The Effect of Using Financial Information Service System (SLIK) on Bad Credit Rate in Fintech Peer to Peer Lending

Elida Tobing

Sekolah Tinggi Ilmu Ekonomi Harapan Bangsa Bandung, Indonesia

Keywords			ABSTRACT
Credit Scoring, SLIK, P2P Lending, TKW90			The development of Fintech Peer to Peer Lending (P2P Lending) in Indonesia is very rapid in supporting increased financial inclusion. Along with the increase in the value of loans disbursed through P2P Lending, the ratio of bad loans or what is often referred to as Non Performing Loans (NPL) also continues to increase. Literature review article "Can the Use of a Financial Information Service System (SLIK) Affect the Level of Bad Credit in Fintech Peer to Peer Lending?" is a scientific article that aims to build a research hypothesis on how one variable influences other variables. The method used in writing this literature review article uses the library research method. The sources used as material for research are through available data from the website of the Otoritas Jasa Keuangan (OJK) & Asosiasi Fintech Pendanaan Bersama Indonesia (AFPI), academic online media, Google Scholar, Mendeley, and other online media. The results of this study provide information that can be used in efforts to minimize the occurrence of an increase in credit risk (NPL) in the P2P Lending industry, namely by taking preventive steps by making an effective creditworthiness assessment to prospective loan recipients through analysis of credit information data obtained from Sistem Layanan Informasi Keuangan (SLIK). Therefore, as an effort to restrain the rate of increase in the number of bad loans in the P2P Lending industry, a tool is needed to carry out an effective credit scoring (credit scoring) by integrating P2P Lending credit loan data into SLIK so that more credit data of prospective loan recipients can be obtained. comprehensive. More comprehensive data can provide more accurate results on credit scoring, and have a positive impact on loan repayment rates.

INTRODUCTION

The presence of P2P Lending is recognized to be one of the solutions that answer the issue of the gap in people's credit needs for the distribution of funds from financial institutions(Tambunan, Santoso, Busneti, & Batunanggar, 2021). In the contribution of P2P Lending companies in increasing financial inclusion by using an internet network with a wide coverage and can be reached almost throughout the region, making it easier for people to use the financial services and services offered. P2P Lending also enables itself to provide financial services to regions or communities that are not reached by the Bank(Alam, Gupta, & Zameni, 2019).

P2P Lending has been around for a long time in Indonesia, but it was only heard of its name in 2016, where the industry is one of the industries under the supervision of the Financial Services Authority (OJK)(Kamal & Ningsih, 2021). In POJK No.77 / POJK.01 / 2016, P2P Lending is also called Information Technology-Based Money Lending and Borrowing Service (LPMUBT). In the operational implementation of P2P Lending, there are several mechanisms that are not carried out as conventional processes, such as not making transactions face-to-face / to the place, being able to carry out high transaction frequencies that run at the same time, fast processing in hours, simple document requirements, and artificial intelligence support. By looking at this much different process, it is considered the need for supervisory methods directed at market discipline (market conduct) by involving relevant industry associations and the need for transparency of activities to the public so as to increase public trust based on their assessment related to the quality of the industry and P2P Lending organizers. The association that oversees the P2P lending industry is the Indonesian Joint Funding Fintech Association (AFPI).

In 2022 there are changes where POJK No.77/POJK.01/2016 is considered still unable to accommodate industrial needs optimally where the industry continues to grow rapidly(Andrealdi Novidsa Pratama Putra, 2022). For example, one of the issues that has been rife in recent years is related to user / consumer data protection (Andrianto, 2022). There are user complaints about the spread of their data without the user's permission.(Tsamara, 2021) So to maintain that this industry can continue to grow in the future consistently, regulatory support is needed on the development, quality, and contribution of this industry, the POJK was updated to POJK (Ria, Fasa, Suharto, &; Fachri, 2023) Number.10/POJK.05/2022, and the mention of LPMUBT was changed to Information Technology-Based Joint Funding Service (LPBBTI).

In the first 3 years, when viewed from OJK data, this industry has been able to distribute funds in total in December 2019 reaching Rp.81.5 billion, which means an increase of 259.56% (ytd). This figure is obtained by the distribution between the island of Java and outside the island of Java(Wahyu Manuhara Putra & Lestari, 2022). Visualization of the accumulated trend of P2P Lending lending to loan recipients based on the location of Java and outside Java can be seen in the diagram below:

Figure 1: Trend of Fund Distribution in the P2P Lending Industry for the period 2017 – 2019

*source: OJK data

This figure always experiences growth from year to year, and it was recorded that in September 2021 the P2P Lending industry distributed funds of 14.26 trillion to 499.23 million borrowers supported by 203.3 million lenders.

