Finquest: Data Intelligent M&A Network

A data-intelligent network wherein institutional investors can connect with the exact mid-size companies they are looking for to contribute their precious money and time!—sounds quite groundbreaking! Finquest, a Singapore-based firm has built this very network for the mid-size market with a revenue range between 10 to 200 million dollars. Tanguy Lesselin, CEO of Finquest, explains the necessity of such an innovative network for this vast mid-sized market, not only for investors but also for the companies. He says—“Larger corporations are already on the radar screen. Mid-size companies, on the other hand, are much harder to identify as potential investment opportunities. Finquest removes the inefficiency of the match-making in the mid-size space, and exposes company CEOs to growth capital and exit opportunities.”

Though the mid-size market provides attractive investment opportunities, there is, however, a significant drawback that has positioned the sector, in reality, to remain “comparatively under-penetrated by institutional capital, and under-served by banks and global financial intermediaries.” He goes on to say, “Private company data rarely includes financial and revenue numbers so for investors or strategic buyers it is difficult to identify the targets, and that is where we come into play.” True to his words, this is precisely where Finquest’s novel intelligent data platform fits in. With an exhaustive and expanding database of over 1.4 million organizations that is enriched continuously, the platform analyzes and connects an investor client with the exact companies they would be interested in having a deal with.

Using machine learning, big data and artificial intelligence, the platform connects institutional investors with mid-sized companies that suit their investment criteria.

Lesselin provides a simple overview of how the model operates. A client, looking for an investment opportunity, first subscribes to the platform and inputs their professional details, business needs, and search parameters (sector, geography, deal size, revenue etc,). Once successfully registered, the platform’s data-based machine learning algorithms provide an ‘opportunity map’ that exhibits the universe of potential targets —all the companies or counterparties meeting the client’s criteria in the region of interest. The curation phase kicks in next establishing a seamless connection between the counterparties and the client. “As much as 95 percent of these recommended connections eventually get accepted by the client which profoundly highlights the quality of our breakthrough platform in targeting the right companies that investors need,” remarks Lesselin. The platform also ensures that these deal information are not visible to preserve the high confidentiality of the process that clients in this sector usually demand.

The business model is subscription based. “We charge a fee based on the number of “business requests” as we call them that our clients want us to work on. The target transaction size is the second variable that determines the fee” informs Lesselin.
The competency of Finquest’s unique subscription-based, intelligent platform has driven the company to boast an extensive client portfolio. “We have clients that are typically private equity funds, investment holdings, family offices, all of them looking for investment opportunities. We also have investment banks and M&A advisors that have sell-side mandates from company clients” says the industry veteran. For instance, a food and beverage company that commanded a chain of restaurants across South East Asia was looking for a potential exit. Finquest was able to generate over 15 investor and corporate buyer connections, thereby significantly expanding the exit opportunities for the company.

The USP of Finquest lies in the profoundly right connections which reflect the firm’s focus on data relevancy and intelligence in addition to just data accumulation. In the future, Finquest will continue to strengthen its platform's data intelligence, deep learning, and predictive capabilities, and hence, capture more of the global market in the process.