The future of venture capital decision making : the impact of quantitative sourcing and machine learning on the VC Investment process

Investing in early-stage startups is a difficult endeavor. Venture Capitalists use heuristics and base their decisions on past experiences, which can lead to biases. Recently, Venture Capitalists are increasingly using artificial intelligence and quantitative sourcing to support their investment process, while others still rely on traditional investment mechanisms. This research investigates the usage and impact of artificial intelligence and machine learning throughout the venture investment cycle to make investment decisions. This dissertation is an exploratory study that employs a qualitative research approach in the form of semi-structured interviews with ten European Venture Capitalists. The results show that Venture Capitalists utilize machine learning and web scraper tools, particularly during the deal origination, firm-specific screening, and general screening stages of the investment process, to solve the identification and selection challenges. As a result, investment processes become more efficient and less biased, allowing for more time to be spent advising and mentoring portfolio startups. It adds to the existing literature on how artificial intelligence and data can augment existing investment mechanisms during the venture capital decision-making process.Investir em startups na sua fase inicial exige um elevado empenho. Os investidores de capital de risco baseiam as suas decisões em pesquisa e experiências passadas, o que pode levar a enviesamentos. Embora muitos investidores de capital de risco ainda utilizem mecanismos de investimento tradicionais, tem havido um aumento na utilização de inteligência artificial e sourcing quantitativo para apoiar o processo de investimento. Esta investigação estuda a utilização e impacto da inteligência artificial e de machine learning ao longo do ciclo de investimento de risco para tomar decisões de investimento. Esta dissertação é um estudo empírico que utiliza uma abordagem de investigação qualitativa sob a forma de entrevistas semi-estruturadas com dez empresas de investimento de capital de risco europeias. Os resultados mostram que os investidores de capital de risco utilizam machine learning e ferramentas de recolha de dados na web, em particular durante o início da oportunidade de negócio, a seleção específica da empresa, e fases gerais de análise do processo de investimento, para resolver os desafios de identificação e seleção. Consequentemente, os processos de investimento tornam-se mais eficientes e menos tendenciosos, permitindo que se utilize mais tempo a aconselhar e a orientar as empresas do portfolio. Este estudo complementa a literatura existente relativamente a como a inteligência artificial e os dados podem elevar os mecanismos de investimento existentes durante o processo de tomada de decisão de capital de risco

The impact of artificial intelligence on decision-making in Venture Capital Firms

This exploratory study examines the opportunity of Artificial Intelligence in the decision-making process of Venture Capitals. Investors have to take decisions under uncertainty, time pressure and suffer from bias. This study investigates the potential of Artificial Intelligence to overcome these challenges and improve the process. The results are based on a qualitative analysis based on 12 interviews with Venture Capitals, AI Experts, and companies offering solutions for Venture Capitals as well as secondary data in form of academic articles and online magazines. The findings reveal that Artificial Intelligence is currently mostly implemented at the beginning of the decision-making process. The usage of Artificial Intelligence improves the process of making decisions by lowering uncertainty, bias and increasing productivity and efficiency. The interviews show that that AI can be implemented in every step in the decision-making process and presents the specific use cases. Furthermore, implementation challenges and implications for practice are outlined. By applying AI, Venture Capitals improve their decision-making process, which ultimately could have a positive impact on the return of their portfolio.Este estudo exploratório examina a oportunidade da Inteligência Artificial no processo de tomada de decisão das Capitais de Venture. Os investidores têm que tomar decisões sob incerteza, pressão de tempo e sofrer de parcialidade. Este estudo investiga o potencial da Inteligência Artificial para superar esses desafios e melhorar o processo. Os resultados são baseados em uma análise qualitativa baseada em 12 entrevistas com Venture Capitals, AI Experts e empresas oferecendo soluções para Venture Capitals, bem como dados secundários em forma de artigos acadêmicos e revistas on-line. Os resultados revelam que a Inteligência Artificial atualmente é implementada principalmente no início do processo de tomada de decisão. O uso da Inteligência Artificial melhora o processo de tomada de decisões, diminuindo a incerteza, o viés e aumentando a produtividade e a eficiência. As entrevistas mostram que a IA pode ser implementada em todas as etapas do processo de tomada de decisão e apresenta os casos de uso específicos. Além disso, desafios de implementação e implicações para a prática são delineados. Ao aplicar a inteligência artificial, as empresas de capital de risco melhoram seu processo de tomada de decisão, o que, em última instância, pode ter um impacto positivo no retorno de sua carteira

