Peer to Peer (P2P) Lending Problems and Potential Solutions: A Systematic Literature Review

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Procedia Computer Science 161 (2019) 204–214

The Fifth Information Systems International Conference 2019 The Fifth Information Systems International Conference 2019

Peer to Peer (P2P) Lending Problems and Potential Solutions: A Peer to Peer (P2P) Lending Problems and Potential Solutions: A Systematic Literature Review Systematic Literature Review Ryan Randy Suryonoa,b , Betty Purwandaria,*, Indra Budia Ryan Randy Suryonoa,b, Betty Purwandaria,*, Indra Budia

a Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia a Faculty of Engineering andof Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung 35142, Indonesia Faculty Computer Science, Universitas Indonesia, Depok 16424, Indonesia b Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung 35142, Indonesia b

Abstract Abstract There is a growing Financial Technology (Fintech) business model, such as Peer to Peer (P2P) Lending. P2P Lending allows There is a growing Financial Technology (Fintech) such as Peer to Peer (P2P)has Lending. Lendingwith allows individuals and businesses to borrow and lend moneybusiness to each model, other. In its development, China becomeP2P the market the individuals and businesses borrow and lend each other. In itsthis development, China hasmonitored. become theThis market the most P2P lending platforms.toHowever, there is money a moraltohazard that makes business need to be threatwith begins most P2P lendingofplatforms. However, a moral hazard that makesinthis businessFintech need toP2P be monitored. threat special begins with verification the borrower's data there that isis not appropriate. Whereas Indonesia Lending hasThis received with verification borrower'sand datapolicies that is have not appropriate. in Indonesia FintechisP2P Lendingashas received special attention, becauseofitstheregulations not maturedWhereas yet. Besides, P2P Lending considered a new business to attention, because its regulations policies have not matured yet.theBesides, P2P Lending considered as This a new business flourish. Consequently, it requiresand investigation on problems from implementation of theis P2P Lending. study aims to flourish. Consequently, requiresand investigation on problems fromand thenon-technical implementation of the to P2P study aims to identify problems in P2PitLending present alternative technical solutions theLending. problems.This By implementing identify problems in P2P Lending andReview present (SLR) alternative technical non-technical solutions to theand problems. implementing the Kitchenham Systematic Literature approach fromand the ACM, AIS, IEEE, SCOPUS, ScienceBy Direct databases, the Systematic Literature Review approach from the ACM, AIS, IEEE,solutions. SCOPUS, and Science Direct databases, thisKitchenham research finds a rich picture, creates a table(SLR) of problem identification and alternative this research finds a rich picture, creates a table of problem identification and alternative solutions. © 2019 The Authors. Published by Elsevier B.V. © 2019 2019 The The Authors. Published by B.V. © Authors. by Elsevier Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under CC BY-NC-ND licenseThe (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee Fifth Information Systems International Conference 2019 Peer-review under responsibility of the scientific committee ofofThe Fifth Information Systems International Conference 2019. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 Keywords: Fintech; P2P Lending; Systematic Literature Review Keywords: Fintech; P2P Lending; Systematic Literature Review

1. Introduction 1. Introduction Financial Technology is recognized as one of the most important innovations in the financial industry and is Financial Technology is recognized as one of theinmost important in theledfinancial industry and is growing rapidly [1]. This was driven by reduced trust financial serviceinnovations providers which to an increase in market growing rapidly [1]. This was driven by reduced trust in financial service providers which led to an increase in market

* Corresponding author. Tel.: +62-21-786-3419; fax: +62-21-786-3415. address:author. [email protected] * E-mail Corresponding Tel.: +62-21-786-3419; fax: +62-21-786-3415. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. 1877-0509 © 2019 Thearticle Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019. 10.1016/j.procs.2019.11.116

