Consider this scenario – It’s the wee hours of a bright sunny day, and a Partner at a leading Investment Bank is getting ready to grapple through his routine. Suddenly, news of Microsoft acquiring Github hits mainstream media and before he can assimilate deal ideas for his team to evaluate, he immediately gets rung by his equivalent at IBM to discuss potential acquisition options for them as a business. Now, the partner can either rely on his personal networks or the bank’s industry knowledge to give a response. However, if the deal falls outside of his industry of expertise, the Partner will find it very difficult to respond to the request. One of two things is likely to happen, the Partner will either lose an opportunity to generate business, or his team will end up hustling over the weekend to quickly hack a response together. In any case, credibility of fast-tracked responses are anybody’s guess.
However, what if the Partner always had someone on his team who can –
- Credibly build domain expertise across sectors,
- Continuously scan the industry trends from variety of sources and provide him and his team with easily digestible insights, so they can position themselves as experts to their clients, and
- Predict M&A opportunities that directly contribute to mandate sourcing, so that he does not have to only rely on his personal networks to generate business.
Introducing Kognetics of the many competencies Kognetics has built over years, including being an Artificial Intelligence (AI) Platform for Investment Banks, few of the things it does best is to continuously scan the market to –
- Provide industry specific insights at your ‘finger tips’
- Accurately predict M&A opportunities
Kognetics is increasingly making itself an indispensable part of several coverage teams within Investment Banks, bringing a disruptive change to the way M&A leads are generated and driving the Investment Banking 2.0 agenda.
They say the proof is in the pudding, so to test Kognetics, we asked it to look at acquisitions that have happened in last 3 months, in the Technology sector and it used Machine Learning (ML) to generate for us sell-side deal ideas.
Below is a snapshot of the automated output that Kognetics generated. It first looked at the companies which are closest competitors of target companies that were acquired in last 3 months, filtered that to companies that are due for primary buyout and applied its AI algorithms to not only provide us the deal rationale, but also provided potential acquirers for each one of the companies.
Note: The above is a snapshot of all deal ideas and potential acquirers that Kognetics generated.