight
Once the hedge fund set up Needl.ai, the analysts no longer had to juggle between countless open tabs, apps, and documents.
Needl.ai integrated and automatically organized all public and private data – personal notes, emails, Telegram and WhatsApp chats, RSS feeds, podcasts, company websites and news sites.
As a result, the research analysts could:
- Speed up the research process and cut down on the noise with customized and curated data feeds
- Cut down the time spent organizing information with automatic data tagging and sorting, irrespective of the source
- Analyze information at a glance with auto-summaries of lengthy conference calls
- Put together essential insights by clipping relevant information – tables, texts, and charts – from PDFs and maintaining them as editable Word or Excel documents on Needl.ai
Since Needl.ai is a cloud-based platform, all the information stored in Needl.ai is automatically indexed and readily available to everyone with the right access. The investment bank eliminated manual tasks in organizing research data and sped up the entire reporting process.
2. Deep search of data from public and private sources
Needl.ai supports deep search of data – audio files, images, PDFs, and tables in Excel sheets. More importantly, Needl.ai assimilated data from index announcement websites – filings, annual or quarterly earnings reports, presentations, and AGM call transcripts.
So, analysts could sift through these data sources using advanced filters to find valuable insights quickly, without wasting hours downloading and scanning them manually.
As a result, the analysts could cut down on time spent looking for information and organizing it by 35%.
3. Easy collaboration and sharing
Sharing information was also a breeze. Analysts could store all their research on the cloud using Needl.ai, eliminating data silos and making information accessible to everyone. They could share data with merely a link or via their communication platform of choice.
In-built collaboration tools allowed the right people to access the information they need, when they need it and add notes or comments, assign tasks, and tag the relevant stakeholders – all in one place.
As a result, the research analysts could instantly share and collaborate on research without having to switch multiple platforms.
Staying on top of global economic trends, industry-related news, company data and announcements, etc., in real-time became a reality as Needl.ai processed three million unique data points from structured and unstructured data sources daily.
In addition, the hedge fund witnessed:
1. A 35% reduction in time spent on non-core activities such as data gathering, organization, sorting, and sharing
2. Faster and effortless discussions around pitch ideas, stock bets, and buy/sell decisions with the fund manager
3. An increase in performance-based annual bonuses, thanks to more accurate stock bets and investment pitches
4. Complete elimination of manual processes in research and analysis, thanks to automated classification of information along with auto-summaries
5. Over 50% decrease in time-to-insight because of a 360-degree view of company assets, seamless information exchange, and more via a single platform with Google-like search and advanced filters
Do you want to optimize your research process and make faster, more meaningful investment decisions?
Then, take Needl.ai for a spin and see how you can cut down on non-core activities, eliminate grunt work, and focus on delivering insightful investment recommendations.