1. Introduction
Peer-to-peer (P2P) lending has emerged as an alternative financing model in the digital economy, particularly in developing economies such as Indonesia, where access to traditional banking services is limited [1]. The growth of financial technology (fintech) platforms has enabled the development of P2P lending, which connects individual lenders directly with borrowers without the intermediation of traditional financial institutions [2]. Previous research has highlighted the potential of P2P lending to enhance financial inclusion and address the credit gap in underserved populations, such as micro, small, and medium enterprises (MSMEs) [1]. In Indonesia, the growth of P2P lending has been rapid, with the number of registered P2P lending platforms growing from 13 in 2018 to over 160 in 2021 [3]. P2P lending platforms in Indonesia have been leveraging data-driven approaches, such as credit scoring and risk assessment, to evaluate the creditworthiness of borrowers and to mitigate the risks faced by financiers. However, the lack of standardized risk assessment and credit evaluation processes on P2P lending platforms poses significant challenges [4]. Lenders often rely on limited data and non-traditional creditworthiness indicators to assess the risk profiles of borrowers, leading to information asymmetry and potential defaults.
Value engineering, a systematic approach to improving the value of a product or service, can be a valuable tool in the context of P2P lending [5]. Through applying value engineering principles, P2P lending platforms can identify and prioritize the key factors that influence lending decisions, such as risk, return, and data privacy, to enhance the overall value proposition for both borrowers and lenders [6]. The application of value engineering as a decision support system can offer a structured approach to evaluating and selecting P2P lending opportunities. Value engineering is a systematic method for improving the “value” of goods or services by examining function, cost, reliability, and other key factors [5].
Value engineering focuses on analyzing and improving existing products or services, while P2P lending disrupts the traditional financial industry by facilitating direct transactions between individuals. Despite their differences, both systems share the goal of maximizing value and reducing inefficiencies in their respective domains. When applied to P2P lending, value engineering can help borrowers, lenders, and platforms assess the overall “value” of a lending contract opportunity by considering factors such as the borrower’s creditworthiness and the platform’s risk management practices.
The application of value engineering as a decision support system offers a structured and scientifically rigorous approach to evaluating and selecting P2P lending opportunities. By examining function, cost, reliability, and other key factors, value engineering provides a framework for systematically improving the “value” of financial products and services [5]. This study, therefore, contributes to the academic discourse by extending the application of value engineering beyond its traditional domains, demonstrating its relevance and utility in the fintech industry.
Moreover, the study’s focus on the Indonesian context provides valuable insights into the unique challenges and opportunities within emerging markets, where financial inclusion remains a critical concern. The integration of value engineering into P2P lending offers a scientifically grounded methodology that can guide lenders, borrowers, and platforms in making data-driven, value-maximizing decisions. This research not only addresses practical challenges but also advances the theoretical understanding of value optimization in digital financial ecosystems.
2. Literature Review
2.1. An Overview of P2P Lending and Its Significance in Indonesia’s Financial Industry
Digital technology is developing at a swift pace in many areas, including in the financial industry. The financial sector is seeing rapid innovation, as evidenced by the emergence of numerous information-technology-based products, commonly referred to as digital finance. These digital financial solutions come in a wide variety of forms, including blockchain, cryptocurrency, P2P lending, digital payments, digital banking, and automated system recommendations [7]. Several non-banking digital financial sectors have formed, producing a wide range of creative digital financial products; thus, the development of digital financial products is not limited to banking institutions. The existence of digital financial products has allowed for the provision of effective services to an increasing number of people who were previously unsatisfied with the financial products offered by banking institutions. The variety of digital financial products available has also made fulfilling their needs easier [8].
P2P lending has emerged as a disruptive force in the financial industry, offering individuals and businesses an alternative source of funding outside of traditional banks and financial institutions [9]. This decentralized approach to lending allows borrowers to access capital quickly and easily while providing investors with the opportunity to earn attractive returns on their investments. As P2P lending platforms continue to grow in popularity, they are reshaping how people think about borrowing and investing and are challenging the traditional banking model. This shift toward a more peer-driven financial system has democratized the lending process, giving individuals more control over their financial futures. With lower fees and interest rates compared with traditional banks, P2P lending has become an appealing option for those seeking funding or looking to diversify their investment portfolios. As technology continues to advance and the sharing economy expands, P2P lending is likely to become an even more integral part of the financial landscape, providing a viable alternative to traditional banking for many individuals and businesses [10].
The rise of P2P lending platforms has also opened up opportunities for borrowers who may have previously been denied loans by traditional banks due to strict credit requirements. This inclusive approach to lending has allowed individuals with varying credit histories to access much-needed funds for personal or business purposes [11]. Additionally, the transparency and accessibility of P2P lending platforms have helped to build trust among users, further solidifying their place in the financial industry. With the potential for continued growth and innovation, P2P lending is poised to revolutionize how people borrow and invest money in the years to come. By eliminating the intermediating agent and connecting borrowers directly with investors, P2P lending has also helped to reduce interest rates and fees for many consumers. This has made loans more affordable and accessible to a wider range of individuals, ultimately democratizing the lending process. As more people become aware of the benefits of P2P lending, the industry is expected to further expand and evolve, offering even more opportunities for individuals to achieve their financial goals. Overall, P2P lending is a promising alternative to traditional banking that is reshaping the financial landscape for the better [12].
P2P lending is a digital financial instrument that has garnered a positive public response in Indonesia. In contrast to the traditional banking business model, in which a very long chain connects the financial institution and the community, this P2P lending product has a shorter chain that allows the community to gain quicker access [13]. P2P lending has grown extremely quickly in Indonesia, with an increasing number of institutions offering P2P loan products and serving a growing number of clients. Indonesia had 160 fintech P2P lending companies registered and licensed at the end of 2021, evidencing their substantial market presence [14]. P2P lending is still expanding in Indonesia and has been doing so since the sector began to take off in 2015 [3].
The rapid development of the P2P lending sector in Indonesia has had both positive and negative impacts. On the positive side, P2P lending has increased financial inclusion by providing access to credit for individuals and small businesses that may have been underserved by traditional financial institutions. This has been particularly beneficial in Indonesia, where there is a large unbanked population and limited access to formal financial services, especially in rural areas [15].
On the negative side, the rapid growth of the P2P lending sector has raised concerns about consumer protection, data privacy, and financial stability. Predatory lending practices, a lack of transparency, and misuse of borrower data by some P2P lending platforms have been reported. As a result, the Indonesian government has been working to strengthen the regulatory framework for the P2P lending sector to address these concerns and to ensure its sustainable growth [14]. In June 2024, the Indonesian Financial Services Authority reported that it had closed several P2P lending platforms. Of the previous 160 platforms, 98 platforms remain operational in the Indonesian market. The application of value engineering principles can be a valuable approach to addressing the challenges facing the P2P lending sector in Indonesia.
2.2. Definition of Value Engineering
Value engineering is a systematic and organized approach to improving the value of a product, process, or service [16]. It involves analyzing and identifying ways to reduce costs while maintaining or improving quality, performance, and customer satisfaction. By focusing on value rather than just cost-cutting, value engineering aims to optimize the overall value proposition for stakeholders. This methodology can be applied to various industries, including construction, manufacturing, and software development, to improve results and to maximize the return on investments [17]. Value engineering is a crucial tool for companies looking to remain competitive in today’s fast-paced market. By constantly seeking ways to increase value for customers, businesses can differentiate themselves from their competitors and strengthen customer loyalty. In addition, value engineering can lead to innovations and breakthroughs that can revolutionize industries and drive growth. Overall, adopting a value engineering mindset can help organizations thrive in an ever-evolving marketplace.
