Diversity in Canadian Venture Capital: Creating Equity

The solution to the female funding gap lies in the widespread adoption of anti-bias AI in the VC funding process.

The Ivey Business Review is a student publication conceived, designed and managed by Honors Business Administration students at the Ivey Business School.

(Venture) Capitalizing on Opportunities

Founded in 1974, the Canadian Venture Capital and Private Equity Association (CVCA) strives to drive innovation in Canada by supporting private capital investors. The CVCA represents more than 270 firms, of which 41 percent are venture capital (VC) firms. The association brings leadership, mentoring, talent management, connections and advice to growing Canadian companies at all stages across diverse sectors. Its work is currently divided into three key segments: policy and advocacy, research and data, and member resources. As part of their research and data work, one of the CVCA’s current focuses is in supporting women and people of colour in the private capital ecosystem. Despite this work, there is still a clear diversity gap in VC, with women and cultural minorities facing additional barriers to funding while having poor representation at the investor level.

Girls Just Want to Have Fun-ding

The path to VC funding involves three general steps: sourcing, pitching, and evaluation. Sourcing includes the initial contact a firm makes with a prospect, which is often facilitated through a mutual contact, investor, portfolio company, or direct outbound research. Once a VC firm believes there may be potential in the prospect, the prospect is asked to pitch, a process that includes a slide deck and/or a live presentation detailing the merits of the opportunity. Following that, the firm performs a thorough evaluation of the selected prospect, including an analysis of their financial history, reputation, and management. While many VC firms believe their process selects the best candidates, the selection process relies heavily on personal judgement and is notoriously prone to bias. 

Unconscious bias is one of many barriers for women which have led to a clear gender gap in investing. In the United States in 2018, less than three percent of VC funding was granted to female-led businesses, despite 40 percent of American businesses being led by women. This mismatch can in part be attributed to the lack of diversity within VC company ranks: currently, over 82 percent of VC firm employees and 89 percent of VC partners are men. 

Additionally, the wide use of personal networks in VC investments leaves too much room for bias and results in missed opportunities for firms. As seen in the 2018 Pitchbook Venture Capital survey, 59 percent of respondents value personal networks as the primary avenue when sourcing prospects. With men constituting the vast majority of today’s VCs, they follow the homophily principle: birds of a feather flock together. These VCs consequently primarily form networks of male founders, networks that female founders and VCs cannot access. 

AI-ming for Transparency

Unbiased funding strategies have also shown merit financially, with gender-balanced portfolios yielding an average of 10 to 20 percent higher returns than traditional funds. According to a study from the UC Davis Graduate School of Management, twenty-five firms with the highest proportion of female leaders produced 74 percent higher ROA and ROE metrics than the average surveyed firm. From a management perspective, funds with gender-diverse leadership teams were also found to outperform their counterparts by an average of 25 percent. Representative and diverse leaders can change decision-making and outcomes in companies, with studies proving there is a clear link between diversity and business performance.

A few players in the industry are turning towards technologynamely artificial intelligence (AI) and predictive analyticsto improve their selection process. Major VC firms like Alphabet’s GV and Georgian Partners have invested millions into proprietary technology aimed at revolutionizing the VC decision-making process. Another impressive player is Canadian lending company Clearbanc. Clearbanc’s solution to eliminate bias in the VC selection process involves using artificial intelligence to enable more data-driven decision-making. Clearbanc founder Michelle Romanow explains, “We use artificial intelligence to figure out effectively the same type of diligence that VCs used to do.” Clearbanc’s use of technology allows it to strictly focus on financial and operational metrics. As a result, Clearbanc funds eight times more women-led businesses than the industry average. Instead of focusing on soft aspects of startups like the founders or company stories, these initiatives help eliminate human bias by providing objectivity in evaluating a business’s potential.

Which Way? Data-Way

To successfully achieve greater diversity and inclusion in private capital while also achieving better returns, VCs must collaborate to tackle existing biases in sourcing and business evaluation. As the voice of the Canadian VC industry and an advocate for diversity, the CVCA should look to champion technological tools to help drive this change in Canada. Specifically, the CVCA should provide technology-driven tools for diverse sourcing and inclusive deal evaluation for the firms in their network. The tools should focus on equitable sourcing through standardized online outreach and predictive analytics for less biased evaluation.