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appetite for alternative financing [2]. Much of Fintech is driven by a variety of technological advancements: the availability and affordability of infrastructure (for example, the Internet, cellular technology, sensors, increasingly mature technology applications (eg platforms, Big Data analysis), and business operations (eg sharing economy), etc [2, 3]. According to Lee & Shin (2018), there are six developing Fintech business models, namely payment, wealth management, crowd funding, Peer to Peer (P2P) lending, capital markets, and insurance services. Peer to Peer Lending (P2P Lending) is a practice or method of lending money to individuals or businesses [4]. P2P Lending Prosper and LendingClub have become fast-growing American investment markets with growth of more than 100% year to year [5]. LendingClub announced that the total loan amount had reached more than US $ 13.4 billion at the end of 2015 [5, 6]. Several Asian countries such as Korea, China and Indonesia show that P2P Lending is a Fintech sector that is growing rapidly from other sectors [7, 8, 9]. It was noted that, China became the market with the most peer to peer loan platforms, amounting to around 2,300 as of March 2017 with a loan volume of CNY 9,208 [8]. China needs government support so that this industry can survive. The P2P Lending platform in China has a moral hazard and is very easy for borrowers to falsify loan information [10]. This is affected because there are no mature regulations related to this industry [11]. The Reg Lab concept began to emerge last year. This concept is known as the Regulatory Sandbox. The countries such as Australia, Indonesia, Singapore, Canada and Thailand use this method to create a place for businesses to ensure they meet regulatory requirements and accelerate product development that benefits consumers [12]. Referring to several Information System studies, this study uses the Kitchenham Systematic Literature Review (SLR) approach [13, 14]. This SLR method is used by Dzulfikar et al. (2018) in personalizing features on B2C ECommerce [15]. The stages of synthesis on SLR can also be done in various ways, including using SWOT analysis (Strength, Weakness, Opportunity, and Threat) in the formulation of e-commerce adoption strategies on e-Library [16]. SLR are also used in identifying the focus of the Recommendation System that influences the implementation of e-Portfolios and classifies widely used systems approaches [17]. This mapping study aims to identify problems in P2P Lending and present alternative technical and non-technical solutions to the problem. Research questions are arranged as a guide for conducting mapping studies and identifying research opportunities. Based on research objectives, mapping studies are driven by research questions about what are the P2P Lending problems and solutions to the problem? By implementing the Systematic Literature Review (SLR) approach from the ACM, AIS, IEEE, SCOPUS, and Science Direct databases this study tries to create a problem identification table and alternative solutions. 2. Research method This research refers to the Kitchenham (SLR), which consists of planning, implementation and reporting steps [13]. The first phase begins by formulating the main objectives of this study. This is to identify what problems arise from the P2P Lending industry. In the second phase, formulate a protocol review, which consists of criteria and research questions. For the selection process, there are two criteria, namely inclusion and exclusion. To formulate a research question, this study uses the PICOC formula (Population, Intervention, Comparison, Results and Context). Search strategies are designed based on the selection of the main terms of each research question and use alternative words and synonyms in each search string. The search string in this study is (fintech OR “Financial Technology” OR “P2P Lending” OR “Peer to Peer Lending”) AND (challenge OR trend OR problem OR issue). The search focuses on the period and source of publication. Publication searches are limited to the period 2014 to 2018. Data sources are taken from the ACM, AIS, IEEE, SCOPUS, and Science Direct databases and then imported into Mendeley software. At this stage, some irrelevant papers are excluded based on the title and abstract. From the appropriate paper, the second stage of selection is carried out to determine the final paper. Explanation of the journal selection process can be seen in Fig. 1. The inclusion and exclusion criteria from this literature study were developed based on research questions and were used to ensure that the results obtained were reliable in accordance with the guidelines set by Kitchenham [14]. Articles contain P2P Lending keywords, articles using English, articles including full text papers, and published years (2014-2018). Because P2P Lending research is a multidisciplinary study, this research is not limited to the computer field. Details of total sources, namely, ACM (1 conference paper and 1 journal), AIS (16 conference papers and 1 journal), IEEE (8 conference papers), SCOPUS (6 conference papers and 23 journals) and Science Direct (4 conference papers and 21 journal). Finally, from 754 papers included in the quality test, 81 papers

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will be included in this research map, consisting of ACM (2), AIS (17), IEEE (8), SCOPUS (29), and Science Direct (25). In this study, data extraction was carried out by reviewing the paper with annotated bibliography. The purpose of this annotation is to find out the relevance, accuracy and quality of the sources cited.