Value engineering was first proposed by Miles in 1961. According to Miles, “value” in the engineering approach is the relationship between the functionality, performance, and cost of a product or system [18]. Value engineering involves systematically measuring the functionality of a product or service compared with the costs that must be incurred to obtain those goods and services. It is closely related to value analysis, which is an approach to finding the best solution by identifying existing information about a product or service and involves optimizing the design and production processes to strike the best balance between these factors [19].
According to the Society of American Value Engineers (SAVE), value engineering is the methodical application of accepted procedures to determine the purpose of a good or service, assign a monetary value, and reliably fulfill the required function at the lowest feasible cost [19]. Thus, when the user can identify and distinguish between functions that are required and those that are not and, consequently, devise alternate ways to complete the required tasks at a reduced overall cost, the goal of a methodical value engineering approach is successfully achieved.
Miles used value engineering to measure the functional level of a product compared with the costs or resources that must be spent to make the product. Then, in 1986, Fallon started using value engineering in the economic field [20] to measure the level of functionality and consumer satisfaction with a product. As an economic approach, this value engineering concept was further developed by Parker in 1994. In his research, Parker provided an alternative interpretation of the value engineering notion by changing the ratio’s basic definition and viewing it as a cost-to-worth ratio [16]. Subsequently, value engineering became increasingly popular and is now employed in many scientific fields, as Table 1 demonstrates. The meaning of cost and function has also been subject to many changes and interpretations, which largely depend on the scientific methodology employed.
It is clear from the literature review that the value index has been extensively utilized in numerous scientific domains, in which the same issue frequently arises: finding and classifying components that are part of functions and factors that are part of cost groups can be challenging. The complexity of finding and classifying these elements varies depending on the scientific discipline. If the process of evaluating and adding factors to the value index is carried out well, the value index that is obtained will be accurate, and its application to a particular field will be successful [17].
The formula of value engineering illustrates the connections between value, performance, and cost [5]:
V = P/C(1)
V = Value Engineering;
P = Function/Performance;
C = Cost.
This study outlines a comprehensive methodology to evaluate the performance and value proposition of P2P lending agreements. The following steps and Figure 1 provide a detailed overview of this thorough value engineering process [5]:
Identify key performance indicators that are critical to the success and sustainability of the P2P lending agreement from the lender and borrower sides.
Determine the hierarchy or relative importance of each criterion. This step, which is crucial to understanding the impact of the criteria on the overall value proposition, can be performed through a structured framework, such as the Analytic Hierarchy Process, to prioritize the key performance indicators based on their importance to both borrowers and lenders.
Establish the baseline of the P2P lending platform agreement performance.
Identify changes in the performance of alternative P2P lending agreements. By comparing the baseline performance to the potential performance of alternative agreements, the value engineering process can help identify the optimal P2P lending solution that maximizes value for all stakeholders.
Apply a rigorous cost–benefit analysis to assess the trade-offs between the functionality, performance, and cost of the P2P lending agreement from the perspectives of both borrowers and lenders relative to the baseline P2P lending agreement performance.
Conduct a comprehensive evaluation of the potential risks and rewards associated with the P2P lending platform agreement to ensure that it delivers optimal value to all stakeholders.
One key aspect of value engineering is its focus on cost reduction without sacrificing quality [26]. By identifying areas where costs can be minimized without compromising the overall value of a product or service, companies can streamline their operations and increase their profit margins. This not only benefits the bottom line but also allows businesses to offer competitive pricing to attract more customers. Additionally, value engineering encourages a culture of continuous improvement and innovation within an organization, as teams are constantly looking for ways to optimize processes and deliver greater value to customers [27]. Ultimately, the principles of value engineering can drive efficiency, profitability, and customer satisfaction for businesses across various industries. By constantly analyzing and reevaluating the value of each aspect of their operations, companies can stay ahead of the competition and adapt to changing market demands. This proactive approach to problem solving can lead to the long-term success and sustainability of businesses. Overall, value engineering is a powerful tool that can help businesses thrive in a competitive marketplace and achieve their goals for growth and success [28].
3. Value Engineering in P2P Lending
3.1. Identifying Key Performance Indicators
The first step in applying value engineering principles to P2P lending is to identify the key performance indicators that are critical to the success of P2P lending platforms. Identifying key performance indicators and utilizing value engineering techniques can greatly enhance the efficiency and effectiveness of P2P lending platforms. Value engineering involves analyzing the various components of a loan or investment opportunity to determine where value can be added or costs can be reduced [5]. By identifying key performance indicators, such as interest rates, repayment terms, and default rates, borrowers and investors can make more informed decisions that align with their financial goals. Additionally, implementing value engineering techniques can help mitigate risks and improve the overall quality of the lending process, leading to greater satisfaction and trust among participants. In essence, value engineering is a strategic approach in P2P lending that can benefit both borrowers and investors by optimizing the value of their financial transactions.
Based on a literature review and a survey conducted in Indonesia from January to June 2024 involving 120 respondents (95 borrowers and 25 lenders) out of 150 questionnaires distributed, this study was focused on understanding the behaviors and preferences of P2P lending users [29,30,31]. The respondents were selected using purposive sampling from a database of P2P lending platform users; specifically, customers of platforms that are members of the Indonesian Fintech Association. The lenders had at least one year of active experience on P2P platforms and had invested a maximum of IDR 30 million, while borrowers were selected based on their loan purposes for SME businesses or working capital, with loans amounting to IDR 30 million. Respondents were drawn from various regions across Indonesia, including Jakarta, Bandung, Surabaya, Medan, and Yogyakarta, to ensure a diverse geographic representation, with ages ranging from 19 to 55 years and educational backgrounds from junior high school to bachelor’s degrees. Data collection was conducted through online surveys.
From these findings, several key performance indicators were identified by lenders as being the most important in P2P lending (Table 2) [30,31]:
Investment rate: This measures the return on investment of the contract.
Default rate: This measures the credit risk associated with the loan portfolio, which is a key determinant of the platform’s financial sustainability.
Customer satisfaction: This measures the level of satisfaction of lenders with the experience of using the P2P lending platform.
Regulatory compliance: This measures the platform’s adherence to relevant laws and regulations, which is crucial for maintaining trust and confidence in the industry.
Service fee: This measures the platform’s service fee, which is important for attracting and retaining investors [32].
Once the key performance indicators had been identified, the Analytic Hierarchy Process was then used to determine the relative weights of these criteria. The AHP methodology was developed to assess the comparative importance of multiple decision factors through pairwise comparisons [5] and to systematically evaluate complex decision problems by comparing individual variables [33]. According to the survey results, 68% of lender respondents (17 respondents) selected the AHP criteria ranking below. The steps of the AHP process were as follows:
Defining the criteria: The five key performance indicators previously identified were used as the criteria for the AHP analysis: investment rate, default rate, customer satisfaction, regulatory compliance, and service fee.
Creating a pairwise comparison matrix (Table 3): Respondents conducted pairwise comparisons of the criteria using the fundamental AHP scale to determine their relative importance. Each factor was compared against the other factors to determine their relative importance. We used a scale from 1 (equal importance) to 9 (extremely more important).
Normalizing the matrix (Table 4): The pairwise comparison matrix was normalized by dividing each element by the sum of its column.
Calculating the weights: The priority weight of each criterion was calculated by taking the average of the normalized row. Table 5 provides an example of the weights derived from the AHP analysis, where “Investment Rate” and “Default Rate” emerged as the most crucial considerations.
After establishing the performance criteria and their relative importance (weights), lenders specified the parameters of the criteria by determining their units of measurement and creating a range of permitted values [5]. This usually provides a quantitative, objective foundation for allocating values on a rating scale ranging from 1 to 10. Table 6 contains examples of definitions for the performance parameters.