Domestic Venture Capitalists’ Effect on Early Internationalization in Finnish Startups : A Non-Financial Resource-Based Approach

Venture Capital (VC) industry has grown rapidly both globally and in Finland for the past two decades. During this time, both the total amount of made investments as well as their size has grown considerably. As the industry has grown substantially, also scientific research has shown interest in the topic. Most of the earlier research has focused on the financial intermediary role of VCs, but lately there have been findings that indicate VCs would also carry out considerable amounts of non-financial resource-provisioning for their investments. This resource-provisioning has not been researched much in the context of early internationalization (EI) which provides an interesting perspective on the topic. Simultaneously, Finland possesses several traits as a market that provides a suitable research setting for this specific topic. Hence, this research attempts to understand the effect that domestic VCs’ non-financial resources have on early internationalization in Finnish startups. The purpose of the study is divided into three sub-questions: the managerial resources, the strategic resources and heterogenous provision of these resources. The theoretical background of this research is divided between earlier research on both early internationalization and VC investing. Both of these main themes are combined in the used theoretical model that identifies six distinct resources. The research method used in this research was an extensive case study which comprised of four unique cases that were selected based on requirements set by the earlier EI research. The data was collected from August 2020 to November 2020 by interviewing four VCs from applicable cases through semi-constructed interviews. The results indicate that VCs are in fact in possession of the resources suggested by the model. However, they will not necessarily provide them to all of their investments as was suggested by the model. Instead, they try to optimize their resource-provisioning by investing in capable founders, who are also experienced entrepreneurs and experts in their field. All in all, the interaction between the founders and the VCs were observed to be much more reciprocal than what the initial model suggested. Findings from the observed cases also suggested that more than often the startups start to internationalize already before they receive VC investment which indicates that they do not need the VCs to start their internationalization process, but more to support the ongoing process.Venture Capital (VC) -sijoittaminen on kasvanut voimakkaasti sekä Suomessa että maailmanlaajuisesti viimeisen kahden vuosikymmenen aikana. Tänä aikana sekä tehtyjen sijoitusten kokonaismäärä että sijoitusten keskimääräinen koko on kasvanut huomattavasti. Alan kasvun myötä myös tieteellinen yhteisö on osoittanut kiinnostusta aiheeseen. Suurin osa aikaisemmasta tutkimuksesta on keskittynyt VC-sijoittajien rooliin rahoituksen välittäjinä, mutta viime aikoina useat tulokset ovat osoittaneet, että VC-sijoittajat tarjoaisivat myös huomattavia määriä ei-rahallisia resursseja sijoituksilleen. Tätä resurssitarjontaa ei ole tutkittu lähes ollenkaan aikaisen kansainvälistymisen yhteydessä, mikä tarjoaa mielenkiintoisen näkökulman aiheeseen. Samanaikaisesti, Suomella on markkinana useampi mielenkiintoinen ominaisuus, jotka yhdessä tarjoavat sopivan tutkimusympäristön kyseisen aiheen tutkimiseen. Tämä tutkimus yrittää ymmärtää, mikä vaikutus kotimaisten VC-sijoittajien tarjoamilla ei-rahallisilla resursseilla on aikaisen vaiheen kansainvälistymiseen suomalaisissa startupeissa. Tämän tutkimuksen tavoite on jaettu kolmeen alatavoitteeseen: johdolliset resurssit, strategiset resurssit sekä heterogeeninen resurssien tarjonta. Tämän tutkimuksen teoreettinen tausta on jaettu aikaisen vaiheen kansainvälistymistä sekä VC-sijoittamista koskevaan aikaisempaan tutkimukseen. Molemmat näistä pääteemoista on yhdistetty tutkimuksen pohjana toimineessa teoreettisessa mallissa, joka tunnistaa kuusi yksittäistä resurssia. Tutkimusmetodina tässä tutkimuksessa oli kattava case-tutkimus, johon valikoitui neljä case-tapausta, jotka täyttivät aikaisen vaiheen kansainvälistymistä aiemmin tutkineiden tutkimusten asettamat kriteerit. Tutkimuksen data kerättiin elokuun 2020 ja marraskuun 2020 välillä haastattelemalla näiden case-tapausten VC-sijoittajia puolistrukturoiduissa haastatteluissa. Tutkimuksen tulokset vahvistavat, että suomalaisilla VC-sijoittajilla on hallussaan käytetyn mallin ehdottamia ei-rahallisia resursseja, mutta he eivät välttämättä tarjoa niitä jokaiselle sijoituskohteelleen, niin kuin malli alkujaan ehdotti. Sen sijaan, VC-sijoittajat pyrkivät optimoimaan resurssien tarjoamista sijoittamalla kykeneviin perustajiin, jotka ovat myös kokeneita yrittäjiä sekä asiantuntijoita alallaan. VC-sijoittajien ja yritysten perustajien välinen kanssakäyminen oli myös paljon vastavuoroisempaa, kuin malli antoi olettaa. Tulokset viittasivat myös siihen, että startupit kansainvälistyvät usein jopa ennen saatuaan VC-sijoitusta