Potentially related papers (754): ACM (10), AIS (90), IEEE (40), SCOPUS (399), and Science Direct (215)

Exclude papers based on title & abstract: ACM (7), AIS (59), IEEE (17), SCOPUS (269), and Science Direct (164)

Exclude papers based on full-text: ACM (1), AIS (14), IEEE (5), SCOPUS (95), and Science Direct (26)

Final Papers (81): ACM (2), AIS (17), IEEE (8), SCOPUS (29), and Science Direct (25)

Relevant for further review: ACM (3), AIS (31), IEEE (13), SCOPUS (130), and Science Direct (51)

Fig. 1. The selection process of final papers.

3. Result and analysis Based on the SLR process and analysis of business processes at P2P Lending, problems and solutions can be obtained in table 1. The P2P Lending platform is actually a business model that combines the Internet and finance, collects funds in small amounts and lends them to those who need them. The loan process such as funds, contracts and information procedures can be fully carried out through the Internet. The basic operational process of P2P loan transactions is roughly the same as traditional processes [4]. The P2P Lending business process in general can be seen in Fig. 2. The first process is registration, the intention to borrow and use the application is the beginning of the use or absence of P2P Lending applications [18, 19]. For this reason, the platform must pay attention to service quality, information quality, structural assurance, perceived ease and usefulness of the system. On the other hand, information integrity, security protection, subjective norms, trust tendencies, trust in platforms, awareness, reputation, risk perception, user satisfaction, and attitudes that are built into factors that are thought to influence adoption of P2P Lending. Concerns about requests for fake loans and delinquency intentions can be anticipated by paying attention to individual characteristics and short interviews.

Fig. 2. The P2P Lending Business Process in general.

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There is an information asymmetry problem in the risk assessment process. The unavailability of individual credit information affects the credit risk assessment. Some loan applications fail because of low success rate of credit risk assessment. One factor in the low success rate of loans is triggered by gender discrimination. Big Data has been brought to the Internet credit service company, because of the value of volume, predictability, variety of services, and privacy protection [20]. Because risk assessment requires a method of predicting and determining loan decisions, several studies have developed machine learning algorithms. Risk assessment success and loan decisions are influenced by individual credit worthiness as seen from credit score information. At present, social media information is needed to determine credit worthiness [21, 22, 23]. Table 1. Problem and Potential Solutions of P2P Lending. Process 1. Registration

Problem

Technical Solutions

Non-Technical Solutions

1.

Lending Intention [18, 19]

1.

Service Quality [19]

1.

Information Integrity [18]

2.

Intention to Use [7]

2.

Information Quality [19]

2.

Privacy Protection [18]

3.

Fraudulent Loan Requests [24]

3.

Structural Assurance [19]

3.

Subjective Norms [18, 27]

4.

Delinquency Intention [25, 26]

4.

Perceived Usefulness [7]

4.

Trust Tendency [18, 19]

5.

Perceived Ease of Use [7]

5.

Platform Trust [19]

6.

Borrower Trust [19]

7.

Awareness [19]

8.

Reputation [19]

9.

Perceive Risk [19]

10. User Satisfaction [7] 11. Attitude Toward Using [7] 12. Personal Characteristic [24, 28, 29, 30] 13. Interview [25] 2. Risk Assessment

1.

Information Asymmetry [22, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]

2.

Individual Credit Information is Unavailable [42]

3.

Credit Risk Assessment [23, 32, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54]

4.

Gender Discrimination [43, 55, 56]

5.

Loans Fail due to insufficient Pledges [57]

6.

Low Success Rate [58]

7.

Reject Inference [59, 60]

1.

Big Data [2, 20, 32, 45, 61, 62]

2.

Machine Learning Algorithms [32, 45, 48]

3.