After the performance parameters were established, the next step was to determine the costs that must be incurred by the lender. This included evaluating the various costs associated with the lending process, such as administrative fees, processing fees, and any other expenses that the lender may need to account for when making a decision about whether to provide a loan. By carefully considering these cost factors, the lender can make a more informed decision about the overall feasibility and profitability of the lending opportunity.
Based on the results of a literature study and a survey of P2P lending users in Indonesia, the following costs must be incurred by the lender [29,34]:
Opportunity cost: The potential earnings or benefits forgone by the lender to participate in the P2P lending platform.
Default cost: The costs incurred by the lender due to borrower defaults or late payments.
Service fee/Process cost: The fees charged by the P2P lending platform for facilitating the transaction between the lender and borrower.
Monitoring cost: The costs associated with the lender’s efforts to monitor the borrower’s creditworthiness and loan repayment status.
Tax cost: The taxes or other government-imposed charges that the lender must pay on their P2P lending income or earnings.
After establishing the performance criteria and costs for the lenders, the same steps were taken to determine the performance criteria indicators and costs from the borrower’s perspective. This involved establishing the units of measurement, the permitted value ranges, and the relative importance of each performance criterion from the borrower’s perspective. This quantitative, objective foundation was then used to assign values on a rating scale ranging from 1 to 10 for the borrower-side criteria [5].
Based on the literature review and the same survey mentioned above, the following key performance indicators for P2P lending users who function as borrowers were considered (Table 7) [30,31]:
Productive system rate: The productive system rate refers to how efficiently and effectively the platform processes loan applications and disburses funds.
Loan disbursement rate: This is the speed and efficiency with which funds are disbursed once a loan application is approved. A high loan disbursement rate indicates that the platform can provide funds quickly to borrowers, which is essential for those who need immediate access to cash.
Service fee/process cost: Service fees are additional charges imposed by the platform on borrowers for using their services. These fees can include administrative fees, handling fees, or other charges.
The loan process: Metrics include the ease and efficiency of applying for a loan, the processing time, and the overall user experience during the loan application and disbursement stages.
Loan flexibility: This includes the availability of alternative payment options, repayment schedules, and the ability to modify loan terms as needed.
Regulatory compliance: Compliance with regulations is crucial to ensure that the platform operates according to legal and regulatory standards.
According to the survey results, 63% of borrower respondents (60 respondents) selected the AHP criteria ranking below. The steps of the AHP process were as follows:
Defining the criteria: The key performance indicators previously identified were used as the criteria for the AHP analysis: productive system rate, loan disbursement rate, service fee/process cost, loan process, loan flexibility, and regulatory compliance.
Creating the pairwise comparison matrix (Table 8): Respondents conducted pairwise comparisons of the criteria using the fundamental AHP scale to determine their relative importance. Each factor was compared against the other factors to determine their relative importance. We used a scale from 1 (equal importance) to 9 (extremely more important).
Normalizing the matrix (Table 9): The pairwise comparison matrix was normalized by dividing each element by the sum of its column.
Calculating the weights: The priority weight of each criterion was calculated by taking the average of the normalized row (Table 10).
After establishing the performance criteria and their relative importance (weights), borrowers specified the parameters of the criteria by determining their units of measurement and creating a range of permitted values. This usually provides a quantitative, objective foundation for allocating values on a rating scale ranging from 1 to 10. Table 11 contains examples of definitions of the performance parameters.
Based on the results of a literature study and a survey of P2P lending users in Indonesia, the following costs must be incurred by the borrower [30,31,33,34]:
Loan interest cost rate: This refers to the interest rate charged on the loan amount by the P2P lending platform.
Operational cost: This encompasses the various operational costs of the borrower’s business.
Service fee/Process cost: This is the fee charged by the P2P lending platform for the services provided, including for loan processing, risk assessment, and platform maintenance.
Business risk cost: This accounts for the potential risk of the borrower’s business venture, which may impact their ability to repay the loan on time.
Tax cost: This refers to any applicable taxes or levies that the borrower must pay on the loan amount or interest earned.
After identifying the performance criteria and cost factors from the perspectives of both lenders and borrowers, the next step was to calculate the value index for each alternative P2P lending contract option. This involved taking the average of the value indices derived from the lender and borrower sides [35]. This allowed us to systematically evaluate and compare the alternatives based on a comprehensive set of criteria beyond just financial considerations.
3.2. Applying Cost–Benefit Analysis
Applying a cost–benefit analysis to features and services can also help P2P lending platforms make informed decisions about where to invest their resources for maximum impact. By carefully weighing the potential benefits against the associated costs, platforms can ensure that they are focusing on initiatives that will truly add value for their users. Additionally, conducting regular audits and risk assessments can help platforms proactively identify and address any vulnerabilities in their systems, further enhancing security and trust within the ecosystem. Overall, taking a strategic and data-driven approach to decision making can help P2P lending platforms not only survive but also thrive in an increasingly competitive market.
In the following paragraphs, we provide an example of using value engineering to select a P2P lending platform. Let us assume that we are considering using platforms 1 (Table 12), platforms 2 (Table 13), and platforms3 (Table 14), with platform 1 serving as our baseline for comparison. To apply a cost–benefit analysis, we would first need to identify the key factors or criteria that are important in evaluating the different P2P lending platforms. These could include factors such as interest rates, loan approval rates, default rates, the user-friendliness of the platform, customer support, and any additional fees or charges. Once we have determined the relevant evaluation criteria, we can then systematically compare the performance of each platform against these factors (Table 15, Table 16 and Table 17). This will allow us to assess the relative benefits and costs of using the different platforms and, ultimately, make an informed decision on which one best meets our needs.
The alternative P2P lending platform contracts are evaluated against the baseline contract using the “Performance Matrix”. The total performance rating for both lenders and borrowers is divided by the total cost for lenders and borrowers to calculate a value index. Throughout this process, stakeholders review the performance ratings of the P2P lending contract alternatives and make necessary adjustments. In some cases, technical studies may be conducted to corroborate the performance values. The consensus on the validity of the alternatives is based on the performance criteria and the cost criteria, and the performance criteria can be updated to align with current information if required.
One key aspect that platforms must consider is the importance of building strong relationships with both borrowers and lenders. By fostering trust and transparency, platforms can attract and retain users, ultimately driving growth and sustainability. Additionally, offering personalized and flexible lending options can help platforms stand out in a crowded marketplace and cater to the diverse needs and preferences of their users. Furthermore, investing in robust customer support and dispute-resolution mechanisms can help platforms address issues quickly and effectively, enhancing user satisfaction and loyalty [36].
4. Selection Process
4.1. Criteria for Selecting P2P Lending Platforms
Selecting a P2P lending platform is not just about finding one that looks good on the surface. Let us consider an investor looking to diversify their portfolio by adding P2P lending. Here are examples of two different platforms:
Platform A has been operational for over five years, with a 95% success rate on loans. It provides detailed loan performance reports and offers responsive customer service, with live chat support available during business hours. It charges a 1% fee on all successful loans and is known for its transparency in disclosing these fees upfront.
Platform B, on the other hand, has been around for only two years, with a lower success rate of 85%. Its fee structure is more complex, with hidden costs that only become apparent after loans are funded. Customer support is provided primarily through email, with a slow response time.
Based on these examples, Platform A would be the preferable choice due to its established track record, increased transparency, and better customer service. This comparison highlights the importance of evaluating the history, transparency, and customer support of P2P lending platforms before investing.
4.2. Comparison of Different Platforms
One way to conduct thorough research on P2P lending platforms is to compare the different options available. This can involve looking at factors such as interest rates, fees, borrower eligibility criteria, and loan terms. Additionally, investors may want to consider the platform’s customer service, user interface, and overall transparency. By carefully analyzing these aspects, investors can make more informed decisions about which platforms align best with their investment goals and risk tolerance.