Seed-ing Change

To reduce bias in the venture evaluation process, one key step that should be taken is relying less on subjective stories and more on quantifiable financial metrics. With the help of data analytics, VC firms can identify top funding opportunities based on objective criteria like financial growth metrics, customer acquisition trends, and other performance measures. After collecting this information, VC firms can then apply predictive modelling in their evaluation process to single out the most probable success stories. 

Case Study: Honing in on the Opportunity

The high-touch nature of the VC funding processes means a full transition to a data-driven approach would require clear financial incentive. A process like this prevents over-reliance on factors affected by bias, like the founding story, and instead focuses on measurable financial success. In the past, Hone Capital, a Silicon Valley-based VC firm, garnered wide attention when it partnered with AngelList, a startup employment website, to create a machine learning model that assesses a startup’s chances of succeeding. With this model, Hone Capital was 2.5 times more accurate than the industry in predicting the likelihood of a business succeeding in a follow-on round of funding. Not only would a more widespread acceptance of data-driven decision-making help reduce unconscious bias, but it is also a lucrative financial opportunity.

Establishing Fund-amentals

CVCA should aim to create a similar machine learning model to Hone Capital’s, but aggregate data from all participating VC funds in its network. The model would receive financial data from participating CVCA partners’ prospective investments and measure the actual returns at exit. Not only would the model remove the bias inherent in current VC investment processes, but it would also likely be widely adopted throughout CVCA if it amplifies returns. Effectively, CVCA can provide a standardized analysis tool that partners can use to improve diversity efforts while boosting returns.

When conducting research to develop their tools, the CVCA should first aim to better understand the nature of predictive modelling. Given that its member firms each have different capabilities, the CVCA should provide accessible tools without high barriers to usage. They should partner with researchers from neighbouring academic institutions such as the University of Waterloo to evaluate how predictive modelling has been used in the past in an investing context. Alongside an academic institution, they should also consider forging external partnerships with AngelList or other equity crowdfunding platforms to collect data on which metrics actually help predict startup success. 

The CVCA should make its screening tools and machine-learning models accessible to all members. This would contribute to breaking the glass ceiling female VCs traditionally face: namely, the network and resources required to succeed through traditional venture capital processes. As a knock-on effect of having more women VCs, women-led startups will also be more likely to receive funding. Across Seed and Series A stage investments, female VCs are twice as likely to back female founding teams. This is further amplified in the startup ecosystem by women-led founding teams being six times more likely to hire female employees.

These tools will be used in collaboration with the current code of conduct CVCA offers for industry standards on diversity and inclusion. VCs who use this tool and surpass the benchmarks will receive an additional certification and access to the network of global investors seeking to capture the business benefits of inclusion. The reasoning for this is twofold: firstly, external investors are increasingly seeing the need for unbiased and diverse funding; secondly, female and minority entrepreneurs are encouraged to work with firms and investors who are operating with unbiased tools. Female and minority entrepreneurs who see this information can feel more encouraged to get in touch with these VCs who are evaluating on unbiased standards.

Empow-her-ing the Future

As a result of systematic biases in the VC industry, there is an evident gender gap with disproportionately lower numbers of female entrepreneurs receiving funding and female investors making funding decisions. Subsequently, fund performance has widely fallen short of its potential and diverse entrepreneurs have missed out on valuable funding opportunities. Bringing objectivity to the funding process through predictive analytics will increase returns for VC firms while empowering diverse founders. Furthermore, an increase in support and resources for female leaders in the venture ecosystems will ultimately level the playing field for women founders. By implementing systemic change at the source of the capital, the CVCA can start a cycle of change leading to elevated levels of equality in Canadian startups. Ultimately, in an expanding marketplace of budding entrepreneurs, the VC industry needs to embrace diversity and inclusion to create better outcomes for inventors and entrepreneurs alike.


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