Investors are an integral part of any business landscape and their decisions can make or break not just companies but entire industries. However, the fact remains that investors are also human beings. They cannot always be rational- they have their limits, and can easily be influenced by their own biases.
As such, the traditional route of pitching to investors for funding would mean that companies are subjected to an inherent bias more often than not.
Common types of biases and how they impact investment decisions
- Over-optimism bias makes investors gullible
Over-optimism is a type of cognitive bias that causes people to believe that they are less likely to experience a negative event and under the influence of this bias, investors tend to equate better impressions with higher returns. This leads to a poor or rather misguided assessment of projects ending in overinvestment in the wrong projects.
According to behavioural finance studies, investors that are crippled with the optimism bias almost live in a bubble of different market realities and thus, the impact of this bias on investment decisions is seen to be significant.
One accurate example of this can be WeWork. In just a matter of a few months, the co-working startup went from unicorn status with a US$50 billion valuation to a cautionary tale worth just $8 billion. Adam Neumann, the charismatic WeWork founder promised to help “change the world” and gullible investors like SoftBank and JPMorgan bought in.
In the aftermath of Uber, Morgan Stanley’s head tech banker Michael Grimes, who is often called a Silicon Valley whisperer to Wall Street, 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 holds women entrepreneurs back:
This is yet another bias that corrupts investment decisions quite often. Among investors, there is a general perception that women are less serious in their business ventures. In a male-dominated VC scene, this stigma is reinforced by funding decisions that are based primarily on heuristics derived from men. Did you know that less than 10% of decision makers at VC firms are women and around 74% of U.S. investment firms have no female investors at all?
In Southeast Asia, over 76% of VC firms do not have women in decision making role, and around the world, only 2.4% of fund managers are women. Last year, Southeast Asian startups raised $8.6 billion. out of which merely 16.5% went to women-led firms.
This bias exists despite data suggesting that greater returns are generated by female entrepreneurs. According to a study, for every dollar of funding, women-led startups generate a return of 78 cents, whereas, at just 31 cents, male-founded startups generate less than half that.
This is where artificial intelligence (AI) and algorithms can be leveraged to help investors make better decisions.
How AI can eliminate biases
When it comes to eliminating biases and democratising the funding landscape, due to advances in machine learning and artificial intelligence, computational statistical methods become particularly valuable. Statistical modelling can enable VCs to make highly consistent predictions for financing decisions. It can help to identify patterns in the prior distribution of data and thus predict future events accurately, leading to an untainted decision based purely on data.
Statistical models consistently integrate empirical evidence and weigh these proof points optimally. Consequently, machine intelligence is a suitable approach for making a statistical inference based on existing data and they have the ability to learn as the data input grows.
Clearbanc, a US-based revenue-based funding firm claims that by using AI to review financial and marketing data, they were 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.
At Jenfi, we look to address the investor bias in Southeast Asia by using tangible metrics to measure a business’ productivity of growth. We rely on credit underwriting and automated screening to remove human biases by evaluating businesses based on quantitative measures.
For example our platform is automatically integrated into real-time data sources, including payment processors like Stripe and Braintree, merchant platforms like Shopify, Lazada and Shopee, as well as digital advertising platforms like Facebook and Google. This helps predict sales and marketing efficiency and eliminates the need for qualitative inputs. It also helps eliminate human biases and expedites the process, enabling them to raise the applicant up to three eligible offers within 24 hours.
With more companies tapping into data and utilising the latest technologies like AI and machine learning to help eliminate biases from the funding scene, a better, more democratic investment landscape should be possible in near future.
Jeffrey Liu is CEO and co-founder of Jenfi