Consider two P2P lending platforms:
Platform X offers interest rates between 5% and 12%, with loan terms ranging from 12 to 36 months. It has a strict borrower lending process, with a focus on high-credit-score individuals. The platform also offers a user-friendly interface with a mobile app, making it easy for investors to monitor their investments on the go.
Platform Y, however, offers higher interest rates ranging from 10% to 18%, but has less stringent borrower criteria, accepting lower credit scores and offering shorter loan terms of 6 to 24 months. While the potential returns are higher, so is the risk. Platform Y also lacks a mobile app, which could be a drawback for investors who prefer to access their investment portfolios.
By analyzing these aspects—interest rates, borrower eligibility, user interface, and mobile accessibility—investors can choose the platform that best aligns with their risk tolerance and investment goals. For example, a conservative investor might prefer Platform X, while a more aggressive investor might opt for Platform Y while understanding the higher risk involved.
4.3. Consideration of Borrower and Lender Requirements
Understanding the criteria for borrowers, such as credit scores, loan amounts, and interest rates, can help investors assess the level of risk associated with each platform. Similarly, knowing the requirements for lenders, such as minimum investment amounts and diversification options, can aid in making strategic investment decisions. By accounting for these factors, investors can tailor their investment strategies to align with their risk tolerance and financial objectives. Additionally, considering the reputation and track record of the P2P lending platform is crucial in determining its reliability and trustworthiness. Conducting thorough due diligence on the platform’s history, performance, and customer reviews can provide valuable insights into its credibility and potential for long-term success [6,36].
As an example, let us consider a P2P lending platform that requires borrowers to have a minimum credit score in the high range and a debt-to-income ratio of less than 40%. The platform also has a minimum investment requirement of IDR 10 million for lenders and encourages diversification by allowing investments in multiple loans with varying risk profiles. An investor with a lower risk tolerance might choose to invest in loans offered to borrowers that have the highest credit scores and low debt-to-income ratios, understanding that these loans are likely to have a lower default rate. On the other hand, a risk-tolerant investor might choose to invest in loans with slightly lower credit scores but higher interest rates, accepting the increased risk in exchange for potentially higher returns.
4.4. Monitoring and Adjusting Strategies as Needed
Monitoring and adjusting strategies are also crucial for success in the ever-changing P2P lending landscape. This includes regularly reviewing and rebalancing investment portfolios, as well as staying informed about regulatory changes and market developments. Additionally, seeking guidance from financial advisors or P2P lending experts can offer valuable perspectives and help investors make more informed decisions. By staying proactive and adaptable, investors can maximize their chances of success and achieve their long-term financial goals in the P2P lending market [36].
Imagine an investor who initially allocates IDR 100 million across five P2P lending platforms, choosing a mix of conservative and high-risk loans. Over time, they notice that the high-risk loans on Platform Y have a higher default rate than expected, while the conservative loans on Platform X are performing consistently well. In response, the investor decides to rebalance their portfolio by reallocating funds from Platform Y to Platform X, focusing more on stable, lower-risk loans. They also stay updated on regulatory changes, such as new government guidelines on P2P lending, which could impact future returns.
By monitoring their investments and adjusting their strategy, the investor minimizes potential losses and positions themselves for better long-term returns. This proactive approach, combined with regular reviews of portfolio performance and market conditions, is key to navigating the dynamic P2P lending environment.
5. Case Studies in Indonesia
The researchers conducted a study using questionnaires to collect data from a sample of P2P lending platform users, consisting of both lenders and borrowers. The data were gathered between January and June 2024. The questionnaire consisted of a mix of closed-ended and open-ended questions, which were validated by a panel of three subject matter experts:
A former head of the Financial Services Authority with 30 years of experience in finance, banking, and P2P lending;
A professor with 30 years of experience in engineering and technology systems;
A professor with 30 years of experience in finance.
Respondents were selected using a purposive sampling technique, with 120 participants recruited out of 150 survey questionnaires distributed, comprising 95 borrowers and 25 lenders. For this study, the researchers considered investment and loan amounts of IDR 30,000,000 for each platform, with a 12-month repayment period. Additionally, 10 P2P lending platforms utilized by the respondents were evaluated. The majority of borrowers in the study were small- and medium-sized enterprises and primarily used P2P lending platforms as a source of working capital or business funding rather than for personal or consumer loans. These borrowers cited improved access to capital and flexible repayment terms as their primary motivations for using P2P lending platforms to support their business operations and growth. The following are the details of the respondents:
Respondent criteria:
Lenders:
Experience as a lender: Respondents must have had active experience as lenders on P2P lending platforms in Indonesia (at least 1 year).
Investment amount: Respondents were selected based on a maximum investment of IDR 30 million made on the platform.
Borrowers:
Loan purpose: The selected respondents’ loans had to be for SME businesses or working capital.
Loan amount: Borrowers who had taken out loans of IDR 30 million were considered.
Respondent selection process:
Random sampling: Respondents were selected through a purposive sampling process from a database of P2P lending platform users in Indonesia who are customers of P2P lending platforms that are members of the Indonesian Fintech Association.
Respondent origin:
Geographic region: Respondents were taken from various regions in Indonesia (Jakarta, Bandung, Surabaya, Medan, Yogyakarta) to obtain a broader picture of the behaviors and preferences of P2P lending users.
Demographics: The ages of the respondents ranged from 19 to 55 years old, with educational backgrounds ranging from junior high school to bachelor’s degrees.
Data collection method:
Online survey: Questionnaires were distributed through online survey applications, which could be accessed by lenders and borrowers at any time.
The P2P platform company names have been disguised in this study to maintain the confidentiality of the data and to protect the privacy of the participants. This decision was made to ensure the ethical handling of sensitive information and to build trust with the survey respondents, who may have been hesitant to share details about their experiences if their identities were revealed. An in-depth analysis was performed on each question in the questionnaire. This analysis involved mapping the responses to the relevant performance criteria and associated costs from both the lender and borrower perspectives. From the data collected through the questionnaires, two key metrics were calculated for each P2P lending platform under evaluation: the lender-side performance matrix and the borrower-side performance matrix (Table 18 and Table 19). These metrics provided a comprehensive assessment to support the decision-making process (Table 20).
The total contract value index serves as a powerful tool for assessing and comparing the value propositions of different P2P lending platforms. By analyzing this index, stakeholders can gain valuable insights into the overall effectiveness and efficiency of each platform in delivering value to both borrowers and lenders.
A high-value index is indicative of a platform that is capable of providing favorable conditions for both parties involved. This suggests that the platform not only offers competitive interest rates and fees but also maintains a robust risk management system, which is reflected in its low level of non-performing loans (NPLs). The low NPL rate implies that the platform has stringent credit assessment procedures, strong borrower screening, and effective loan monitoring practices. As a result, lenders on such platforms can expect more reliable returns, while borrowers benefit from a more stable and supportive lending environment. This enhances the platform’s reputation, making it more attractive to potential users and encouraging continued growth and sustainability in the market.
Conversely, a low-value index indicates that a platform may not be optimizing its operations to the benefit of its stakeholders. A low index is often associated with higher rates of non-performing loans and loan defaults. This could be due to weaker credit assessment protocols, inadequate borrower support, or insufficient risk mitigation strategies. For lenders, this translates into higher risks and potentially lower returns, which may deter investment. For borrowers, it could mean facing less favorable loan conditions, including higher interest rates or less flexible repayment terms, as the platform compensates for the higher risk of default. In summary, the total contract value index is a crucial indicator of a P2P lending platform’s performance and its ability to balance the needs of both lenders and borrowers. By providing a clear, comparative metric, the index helps stakeholders make more informed decisions, driving better outcomes across the entire P2P lending ecosystem.