Algorithms LightGBM and XGboost [63]

4.

Prediction performance Loan with Data Mining [64, 47]

5.

Fraud Detection [20, 24]

6.

Credit Risk Prediction [20, 65]

7.

Profit Scoring [66]

8.

Decision Support System [44, 49]

9.

Loan Evaluation and Portfolio Allocation Model [44]

10. Profit- based statistical discrimination [55] 11. Costly taste- based discrimination [55] 12. Text Mining [62] 13. Hybrid Random Walk Approach [57] 14. AdaBoost Algorithm [35]

1.

Individual Creditworthiness [25, 35, 58, 62, 69, 71, 75]

2.

Friendship Network [31]

3.

Group Cohesiveness [31]

4.

Group Rating [31, 41, 72]

5.

Loan Characteristic [26, 42, 58]

6.

Credit Score Information [42, 43, 49, 54, 56, 58, 73]

7.

Demography Information [42, 74]

8.

Signaling Cost of Borrowers [32]

9.

Search Cost of Lender [32]

10. Offline Activity Assessment [20] 11. Telecommunication Patterns [35] 12. Mobility Patterns [35] 13. App Usage Patterns [35] 14. Loan Purpose [74] 15. Social Media Information [21, 22, 23]

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Process

Problem

Technical Solutions

Non-Technical Solutions

15. Random Forest [67, 68]

16. Social Recommendation [38]

16. Feature Selection [24]

17. Social Influence [38]

17. Imbalanced Learning Techniques [65]

18. Physical Attractiveness [71]

18. Score Matrix [69] 19. Classification Method [46, 70] 20. Lender-Borrower Communication Features [37] 21. Decision Tree [50] 22. Bayesian Hyper-Parameter Optimization [52] 23. Semi Supervised SVM [59, 60] 24. Sentiment Analysis [23, 71] 3. Billing

4. Refunds

5. Investment Decision

1.

Default Risk of Loans [63]

2.

Improper Billing [75, 76, 77]

2.

Financial Status [63]

3.

Abuse of Privacy Data [78]

3.

Credit Status [63]

1.

Notification by System

1.

Lack the Risk Control Ability [43]

2.

Failed to Pay[27, 43]

3.

Late to Pay off [79]

1.

Trust in Borrower [80]

1.

Service Quality [80]

2.

Trust in Intermediary [80, 81, 82]

2.

Information Quality [80]

3.

Moral Hazard [32, 83]

3.

Web Design [84]

4.

Adverse Selection [32]

4.

Website Quality [82]

5.

Investment Intention [27, 30,

5.

Perceived Risk [84]

1.

Notification by System

1.

Loan Details [63]

4.

Personal Information [63]

5.

Code of Ethics [75]

6.

Data Privacy Guarantee [78]

1.

Payment Guide Available

2.

Payment Method Available

3.

Punishment [26]

1.

Bidding Strategy [6, 42, 62, 69, 87, 89, 94]

2.

Successful Borrowing Request [42, 43, 73]

3.

Unsuccessful Borrowing Request [43, 73]

36, 74, 84, 85, 86]

6.

Perceived Ease of Use [84]

4.

Overdue Repayment [73]

6.

Herding Behavior [27, 33, 87]

7.

Perceived Security [84, 92]

5.

Number of Bids [42]

7.

Investors Blindly Seek High Returns and Follow Suit in Investment [43]

8.

Perceived Privacy Security [84]

6.

Funding Time [36, 42, 43, 73]

9.

Perceived Reputation [84]

7.

Message content [37]

8.

Investment Recommendation [6, 88, 89, 90]

9.

Lender Decision [25, 28, 29, 36, 68]

10. Continuous Investment [82, 91] 11. Detecting the Abnormal Lenders [70, 83]

10. Third – party Certification [84, 91, 92] 11. Platform Assurance [91] 12. Automatic bidding mechanism [87] 13. Clustering [81, 93] 14. Bayesian hidden Markov model (BHMM) [6] 15. Matching Model [94]

8.