OJK data summarized by TrenAsia in July 2022 stated that the distribution of funds in P2P Lending grew by 88.8% on an annual basis (YoY) or an increase from IDR 1.14 trillion to IDR 46 trillion. As of the end of the second semester of 2022, there were 902,000 lenders and 82.19 million loan recipients. In addition to the high number of loans, it is also known that there is an increase in the number of credit risks or known as Non-Performing Loans (NPL). Based on OJK data in October 2022, on a YoY basis , there was an increase in NPL figures of 140.78 percent or IDR 1.42 million trillion with a distribution of IDR 1.23 trillion in individual loans and IDR 196.52 billion in corporate loans. However, in MoM terms there was a decrease of 4.53 percent.

The number of P2P Lending disbursements continues to increase when viewed in February 2023 has touched Rp.18.22 trillion, where this figure decreased from the previous month which was able to disburse Rp.18.73 trillion or a decrease of 2.72% (MoM). However, it increased compared to the distribution in February 2022 which distributed IDR 16.52 trillion or an increase of 10.29%. Similar to 2022, the NPL figure in 2023 also remains high, where all companies recorded a TWP figure of more than 5%. In addition, there are still several companies that record TK90 numbers below 95%, for example PIntek recorded 33.73% and Tanifund recorded 36.07%. This also causes a decrease in the performance of P2P Lending companies. In addition to the decline in performance, it is known that as of June 2020 there were 158 P2P Lending companies where this number shrank to 102 companies in April 2022.

Figure 2: Trend of Accumulated Fund Disbursement in the P2P Lending Industry for the period May'22 – Mar'23

There are many factors that can cause a decrease in company performance, one of which is credit risk. In accordance with the general description of the P2P Lending process flow above, there is a Credit Scoring process, where this process assesses the level of eligibility of prospective loan recipients to be given loans based on personal and financial data from prospective loan recipients needed in the feasibility analysis. With the meaning that this process is carried out as a deepening of information on the character and credibility of prospective loan recipients as a reference for the eligibility of credit recipients by applying the 5C principles, namely Character, Capacity, Capital, Collateral, Condition.

One of the credit scoring factors is the credit lending history of prospective loan recipients. This data can be obtained from SLIK. But currently P2P Lending companies cannot use SLIK data fully because loan history data from P2P Lending companies has not been included in SLIK data. And this data cannot be accessed easily by P2P Lending companies.

To respond to this need, AFPI built a Fintech Data Center (FDC) where all P2P Lending companies are required to report payment data for all loan recipients to AFPI every month, but the data still cannot be used optimally because it requires experts who can process data appropriately and safely. Another solution used by several P2P Lending organizing companies is to use the services of a Credit Bureau to obtain profile information from prospective loan recipients, the use of Credit Bureau services can have an impact on the amount of costs that must be incurred.

With several sources of data information, this causes additional time and process in creditworthiness analysis. Because of the need to synchronize data from several data sources, which can still be a potential for inaccurate analysis of the loan recipient profile, causing errors in assessment that can result in the risk of default.

Based on the description above, the author raises problems related to how the use of SLIK data in the P2P Lending industry can be used to minimize the level of credit risk.

METHODS

The research method used is descriptive, which investigates the subject matter that is used as the object of research(Aprilia & Aminatun, 2022). The research method uses library research methods through collecting regulations and data from regulators and associations, collecting scientific data related to the level of bad debts, so that the data used can be accounted for its validity. The subjects of the study are workers in the P2P Lending industry, namely Director, Operational Head, Legal Head, and Credit Analyst(Senyo, Gozman, Karanasios, Dacre, & Baba, 2023). While the object of research includes the use of SLIK data, the use of credit scoring data, the level of credit risk or NPL(Khan, Ramzan, Kousar, & Shafiq, 2023). The data sources used are primary data sources obtained from interviews and secondary data sources through library materials, literature, previous research and so on(DeJonckheere & Vaughn, 2019). And also carried out a data analysis approach using a qualitative analysis approach to obtain the results of the object under study(Djafar, Yunus, Pomalato, & Rasid, 2021).

RESULTS AND DISCUSSION

Overview of P2P Lending in Indonesia From Year to Year

With the issuance of POJK No.77 / POJK.01 / 2016 and the establishment of AFPI further strengthens the position of the P2P Lending industry in the non-banking financial services industry. In 2022, OJK stated that Indonesia controls around 77 billion US dollars or equivalent to 40% of the total value of digital economy transactions, according to the e-Conomy South East Asia report.

One of the functions of the existence of financial institutions, both banking and non-banking finances is to channel funds to the public(Taghizadeh-Hesary & Yoshino, 2020). Similarly, P2P Lending companies, even though they serve as funding organizers, P2P Lending must also be responsible for the movement of these funds(Davis, 2016). In the process of running P2P Lending operational activities in Indonesia, OJK provides a review of this industry by issuing a new POJK, namely POJK Number 10 / POJK.05 / 2022 with the aim that this industry can grow stronger and can develop consistently. Change or additional points are related to:

1. The legal entity of P2P Lending companies is limited to limited liability (PT) as the only organizing entity.

2. Foreign share ownership is limited and must be owned Together with Indonesian Citizens (WNI), full ownership is only permitted through transactions on the stock exchange.