6. Conclusions
6.1. Summary of Findings
In conclusion, the application of value engineering in the selection and evaluation of P2P lending platforms not only offers practical benefits but also contributes significantly to the scientific understanding of decision-making processes in the fintech sector. This research advances the methodology of value engineering by adapting it to the context of P2P lending, thereby providing a novel and systematic approach to optimizing platform selection. The structured and quantitative framework developed in this study enables a rigorous comparison of different P2P lending platforms, offering a scientifically grounded method to identify the most valuable alternatives that meet the performance requirements of both lenders and borrowers.
Moreover, this study contributes to the theoretical literature by demonstrating how value engineering principles can be integrated with financial risk assessment and technoeconomic analysis to enhance decision making in digital financial ecosystems. By balancing performance criteria with cost considerations, the value engineering approach enables stakeholders to make informed, data-driven choices that are not only practical but also scientifically robust. This research, therefore, not only provides actionable insights for industry practitioners but also enriches the academic discourse on value optimization and decision support systems in emerging financial technologies.
6.2. Limitations and Comparison with Other Studies
While this study provided a comprehensive framework for evaluating P2P lending platforms, several limitations should be acknowledged. A primary limitation is the lack of representative data on the development of the P2P lending market in Indonesia. The available data may not fully capture the dynamic and rapidly changing nature of the local P2P lending industry. This limitation can hinder the study’s ability to accurately reflect the latest trends and challenges faced by the industry in Indonesia.
In comparison with other studies, this research makes a contribution by incorporating assessments from multiple stakeholder perspectives (both lenders and borrowers). While similar studies have often evaluated P2P lending platforms from a single viewpoint—typically either the lender’s or the borrower’s—this study considers the needs of, risks to, and benefits for both parties [37,38]. This multidimensional approach provides a more balanced and comprehensive evaluation of P2P lending platforms, although it also introduces a level of complexity that might require further refinement, especially in the weighting of performance parameters to ensure broad applicability across various financial contexts.
6.3. Suggestions for Further Research
The value engineering framework can be applied to evaluate and optimize various types of financial instruments and services beyond P2P lending, such as leasing, procurement, and logistics contracts. Future research efforts should focus on refining and expanding the methodology, particularly through the development of a more comprehensive and adaptable set of performance parameters and their relative weights. This would ensure the framework’s broader applicability across a wider range of financial applications that extend beyond the P2P lending domain, enabling informed decision making and value maximization in diverse financial contexts.
These authors contributed equally to this work. Conceptualization, S.Y., A.Z.R.L., A.A.A. and T.M.S.; Methodology, S.Y., A.Z.R.L., A.A.A. and T.M.S.; Validation, A.Z.R.L., A.A.A. and T.M.S.; Investigation, S.Y.; Writing—original draft, S.Y.; Visualization, S.Y.; Supervision, A.Z.R.L., A.A.A. and T.M.S.; Project administration, S.Y. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data is contained within the article.
The authors declared no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Value index use in various scientific domains.
| Year | Implementation | Explanation |
|---|---|---|
| 2010 | Civil Engineering | Particular value engineering parameters are established and applied as selection criteria, and multicriteria decision making is combined with value analysis [ |
| 2010 | Civil Engineering | In civil construction and infrastructure projects, the performance-to-cost ratio can be improved through the use of value engineering and the value index concept [ |
| 2011 | Project Management | Value engineering is used as a concept for developing a multi-project environment [ |
| 2014 | Business Management | A conceptual model of agreement choice is created by connecting value engineering, value methodologies, and decision making [ |
| 2015 | Product Design | To assess the influence of topological optimization within the framework of the product design process, a function performance-to-cost ratio is employed [ |
| 2015 | Supply Chain Management | Suppliers are chosen within the framework of production lines by applying value engineering [ |
| 2018 | Design Engineering | A value engineering approach is applied in order to assess and choose the optimal options for system design and to implement them for motorized vehicle components [ |
Lenders’ performance criteria.
| No. | Criterion | Factor |
|---|---|---|
| 1 | Investment rate | Return on Investment |
| 2 | Default rate | Degree of Default (TWP90—Tingkat Wan Prestasi in 90 days) |
| 3 | Customer satisfaction | Customer Satisfaction Score (CSAT) |
| 4 | Regulatory compliance | Product Information Compliance |
| 5 | Service fee | Percentage of Service Rate |
Lenders’ pairwise comparison.
| Criteria | Investment Rate | Default Rate | Service Fee | Customer Satisfaction | Regulatory Compliance |
|---|---|---|---|---|---|
| Investment Rate | 1 | 3 | 5 | 7 | 9 |
| Default Rate | 1/3 | 1 | 3 | 5 | 7 |
| Service Fee | 1/5 | 1/3 | 1 | 3 | 5 |
| Customer Satisfaction | 1/7 | 1/5 | 1/3 | 1 | 3 |
| Regulatory Compliance | 1/9 | 1/7 | 1/5 | 1/3 | 1 |
Lenders’ normalized matrix.
| Criteria | Investment Rate | Default Rate | Service Fee | Customer Satisfaction | Regulatory Compliance |
|---|---|---|---|---|---|
| Investment Rate | 0.55 | 0.64 | 0.52 | 0.43 | 0.36 |
| Default Rate | 0.18 | 0.21 | 0.31 | 0.31 | 0.28 |
| Service Fee | 0.11 | 0.07 | 0.11 | 0.18 | 0.20 |
| Customer Satisfaction | 0.08 | 0.04 | 0.04 | 0.06 | 0.12 |
| Regulatory Compliance | 0.06 | 0.03 | 0.02 | 0.02 | 0.04 |
Lenders’ performance weights.
| Criteria | Percentage |
|---|---|
| Investment Rate | 50.10% |
| Default Rate | 26% |
| Service Fee | 13.40% |
| Customer Satisfaction | 6.80% |
| Regulatory Compliance | 3.70% |
Matrix of lenders’ performance parameters.
| Lender Performance Parameter Matrix | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Criteria | Unit of Measurement | Weight | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Investment Rate | Percentage Return on Investment | 50.10% | ≤5 | 10 | 15 | 20 | 25 | 30 | 35 | 40 | 45 | ≥50 |
| Default Rate | Percentage Degree of Default (TWP90) | 26% | ≥10 | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | ≤1 |
| Service Fee | Percentage of Service Rate | 13.40% | ≥10 | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | ≤1 |
| Customer Satisfaction | Percentage Customer Satisfaction Score | 6.8% | ≤10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 |
| Regulatory Compliance | Product Information Compliance | 3.7% | Nonregistered and Non Complete Annual Report and Product information | Nonregistered and Complete Annual Report and Product information | Registered and Non Complete Annual Report and Product information | Registered and Complete Annual Report and Product information | ||||||
| TWP90 = Tingkat Wanprestasi in 90 days | ||||||||||||
Borrowers’ performance criteria.
| No. | Criteria | Factor |
|---|---|---|
| 1 | Productive system rate | Productive system rate |
| 2 | Loan disbursement rate | Total amount disbursed/total amount applied |
| 3 | Loan process | Ratings from customers about loan process |
| 4 | Loan flexibility and payment | Ratings from customers about loan flexibility and payment |
| 5 | Regulatory compliance | Product information compliance |
| 6 | Service fee/process cost | Percentage of service rate |
Borrowers’ pairwise comparison.