Borrower’s Reputation [18, 89]

9.

Interest Rate [36, 43, 72, 73, 92]

10. Borrow Amount [36, 73] 11. Trust Propensity [30, 84, 89] 12. Reducing Information Asymmetry [30, 33] 13. A Portfolio Perspective with Risk Management [51, 88, 93] 14. Credit Grade [36, 53, 83] 15. Perceive Risk [82, 89, 85] 16. Perceive Social Capital [89] 17. Economic Feasibility [91]

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Process

Problem

Technical Solutions

209

Non-Technical Solutions 18. Transfer Fee [92] 19. Historical Behaviors [26] 20. Trust in Platform [82, 85]

6. P2P Lending Platform Registration

1.

Functional Requirements of P2P E-finance Platform [4]

1.

Module of Capital Clearing and Settlement [4]

1.

Formalize the P2P Process model [61]

2.

Platforms Have been Force to Close [43]

2.

Module of Signing of Electronic Contract [4]

2.

Regulatory Sandbox [96]

3.

Money Laundering [93]

3.

3.

Business Operation [96]

4.

P2P Lending Platforms Evaluation [92]

Module of Credit Rating And Check Limiting [4]

4.

4.

5.

Platform Performance [86]

Module of Information Release and Matching [4]

Customer Recognition and Protection [96, 97]

5.

Capital Requirement [96]

6.

P2P Platform Lack Sufficient Supervision [95]

5.

Module of System Management [4]

6.

Ownership and activity restrictions [96]

7.

Fraudulent Activities [95]

6.

7.

Investor base and restrictions [96]

8.

Regulation [79]

Module of Financial Management [4]

7.

Module of Management after loan [4]

8.

Interest Rate Limits [96]

9.

8.

Module of Mortgage Management [4]

Loan and Investment Duration [96]

10. Operational Risk Mitigation [96]

9.

Module of Guarantee Management [4]

11. Registration Requirements [97] 12. Safety [92]

10. Module of Loan Check Management [4]

13. Profit [92]

11. Module of Customer Management [4]

15. Experience [92]

12. Module of Data Analysis [4] 13. Web Designing [92] 14. Costumer Service [92]

14. Liquidity [92] 16. Value Added Service [92] 17. Transaction Volume [86] 18. Number of Borrowers [86] 19. Number of Lenders [86]

The level of projected credit standard is an absolute prerequisite to guarantee the proper operation of the project or related financial platform [63]. Factors affecting online P2P borrower payments are classified into four categories: loan details, financial status, credit status and personal information [63]. When the borrower is due, the collection procedure is carried out by the platform. Then the system designed to be accompanied by notifications and the P2P Lending industry is expected to establish a code of ethics to guarantee the borrower's data privacy. The refund process failed, due to a lack of risk control capabilities [43]. In the case of late payment, the P2P Lending platform can provide penalties in the form of additional interest that must be paid [26]. P2P Lending platforms are recommended to provide payment guidelines and payment methods for borrowers to understand. On the other hand, P2P Lending does not only discuss between platforms and borrowers. We know that P2P Lending users are lenders. In this case, investment decisions are influenced by the lender's trust in the borrower and trust in the platform [80, 81, 82]. The right investment decision is expected to reduce the wrong choices due to herding behavior. Moral hazard issues have an impact on lenders' decisions. If the investment decision provides the appropriate return value, the lender will make further investments [82, 91]. For developers of P2P Lending platforms, they can pay attention to the quality of their services and information [80]. The design and quality of P2P Lending websites can improve the perception of ease of use of the system [84]. It is recommended that P2P Lending platforms have a bidding strategy in minimizing information asymmetry [30, 33].