Equitable Ecosystem: A Two-Pronged Approach to Equity in Artificial Intelligence

Lawmakers, technologists, and thought leaders are facing a once-in-a-generation opportunity to build equity into the digital infrastructure that will power our lives; we argue for a two-pronged approach to seize that opportunity. Artificial Intelligence (AI) is poised to radically transform our world, but we are already seeing evidence that theoretical concerns about potential bias are now being borne out in the market. To change this trajectory and ensure that development teams are focused explicitly on creating equitable AI, we argue that we need to shift the flow of investment dollars. Venture Capital (VC) firms have an outsized impact in determining which innovations will scale, we argue that influencing how these firms allocate the capital in their funds can ensure that issues of equity are top of mind for development teams. To shift the flow of investment dollars, we propose a two-pronged approach that will address two core drivers of the flow of investment: intellectual property (IP) and diversity. Our current IP system incentivizes a lack of transparency in the AI space frustrating attempts by third parties to assess whether AI- powered products and services are inequitable. And the current demographic makeup of VC firms and companies within the AI investment environment are out of sync with the general population, which can have negative downstream effects in terms of bias in AI. To change the existing dynamic, we argue for 1. creating a fifth category of IP for data and AI that would exchange ownership for compliance with a human rights framework and 2. establishing a tax incentive for VC firms graded favorably on our commitment index. Our approach is designed to create an equitable ecosystem of sorts, one that both necessitates and encourages equitable AI from conception to implementation

The effects of intra-industry and extra-industry networks on performance: A case of venture capital portfolio firms

This study examines the influence of intra-industry and extra-industry networks on firm performance by using data on 1264 UK venture-capital-backed start-up companies. The venture's network was operationalized by connecting together the various portfolio companies sharing the same investor. Regression results show that the venture's network has a strong impact on firm's success. Yet, whereas extra-industry ties are directly and positively linked to the likelihood of the venture to reach a successful exit, intra-industry ties exert a negative impact on companies' performances. However, interaction effects show that once a firm establishes a sufficient number of extra-industry ties, it is able to profit from the network in its industry of operation. Overall, these findings show that an optimal balance of ties is achieved through a diverse set of connections incorporating both intra-industry and extra-industry ties