| Productive Rate | Loan Disbursement | Service Fee/Process Cost | Loan Process | Loan Flexibility | Regulatory Compliance | |
|---|---|---|---|---|---|---|
| Productive Rate | 1 | 3 | 5 | 7 | 9 | 9 |
| Loan Disbursement | 1/3 | 1 | 3 | 5 | 7 | 9 |
| Service Fee/Process Cost | 1/5 | 1/3 | 1 | 3 | 5 | 7 |
| Loan Process | 1/7 | 1/5 | 1/3 | 1 | 3 | 5 |
| Loan Flexibility and Payment | 1/9 | 1/7 | 1/5 | 1/3 | 1 | 3 |
| Regulatory compliance | 1/9 | 1/9 | 1/7 | 1/5 | 1/3 | 1 |
Borrowers’ normalized matrix.
| Productive Rate | Loan Disbursement | Service Fee/Process Cost | Loan Process | Loan Flexibility | Regulatory Compliance | |
|---|---|---|---|---|---|---|
| Productive Rate | 0.596 | 0.623 | 0.512 | 0.423 | 0.355 | 0.265 |
| Loan Disbursement | 0.199 | 0.208 | 0.307 | 0.302 | 0.276 | 0.265 |
| Service Fee/Process Cost | 0.120 | 0.069 | 0.102 | 0.181 | 0.197 | 0.206 |
| Loan Process | 0.085 | 0.042 | 0.034 | 0.060 | 0.118 | 0.147 |
| Loan Flexibility and Payment | 0.066 | 0.030 | 0.020 | 0.020 | 0.039 | 0.088 |
| Regulatory compliance | 0.014 | 0.029 | 0.026 | 0.012 | 0.016 | 0.029 |
Borrowers’ performance weights.
| Criteria | Percentage |
|---|---|
| Productive System Rate | 45.67% |
| Loan Disbursement Rate | 25.94% |
| Service Fee/Process Cost | 14.61% |
| Loan Process | 7.74% |
| Flexibility and Payment | 4.65% |
| Regulatory Compliance | 1.39% |
Matrix of borrowers’ performance parameters.
| Borrower Performance Parameter Matrix | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Criteria | Unit of Measurement | Weight | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Productive System Rate | Percentage Productive System Rate | 45.67% | ≤5 | 10 | 15 | 20 | 25 | 30 | 35 | 40 | 45 | ≥50 |
| Loan Amount Disbursement Rate | Percentage of Total Amount Disbursed/Total Amount | 25.94% | ≤10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 |
| Service Fee/Process Cost | Percentage of Service Rate | 14.61% | ≥10 | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | ≤1 |
| Loan Process | Rating from Customer about Loan Process (Percentage of Satisfaction) | 7.74% | ≤10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 |
| Loan Flexibility and Payment | Rating from Customer about Loan Flexibility (Percentage of Satisfaction) | 4.65% | ≤10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 |
| Regulatory Compliance | Product Information Compliance | 1.39% | Nonregistered and Non Complete Annual Report and Product information | Nonregistered and Complete Annual Report and Product information | Registered and Non Complete Annual Report and Product information | Registered and Complete Annual Report and Product information | ||||||
P2P lending example: Platform 1.
| Platform 1—Baseline | |||
|---|---|---|---|
| Plafond | Rp 10,000,000 | ||
| Lender Performance Criteria | Borrower Performance | ||
| Percentage Return on Investment | 30% | Percetage Productive System Rate | 35% |
| Percentage Degree of Default(TWP90) | 10% | Percentage Total Amount Disbursed/Total Amount | 75% |
| Percentage of Service Rate | 5% | Percentage of Process Cost | 5% |
| Percentage Customer Satisfaction Score | 50% | Percentage Customer Satisfaction of Loan Process | 50% |
| Product Information Compliance | Registered but Not Complete | Percentage Customer Satisfaction of Loan Flexibility and Payment | 50% |
| Product Information Compliance | Registered but Not Complete |
P2P lending example: Platform 2.
| Platform 2 | |||
|---|---|---|---|
| Plafond | Rp 10,000,000 | ||
| Lender Performance Criteria | Borrower Performance | ||
| Percentage Return on Investment | 15% | Percetage Productive System Rate | 40% |
| Percentage Degree of Default(TWP90) | 5% | Percentage Total Amount Disbursed/Total Amount | 75% |
| Percentage of Service Rate | 2% | Percentage of Process Cost | 2% |
| Percentage Customer Satisfaction Score | 60% | Percentage Customer Satisfaction of Loan Process | 60% |
| Product Information Compliance | Registered but Not Complete | Percentage Customer Satisfaction of Loan Flexibility and Payment | 60% |
| Product Information Compliance | Registered but Not Complete |
P2P lending example: Platform 3.
| Platform 3 | |||
|---|---|---|---|
| Plafond | Rp 10,000,000 | ||
| Lender Performance Criteria | Borrower Performance | ||
| Percentage Return on Investment | 30% | Percetage Productive System Rate | 20% |
| Percentage Degree of Default(TWP90) | 10% | Percentage Total Amount Disbursed/Total Amount | 75% |
| Percentage of Service Rate | 5% | Percentage of Process Cost | 5% |
| Percentage Customer Satisfaction Score | 40% | Percentage Customer Satisfaction of Loan Process | 40% |
| Product Information Compliance | Registered but Not Complete | Percentage Customer Satisfaction of Loan Flexibility and Payment | 40% |
| Product Information Compliance | Registered but Not Complete |
Performance matrix for lenders.
| Performance Matrix for Lender | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Example Selection on P2P Lending | |||||||||||||||
| Performance Rating | |||||||||||||||
| Criteria | Unit of Measurement | Criteria | Alternative | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Total Performance | |
| Investment Rate | Percentage Return on Investment | 50.10% | Platform 1 (Baseline) | 6 | 300.6 | ||||||||||
| Platform 2 | 3 | 150.3 | |||||||||||||
| Platform 3 | 6 | 300.6 | |||||||||||||
| Default Rate | Percentage Degree of Default (TWP90) | 26% | Platform 1 (Baseline) | 1 | 26 | ||||||||||
| Platform 2 | 6 | 156 | |||||||||||||
| Platform 3 | 1 | 26 | |||||||||||||
| Service Fee | Percentage of Service Rate | 13.40% | Platform 1 (Baseline) | 6 | 80.4 | ||||||||||
| Platform 2 | 9 | 120.6 | |||||||||||||
| Platform 3 | 6 | 80.4 | |||||||||||||
| Customer Satisfaction | Customer Satisfaction Score (CSAT) | 6.8% | Platform 1 (Baseline) | 5 | 34 | ||||||||||
| Platform 2 | 6 | 40.8 | |||||||||||||
| Platform 3 | 4 | 27.2 | |||||||||||||
| Regulatory compliance | Product Information Compliance | 3.7% | Platform 1 (Baseline) | 6 | 22.2 | ||||||||||
| Platform 2 | 6 | 22.2 | |||||||||||||
| Platform 3 | 6 | 22.2 | |||||||||||||
| Overall Lender side Performance | Total Performance | Total Cost (×10,000) | Value Index | ||||||||||||
| Platform 1 (Baseline) | 463.2 | 210 | 2.21 | ||||||||||||
| Platform 2 | 489.9 | 180 | 2.72 | ||||||||||||
| Platform 3 | 456.4 | 210 | 2.17 | ||||||||||||
Performance matrix for borrowers.