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The development of P2P Lending platforms allows the emergence of risks. Both cheating, money laundering, and even shadow banking [98]. For this reason, regulations are expected to be able to formulate policies and rules before this business is declared feasible to operate. Need for functional platform requirements and evaluation of the p2p lending platform [4, 92]. Some modules are suggested in the study of Fang et al (2014) supported by a website design that features customer service features [92]. Regulations can formulate a proper model process for P2P Lending platforms. 4. Discussion Information asymmetry is a major problem in the P2P Lending industry. Information asymmetry occurs if one party from a transaction has more or better information than the other party. Compared to traditional banks, Peer-toPeer (P2P) loans are claimed to be beneficial for borrowers and lenders. However, due to information asymmetry, far fewer investors dare to use this alternative finance [38]. Zhao et al (2014) gives advice so that investors can make the right decisions, a recommendation system needs to be made [88]. Based on the literature review, in 2015 the idea emerged to utilize Big Data in reducing information asymmetry [20,32]. The second problem is determining the borrower's score [27, 37, 38, 48]. To evaluate the performance of the credit rating model [20, 65], many methods are used such as Machine Learning Algorithms [32, 45, 48], Feature Selection [24], Algorithms LightGBM and XGboost [63], Profit Scoring [66], Score Matrix [69], Decision Support System [44, 49], Decision Tree [50], Hybrid Random Walk Approach [57], AdaBoost Algorithm [35], Random Forest [67, 68], Bayesian Hyper-Parameter Optimization [52], Prediction performance Loan with Data Mining [64, 47], and Classification Method [46, 70]. However, because the data collected by P2P Lending comes from third parties, the data credibility is invalid compared to the credit score available at the Bank. Credit score service companies usually take analysis from Cash flow data from prospective borrowers, Collateral provided (can be bills from PO / SPK / Contract / Invoice, in the form of inventory, equipment, or land & buildings), and Credit Behavior. Because of the invalid credit worthiness assessment, the third problem is the danger of Moral Hazard [25, 26]. Since 2016, several articles have noted that Delinquency Intention appeared [25]. Failure and late pay events occur because of moral hazard. So in 2016, several studies sought to identify features to detect fraud [24]. In overcoming this, it is important for the P2P Lending Platform to improve the credit score method. Research from 2016 to the present, discussed a lot about increasing credit score predictions [46, 49, 50, 52, 53, 59, 65, 66, 69, 81]. Because the concept of P2P Lending is to bring together borrowers and lenders. Then investment decisions become the fourth problem in this industry [88]. Some irregularities occur, such as herding behavior. This behavior shows how investors will compete in investing actions if they hear positive rumors and otherwise sell massively if the rumors are negative [33, 87]. Automatic bidding is an effective mechanism applied in online P2P loans. On the one hand, it can increase investment efficiency and save time for completing auctions. This can effectively weaken the grazing effect and produce ratio grazing behavior, which is significant for investors and platforms [87]. In addition, problems arise about gender discrimination. The results illustrate that female borrowers are more likely to be funded than male borrowers [55]. For this reason, P2P Lending offers several alternatives in determining investment decisions. Algorithms for profile matching have been proposed to get effective matching [47, 57, 94]. The fifth problem is about regulation and policy. The P2P loan platform began to mushroom in China in 2006. This platform has not been properly regulated. When a borrower denies, the platform owner must intervene and compensate the lender. The scandal involved three P2P loan platforms in Hangzhou, Shanghai and Shenzhen, collapsing under the burden of an extraordinary loan of 231 million Yuan, and the owner escaping liability [99]. In the past two years, there have been more cases reported that some platforms experienced “escape” or “closure” in which platform owners fled without leaving a trace or the platform had to be closed due to lack of cash, poor management or even fraud [99]. Huang (2018) conducted a study that China has recently established a relatively complete regulatory regime for online loans, introducing a number of significant changes, such as restrictions on business models that can be adopted by platforms, registration requirements, custodial requirements, information disclosure requirements, and limits loan [97]. As with Indonesia, Regulators struggle to find the right balance between the desire to achieve Fintech benefits for their national economy, and their need to protect the financial system and participants from risk [96]. The last problem is the feasibility of P2P Lending Platform. Apart from business operations, this industry must complete several functional requirements [4, 96]. Important for the P2P Lending Platform to formalize the P2P

