Cognitive biases are ‘hard wired’ within us and all humans, including investors, are liable to take shortcuts and sometimes oversimplify complex decisions. As such, the traditional route of pitching to VCs for funding a la Shark-Tank-style would mean that the startup is subjected to the inherent biases of the investors. 

First, let us understand some of the most common types of biases and explore how they can impact investment decisions.

Overoptimism bias can lead to overinvestment in the wrong projects

This is a type of cognitive bias that causes someone to believe that they themselves are less likely to experience a negative event. As a result of this bias, investors tend to equate better impressions with higher returns and their assessment of projects are overoptimistic hence they end up indulging in overinvestment. 

Jenfi discusses the relationship between digital banks and fintechs

According to a study conducted in Pakistan, the impact of optimism bias on investment decisions is seen to be significant. One good example of this can be the WeWork debacle. The co-working startup went from unicorn status with a nearly US$50 billion valuation to a cautionary tale for gullible investors worth just $8 billion in a matter of few months. The charismatic WeWork founder Adam Neumann pitched the culture of “We” and promised to help “change the world” and investors like SoftBank and JPMorgan bought in. 

In the aftermath of Uber, Morgan Stanley’s head tech banker Michael Grimes, who has been called “Wall Street’s Silicon Valley whisperer” said, “It’s really easy to be a pundit and say, ‘It should be higher’ or ‘It should be lower,’ but investors are making decisions about that every day.”

Gender bias or the Cupcake Stigma is holding women entrepreneurs back

Yet another bias that corrupts investment decisions quite often is the gender bias also known as the Cupcake Stigma. There is a general perception that women are less serious in their business ventures than the typical male entrepreneur. This stigma is reinforced by VC funding decisions, which are made mostly by men and thus based primarily on heuristics derived from men. According to an MIT Sloan article, less than 10% of decision-makers at VC firms are women and 74% of U.S. VC firms have no female investors at all. In Southeast Asia, 76% of venture capital funds do not have any women in decision-making roles. Globally, only 2.4% of fund managers are women. In 2020, startups in the region raised $8.6 billion, out of which only 16.5% went to women-led firms. 

This bias exists despite the availability of mounting data, which suggests that greater returns are generated by female founders. According to a study by Boston Consulting Group, for every dollar of funding, women-led startups generate a return of 78 cents, while male-founded startups generate less than half that, at just 31 cents. 

As a result of the cupcake stigma, women in business continue to be overlooked by traditional venture capitalists. This is where artificial intelligence and algorithms can be leveraged to eliminate these biases and help investors make better decisions.

Leveraging AI to eliminate biases in investment decisions

In the pursuit of eliminating biases and democratizing the funding landscape, computational statistical methods become particularly valuable due to advances in machine learning and artificial intelligence. Statistical modelling can allow investors to make highly accurate and consistent predictions in the context of financing decisions and return by identifying patterns in the prior distribution of data and thus predict future events. It eliminates human biases and leads to an untainted decision based purely on data.

Statistical models are unbiased, free of social or affective contingence, consistently integrate empirical evidence and weigh them optimally and they are not constrained by cognitive resource limitations. Consequently, machine intelligence is a suitable approach for making a statistical inference based on prior data and they can learn as the data input grows

According to a US-based revenue-based funding firm, Clearbanc’s use of AI to review financial and marketing data was successful in removing the bias of traditional VC funding, resulting in 8 times more finance capital going to female founders on the platform than the industry average. For Singapore-based fintech Jenfi that provides revenue-based financing to rapidly growing businesses in Asia, approximately 30% of their total deals funded have been to a female founder or director.

Jenfi: Relying on tangible metrics and data for unbiased investment decisions

Singapore-based Jenfi is helping address the investor bias in Southeast Asia having worked with more than a thousand eCommerce and digital businesses across the region. Unlike traditional investors, Jenfi uses tangible metrics to measure a business’ productivity of growth and is able to provide additional capital when businesses are at an inflexion point and primed to take off. Their credit underwriting and automated screening remove the human bias by evaluating businesses based on quantitative measures. With an applied data science approach, Jenfi is able to fund with an “agnostic lens” looking purely at the metrics that matter the most.

For instance, Jenfi is automatically integrated into real-time data sources. This includes payment processors like Braintree and Stripe, merchant platforms, such as Shopify, Lazada and Shopee, and digital advertising platforms like Facebook and Google, to predict sales and marketing efficiency, eliminating the need for qualitative inputs. Jenfi’s AI reviews the applicant’s inventory, marketing and revenue data, return on ad spend, and unit economics to automate the diligence process. The fast decision-making process enables them to raise the applicant up to three eligible offers within 24 hours.

With Jenfi, the average company experiences a significant amount of compounded growth over time: more than +26.5% in a period of 3 months, over 60.0% in 6 months and over 156% in 12 months time. The aggregate sales of companies funded are at  more than USD$ 27 million and Jenfi believes their capital will help companies generate an incremental around USD$ 44 million (156%) in revenue in over 12 months.

With startups like Jenfi tapping into data and leveraging the latest technologies like AI and machine learning to help eliminate biases from the investment scene, a better, more democratic funding landscape should be possible in near future. 

This article was contributed by Jeffrey Liu, co-founder and CEO of Jenfi

About the author

Jeffrey is one half of the dynamic duo who started Jenfi, a financial technology company that provides revenue-based financing to rapidly growing businesses in Asia. Jenfi aims to assist digitally-enabled businesses, such as e-Commerce ventures and high growth startups, to accelerate their sales velocity by funding their marketing, inventory, and growth campaigns.

Jeff brings a strong operational and financial background from GuavaPass where he served as the Co-founder & CEO. Prior to that, he helmed the role of Head of Corporate Development at BeachMint, a venture-backed social e-commerce company in Santa Monica (founded by the Co-founder of MySpace). During his time there, he ran the company’s Business Intelligence and Analytics divisions where he forefronted the merger between BeachMint and Lucky Magazine, a Conde Nast subsidiary, forming The Lucky Group.

Prior to this, he helped launch a hedge fund in Singapore; worked as an investment associate at credit and special opportunities hedge fund in Chicago; and as an investment banker at Lehman Brothers. Jeffrey graduated with an MBA from the University of Chicago Booth School of Business and received a BS in Industrial Engineering with an additional major in Economics at Northwestern University.