| Performance Matrix for Borrower | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Example Selection on P2P Lending | |||||||||||||||
| Performance Rating | |||||||||||||||
| Criteria | Unit of Measurement | Criteria | Alternative | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Total Performance | |
| Productive System Rate | Percetage Productive System Rate | 45.67% | Platform 1 (Baseline) | 7 | 319.69 | ||||||||||
| Platform 2 | 8 | 365.36 | |||||||||||||
| Platform 3 | 4 | 182.68 | |||||||||||||
| Loan Disbursement Rate (Percent) | Total Amount Disbursed/Total Amount | 25.94% | Platform 1 (Baseline) | 1 | 8 | 207.52 | |||||||||
| Platform 2 | 8 | 207.52 | |||||||||||||
| Platform 3 | 1 | 8 | 207.52 | ||||||||||||
| Service Fee/Process Cost | Percentage of Service Rate/Process Cost | 14.61% | Platform 1 (Baseline) | 6 | 87.66 | ||||||||||
| Platform 2 | 9 | 131.49 | |||||||||||||
| Platform 3 | 6 | 87.66 | |||||||||||||
| Loan Process | Percentage of Customer Satisfaction of Loan Process | 7.74% | Platform 1 (Baseline) | 5 | 38.7 | ||||||||||
| Platform 2 | 6 | 46.44 | |||||||||||||
| Platform 3 | 4 | 30.96 | |||||||||||||
| Loan Flexibility and Payment | Percentage of Customer Satisfaction of Loan Flexibility and Payment | 4.65% | Platform 1 (Baseline) | 5 | 23.25 | ||||||||||
| Platform 2 | 6 | 27.9 | |||||||||||||
| Platform 3 | 4 | 18.6 | |||||||||||||
| Regulatory compliance | Product Information Compliance | 1.39% | Platform 1 (Baseline) | 6 | 8.34 | ||||||||||
| Platform 2 | 6 | 8.34 | |||||||||||||
| Platform 3 | 6 | 8.34 | |||||||||||||
| Overall Borrower side Performance | Total Performance | Total Cost (×10,000) | Value Index | ||||||||||||
| Platform 1 (Baseline) | 685.16 | 310 | 2.21 | ||||||||||||
| Platform 2 | 787.95 | 230 | 3.42 | ||||||||||||
| Platform 3 | 535.76 | 310 | 1.73 | ||||||||||||
Overall performance value index.
| Overall Lender Side Performance | Total Performance | Total Cost (×10,000) | Value Index Lender | |
|---|---|---|---|---|
| Platform 1 (Baseline) | 463.2 | 210 | 2.21 | |
| Platform 2 | 489.9 | 180 | 2.72 | |
| Platform 3 | 456.4 | 210 | 2.17 | |
| Overall Borrower side Performance | Total Performance | Total Cost (×10,000) | Value Index Borrower | |
| Platform 1 (Baseline) | 685.16 | 310 | 2.21 | |
| Platform 2 | 787.95 | 230 | 3.42 | |
| Platform 3 | 535.76 | 310 | 1.73 | |
| Overall Performance | Value Index Lender | Value Index Borrower | Value Index | % Improvement |
| Platform 1 (Baseline) | 2.21 | 2.21 | 2.21 | − |
| Platform 2 | 2.72 | 3.42 | 3.07 | 38.91% |
| Platform 3 | 2.17 | 1.73 | 1.95 | −11.76% |
Performance matrix for lenders (case studies).
| Performance Matrix for Lender | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Example Selection on P2P Lending | |||||||||||||||
| Performance Rating | |||||||||||||||
| Criteria | Unit of Measurement | Criteria | Alternative | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Total Performance | |
| Investment Rate | Percentage Return on Investment | 50.10% | Platform A (Baseline) | 2 | 100.2 | ||||||||||
| Platform B | 2 | 100.2 | |||||||||||||
| Platform C | 3 | 150.3 | |||||||||||||
| Platform D | 2 | 100.2 | |||||||||||||
| Platform E | 3 | 150.3 | |||||||||||||
| Platform F | 4 | 200.4 | |||||||||||||
| Platform G | 3 | 150.3 | |||||||||||||
| Platform H | 2 | 100.2 | |||||||||||||
| Platform I | 3 | 150.3 | |||||||||||||
| Platform J | 3 | 150.3 | |||||||||||||
| Default Rate | Percentage Degree of Default (TWP90) | 26% | Platform A (Baseline) | 9 | 234 | ||||||||||
| Platform B | 1 | 26 | |||||||||||||
| Platform C | 9 | 234 | |||||||||||||
| Platform D | 10 | 260 | |||||||||||||
| Platform E | 1 | 26 | |||||||||||||
| Platform F | 6 | 156 | |||||||||||||
| Platform G | 1 | 26 | |||||||||||||
| Platform H | 1 | 26 | |||||||||||||
| Platform I | 8 | 208 | |||||||||||||
| Platform J | 1 | 26 | |||||||||||||
| Service Fee | Percentage of Service Rate | 13.40% | Platform A (Baseline) | 10 | 134 | ||||||||||
| Platform B | 10 | 134 | |||||||||||||
| Platform C | 8 | 107.2 | |||||||||||||
| Platform D | 7 | 93.8 | |||||||||||||
| Platform E | 10 | 134 | |||||||||||||
| Platform F | 9 | 120.6 | |||||||||||||
| Platform G | 10 | 134 | |||||||||||||
| Platform H | 10 | 134 | |||||||||||||
| Platform I | 8 | 107.2 | |||||||||||||
| Platform J | 8 | 107.2 | |||||||||||||
| Customer Satisfaction | Customer Satisfaction Score (CSAT) | 6.8% | Platform A (Baseline) | 8 | 54.4 | ||||||||||
| Platform B | 8 | 54.4 | |||||||||||||
| Platform C | 8 | 54.4 | |||||||||||||
| Platform D | 7 | 47.6 | |||||||||||||
| Platform E | 9 | 61.2 | |||||||||||||
| Platform F | 6 | 40.8 | |||||||||||||
| Platform G | 8 | 54.4 | |||||||||||||
| Platform H | 6 | 40.8 | |||||||||||||
| Platform I | 6 | 40.8 | |||||||||||||
| Platform J | 6 | 40.8 | |||||||||||||
| Regulatory compliance | Product Information Compliance | 3.7% | Platform A (Baseline) | 6 | 22.2 | ||||||||||
| Platform B | 6 | 22.2 | |||||||||||||
| Platform C | 6 | 22.2 | |||||||||||||
| Platform D | 6 | 22.2 | |||||||||||||
| Platform E | 6 | 22.2 | |||||||||||||
| Platform F | 6 | 22.2 | |||||||||||||
| Platform G | 6 | 22.2 | |||||||||||||
| Platform H | 6 | 22.2 | |||||||||||||
| Platform I | 6 | 22.2 | |||||||||||||
| Platform J | 6 | 22.2 | |||||||||||||
| Overall Lender side Performance | Total Performance | Total Cost (×10,000) | Value Index | ||||||||||||
| Platform A (Baseline) | 544.8 | 228.3 | 2.39 | ||||||||||||
| Platform B | 336.8 | 1181.4 | 0.29 | ||||||||||||
| Platform C | 568.1 | 206.7 | 2.75 | ||||||||||||
| Platform D | 523.8 | 212.4 | 2.47 | ||||||||||||
| Platform E | 393.7 | 2928.6 | 0.13 | ||||||||||||
| Platform F | 540 | 331.5 | 1.63 | ||||||||||||
| Platform G | 386.9 | 97.5 | 3.97 | ||||||||||||
| Platform H | 323.2 | 178.5 | 1.81 | ||||||||||||
| Platform I | 528.5 | 250.8 | 2.11 | ||||||||||||
| Platform J | 346.5 | 574.2 | 0.6 | ||||||||||||
Performance matrix for borrowers (case studies).