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Lending process model [61]. In technical solutions, P2P Lending platforms must provide several modules such as Module of Capital Clearing and Settlement, Module of Signing of Electronic Contract, Module of Credit Rating And Check Limiting, Module of Information Release and Matching, Module of System Management, Module of Financial Management, Module of Management after loan, Module of Mortgage Management, Module of Guarantee Management, Module of Loan Check Management, Module of Customer Management and Module of Data Analysis [4]. Developers of P2P Lending applications need to design Web and applications that are easy for users to understand [92]. In addition, P2P Lending is recommended to provide customer service features [92]. The main threat to the validity of the review is the limitations of conference and journal selection. There are 81 papers published from 2014-2018 from five databases can be expanded again. This study has limitations because of inaccuracies and biases in research taken based on automatic search. Bias can be obtained from the process of inclusion and exclusion. The data extraction process has difficulties because many studies do not explicitly explain the problems that occur in each process. 5. Conclusion This study produces a table of P2P lending problem identification and alternative solutions by employing a SLR of 81 publications. Six common themes of P2P Lending problems are identified. These are information asymmetry, determining borrower scores, moral hazard, investment decisions, regulations and policies, and feasibility of P2P Lending Platform. Moreover, the research finds out alternative technical and non-technical solutions by comparing several relevant studies from various countries. As in most areas in the field of information systems research, there are three important elements that can determine the success of the system, namely people, processes, and technology. This research contributes to determine parties involved in the P2P industry, such as borrowers, lenders, P2P Lending platforms, and other stakeholders. Furthermore, six processes of P2P Lending Platforms are identified, i.e. as registration, risk assessment, billing, refunds, investment decisions and P2P Lending platform registration. This study also explains which technologies used to determine credit scores, application design and even modules, which are needed to build P2P Lending applications. It turns out that there is no previous study on problems with the billing and refund process. As a result, many cases of improper billing and awareness of privacy data can be investigated in further research [75, 76, 77]. It relates to the feasibility of P2P Lending Platform as a significant concern. Besides, there is very limited work on analyzing positive and negative sentiments on P2P lending by extracting relevant information from online news and social media. This is an opportunity for further research to use text mining on online news and social media to understand the tendency of people’s opinions on P2P Lending. Acknowledgement This study was funded by the PIT 9 grant from the University of Indonesia (NKB-0006/UN2.R3.1/HKP.05.00/ 2019). References [1] Lee, In, and Yong Jae Shin. (2018) “Fintech: Ecosystem, Business Models, Investment Decisions, and Challenges.” Bus Horiz 61: 35–46. [2] Leong, Carmen, Barney Tan, Xiao Xiao, Felix Ter Chian Tan, and Yuan Sun. (2017) “Nurturing A Fintech Ecosystem: The Case of a Youth Microloan Startup in China.” Int J Inf Manage 37: 92–97. [3] Puschmann, Thomas. (2017) “Fintech.” Bus Inf Syst Eng 59: 69–76. [4] Fang, Z, J Zhang, and F Zhiyuan. (2014) “Study on P2P E-Finance Platform System: A Case in China”, in Proc. - 11th IEEE Int. Conf. Ebus. Eng. ICEBE 2014 - Incl. 10th Work. Serv. Appl. Integr. Collab. SOAIC 2014 1st Work. E-Commerce Eng. ECE 2014, University of Illinois at Urbana-Champaign, United States. pp. 331–337. [5] TechCrunch. (2019) “Peer To Peer Lending Crosses $1 Billion In Loans Issued.” Available from: www.techcrunch.com. pp. 28–29 [6] Zhao, H, Q Liu, H Zhu, Y Ge, E Chen, Y Zhu, and J Du. (2018) “A Sequential Approach to Market State Modeling and Analysis in Online P2P Lending.” IEEE Trans Syst Man, Cybern Syst 48: 21–33. [7] Lee, S. (2017) “Evaluation of Mobile Application in User’s Perspective: Case of P2P Lending Apps in Fintech Industry.” KSII Trans Internet Inf Syst 11: 1105–1115.

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