## 2 Materials and Methods
Through forecasting success, improving funding targets, focusing on the correct audience, identifying fraud, and modeling supporter behavior, these mathematical models and AI techniques can increase the effectiveness and efficiency of crowdfunding campaigns. AI can be used by crowdfunding platforms and campaign managers to make data-driven decisions that will enhance the results of campaigns and help them meet their fundraising targets. In the simplest terms, crowdfunding is a community of people putting up money to support a project or a specific cause. There are four types of crowdfunding, of which three of the four are related to raising capital for small businesses or start-ups [21]. 1. Rewards-based crowdfunding: With this type of crowdfunding, an investor provides an online contribution in return for a reward. This can include providing a product that was launched with the funding for free or at a discount. 2. Equity crowdfunding: With this type of crowdfunding, investors support the goal of raising capital online in exchange for a percentage of equity ownership in the business itself. 3. Peer-to-peer (P2P) lending: Similar to acquiring a bank loan, this type of lending instead comes from an individual as opposed to a financial institution. The loan is expected to be repaid over a certain time. 4. Donations: Similar to GoFundMe, this type of crowdfunding allows individuals or groups to benefit from funding (without repayment) to support an individual cause or project. Our research focuses on peer-to-peer funding with the assistance of Kiva's data. There is an increased demand for investments to fulfill social impact goals, providing many benefits to peer-to-peer lending given the societal impact resulting from such loans. Whether investing in renewable energy projects creating programs and resources to increase diversity amongst youth and their interest in STEM or creating programs to solve for food shortage of affordable housing, P2P lending shows promise to those entrepreneurs who wish to contribute and leverage untraditional sources of funding. One example is LenderKit, which offers the opportunity to launch a customized ESG crowdfunding platform, promoting environmental crowdfunding, socially responsible crowdfunding, and impact investing platforms. With the emerging use of P2P platforms, especially to help drive ESG goals, how does a company implement continuous monitoring procedures to mitigate related risks while promoting the promise of positive global impact? On a broader ESG level, there is power in pairing ethical AI and financial inclusion. For example, AI solutions can collect and analyze large volumes of data related to ESG risks and opportunities through assessing real-time company impact as well as gaps and discrepancies in several ESG factors. AI can be used to conduct market narratives and sentiment analysis. AI can also be used to assess a company's compliance with related ESGregulations and disclosures. Furthermore, AI can be used on publicly available data to help predict material ESG factors and inform future funding needs. Specific applications of AI to promote social impact initiatives include:  
1. Natural language processing (NLP), a program trained to read the transcript of a company's quarterly earnings and investments public meetings, can analyze the CEO's use of words, and assess which parts of the discussion focus on social-justice-related topics and, ultimately, develop an understanding of a company's commitment to ESG factors. 2. Peer-to-peer lending has resulted in widely known impactful societal outcomes across the globe. There are still challenges that exist in the P2P market, including limited information to assess creditworthiness, volumes of applicant data for humans to review to inform decision-making, fraudulent activities, data privacy, biased decisions, loan defaults, the feasibility of lending platforms to collect information and process applications in a timely manner, and the pace of evolving regulation.