| Performance Matrix for Borrower | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Example Selection on P2P Lending | |||||||||||||||
| Performance Rating | |||||||||||||||
| Criteria | Unit of Measurement | Criteria | Alternative | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Total Performance | |
| Productive System Rate | Percetage Productive System Rate | 45.67% | Platform A (Baseline) | 8 | 365.36 | ||||||||||
| Platform B | 4 | 182.68 | |||||||||||||
| Platform C | 8 | 365.36 | |||||||||||||
| Platform D | 7 | 319.69 | |||||||||||||
| Platform E | 9 | 411.03 | |||||||||||||
| Platform F | 7 | 319.69 | |||||||||||||
| Platform G | 10 | 456.7 | |||||||||||||
| Platform H | 10 | 456.7 | |||||||||||||
| Platform I | 6 | 274.02 | |||||||||||||
| Platform J | 6 | 274.02 | |||||||||||||
| Loan Disbursement Rate (Percent) | Total Amount Disbursed/Total Amount | 25.94% | Platform A (Baseline) | 2 | 51.88 | ||||||||||
| Platform B | 1 | 25.94 | |||||||||||||
| Platform C | 3 | 77.82 | |||||||||||||
| Platform D | 3 | 77.82 | |||||||||||||
| Platform E | 7 | 181.58 | |||||||||||||
| Platform F | 6 | 155.64 | |||||||||||||
| Platform G | 5 | 129.7 | |||||||||||||
| Platform H | 5 | 129.7 | |||||||||||||
| Platform I | 6 | 155.64 | |||||||||||||
| Platform J | 3 | 77.82 | |||||||||||||
| Service Fee/Process Cost | Percentage of Service Rate/Process Cost | 14.61% | Platform A (Baseline) | 8 | 116.88 | ||||||||||
| Platform B | 10 | 146.1 | |||||||||||||
| Platform C | 6 | 87.66 | |||||||||||||
| Platform D | 6 | 87.66 | |||||||||||||
| Platform E | 10 | 146.1 | |||||||||||||
| Platform F | 8 | 116.88 | |||||||||||||
| Platform G | 7 | 102.27 | |||||||||||||
| Platform H | 7 | 102.27 | |||||||||||||
| Platform I | 7 | 102.27 | |||||||||||||
| Platform J | 7 | 102.27 | |||||||||||||
| Loan Process | Percentage of Customer Satisfaction of Loan Process | 7.74% | Platform A (Baseline) | 7 | 54.18 | ||||||||||
| Platform B | 8 | 61.92 | |||||||||||||
| Platform C | 7 | 54.18 | |||||||||||||
| Platform D | 7 | 54.18 | |||||||||||||
| Platform E | 8 | 61.92 | |||||||||||||
| Platform F | 6 | 46.44 | |||||||||||||
| Platform G | 8 | 61.92 | |||||||||||||
| Platform H | 9 | 69.66 | |||||||||||||
| Platform I | 6 | 46.44 | |||||||||||||
| Platform J | 38.7 | ||||||||||||||
| Loan Flexibility and Payment | Percentage of Customer Satisfaction of Loan Flexibility and Payment | 4.65% | Platform A (Baseline) | 7 | 32.55 | ||||||||||
| Platform B | 9 | 41.85 | |||||||||||||
| Platform C | 7 | 32.55 | |||||||||||||
| Platform D | 7 | 32.55 | |||||||||||||
| Platform E | 8 | 37.2 | |||||||||||||
| Platform F | 6 | 27.9 | |||||||||||||
| Platform G | 9 | 41.85 | |||||||||||||
| Platform H | 9 | 41.85 | |||||||||||||
| Platform I | 5 | 23.25 | |||||||||||||
| Platform J | 6 | 27.9 | |||||||||||||
| Regulatory compliance | Product Information Compliance | 1.39% | Platform A (Baseline) | 6 | 8.34 | ||||||||||
| Platform B | 6 | 8.34 | |||||||||||||
| Platform C | 6 | 8.34 | |||||||||||||
| Platform D | 6 | 8.34 | |||||||||||||
| Platform E | 6 | 8.34 | |||||||||||||
| Platform F | 6 | 8.34 | |||||||||||||
| Platform G | 6 | 8.34 | |||||||||||||
| Platform H | 6 | 8.34 | |||||||||||||
| Platform I | 6 | 8.34 | |||||||||||||
| Platform J | 6 | 8.34 | |||||||||||||
| Overall Borrower side Performance | Total Performance | Total Cost (×10,000) | Value Index | ||||||||||||
| Platform A (Baseline) | 629.19 | 984.3 | 0.64 | ||||||||||||
| Platform B | 466.83 | 580 | 0.8 | ||||||||||||
| Platform C | 625.91 | 1106 | 0.57 | ||||||||||||
| Platform D | 580.24 | 1092 | 0.53 | ||||||||||||
| Platform E | 846.17 | 2375 | 0.36 | ||||||||||||
| Platform F | 674.89 | 1050 | 0.64 | ||||||||||||
| Platform G | 800.78 | 960 | 0.83 | ||||||||||||
| Platform H | 808.52 | 1035 | 0.78 | ||||||||||||
| Platform I | 609.96 | 1183 | 0.52 | ||||||||||||
| Platform J | 529.05 | 950 | 0.56 | ||||||||||||
Overall performance value index (case studies).
| Overall Lender Side Performance | Total Performance | Total Cost (×10,000) | Value Index Lender | ||
|---|---|---|---|---|---|
| Platform A (Baseline) | 544.8 | 228.3 | 2.39 | ||
| Platform B | 336.8 | 1181.4 | 0.29 | ||
| Platform C | 568.1 | 206.7 | 2.75 | ||
| Platform D | 523.8 | 212.4 | 2.47 | ||
| Platform E | 393.7 | 2928.6 | 0.13 | ||
| Platform F | 540 | 331.5 | 1.63 | ||
| Platform G | 386.9 | 97.5 | 3.97 | ||
| Platform H | 323.2 | 178.5 | 1.81 | ||
| Platform I | 528.5 | 250.8 | 2.11 | ||
| Platform J | 346.5 | 574.2 | 0.6 | ||
| Overall Borrower side Performance | Total Performance | Total Cost (×10,000) | Value Index Borrower | ||
| Platform A (Baseline) | 629.19 | 984.3 | 0.64 | ||
| Platform B | 466.83 | 580 | 0.8 | ||
| Platform C | 625.91 | 1106 | 0.57 | ||
| Platform D | 580.24 | 1092 | 0.53 | ||
| Platform E | 846.17 | 2375 | 0.36 | ||
| Platform F | 674.89 | 1050 | 0.64 | ||
| Platform G | 800.78 | 960 | 0.83 | ||
| Platform H | 808.52 | 1035 | 0.78 | ||
| Platform I | 609.96 | 1183 | 0.52 | ||
| Platform J | 529.05 | 950 | 0.56 | ||
| Overall Performance | Value Index Lender | Value Index Borrower | Value Index | % Improvement | Actual NPL |
| Platform A (Baseline) | 2.39 | 0.64 | 1.515 | − | 1.91% |
| Platform B | 0.29 | 0.8 | 0.545 | −64.03% | 22.38% |
| Platform C | 2.75 | 0.57 | 1.66 | 9.57% | 1.89% |
| Platform D | 2.47 | 0.53 | 1.5 | −0.99% | 0.18% |
| Platform E | 0.13 | 0.36 | 0.245 | −83.83% | 46.56% |
| Platform F | 1.63 | 0.64 | 1.135 | −25.08% | 5.05% |
| Platform G | 3.97 | 0.83 | 2.4 | 58.42% | 0.25% |
| Platform H | 1.81 | 0.78 | 1.295 | −14.52% | 0.15% |
| Platform I | 2.11 | 0.52 | 1.315 | −13.20% | 3.36% |
| Platform J | 0.6 | 0.56 | 0.58 | −61.72% | 10.14% |
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Details
; Armein Z R Langi 1 ; Arman, Arry Akhmad 1 ; Simatupang, Togar M 2
1 School of Electrical and Informatics Engineering, Bandung Institute of Technology, Bandung 40116, Indonesia;
2 School of Business and Management, Bandung Institute of Technology, Bandung 40116, Indonesia;