3. Increased capital requirements from the beginning of registration at least Rp1.000.000.000, (one billion rupiah) to a minimum of Rp25.000.000.000,- (twenty-five billion rupiah) at the time of establishment

4. Rules on Controlling Shareholders (PSP), where there is an obligation to report the determination of the PSP and its amendments.

5. Rules against conventional organizers who want to switch Sharia organizers.

6. Prohibition of use of personal data without the consent of the data owner.

7. Liabilities have equity of at least IDR 12,500,000,000 (twelve billion five hundred million rupiah).

8. Setting the level of funding quality of the organizer.

The last point for change/additional points on POJK is related to credit risk. Three measuring instruments that can be used in measuring the level of risk are:

1. Default probability

2. Distance to default

3. Recovery rate

Seeing the rapid growth in the P2P Lending industry and the amount of funds that can be channeled to the community, PLP Lending has a role in supporting the growth of financial inclusion in Indonesia. The total funding disbursement in December 2017 accumulatively disbursed funds amounting to Rp2.57 trillion with a ratio of 85% distribution in Java and 15% distribution outside Java. Meanwhile, in December 2020, there was an accumulation of funds disbursement of IDR 132.38 trillion in Java and IDR 23.52 trillion outside Java or with a total distribution of IDR 155.90 trillion or recording a growth of 91.29% from the previous year's fund distribution which recorded IDR 81.50 trillion. The movement of this figure can be seen in the picture of the trend of fund distribution in the P2P Lending industry below:

Figure 3: Trend of Accumulated Fund Disbursement in P2P Lending Industry for the period Dec'17-Dec'20

During the Covid-19 pandemic, there is still growth in the distribution of loan funds to the community. This signals that the community's need for funding is still quite high. It can also be seen in the picture below the growth movement for the period December 2021 to April 2023.

Figure 4: Trend of Accumulated Fund Disbursement in P2P Lending Industry for the period Dec'21-Apr'23

Accumulative calculations in December 2021 recorded a growth of 100.05% from the previous year with a total disbursement of IDR 311.87 trillion or an average fund distribution of IDR 12,997 billion per month, where the highest distribution occurred in July 2021 of IDR 15,669 billion and the lowest occurred in January 2021 of IDR 9,384 billion. For Java, distribution was recorded at Rp.259.73 trillion, while outside Java it was Rp.52.14 trillion. The same thing also happened at the end of 2022 with a distribution of IDR 537.42 trillion with an average monthly distribution of IDR 15,282 trillion.

In April 2023, accumulatively it has been distributed amounting to IDR 611.42 trillion with the amount of distribution in Java of IDR 501.48 trillion and non-Java of IDR 109.94 trillion. Within 4 months there was a growth of 89.60% yoy in the distribution of funds in Java, and growth of 101.47% in non-Java distribution.

The loan funds can be absorbed by 519,422,719 loan recipient accounts calculated accumulatively in December 2022. With the composition of loan recipients from Java as many as 425,495,974 accounts and outside Java as many as 93,926,745 accounts. In the first month of the 2nd quarter there was an increase of 56,344,240 loan recipient accounts so that the total loan recipients accumulatively became 575,766,959 loan recipient accounts with 469,832,581 borrower accounts on the island of Java, and 105,934,378 accounts outside Java.

The distribution of loan funds is supported by lenders, where in December 2022 (only) there were 10,433,816 lenders consisting of 7,857,758 lenders from Java, and 86,046 from non-Java lenders, as well as 2,490,012 from foreign lenders. However, the number of lenders in April 2023 decreased by 5.36% or to 9,874,782 (only) lenders, with details of Java island lenders as many as 7,503,617, outside Java 54,732, and 9,874,782 foreign lenders.

In POJK, it has been regulated that funders can come from within the country or from abroad provided that they are registered as Indonesian citizens, foreign nationals, Indonesian legal entities, foreign legal entities, Indonesian business entities, foreign business entities, and international institutions. The maximum limit of funding to recipients of funds is limited to IDR 2 billion. Likewise, with funding, the maximum funding limit from funders is at most 25% of the final funding position.

With the ability of P2P Lending companies to distribute loan funds, it can provide benefits in encouraging the acceleration of financial inclusion in Indonesia. Financial inclusion means certainty for the public in having access to financial services either making savings, loans, or investments.

The distribution of loan funds is given for consumptive and productive purposes with a distribution ratio of 55%: 45%. The presence of P2P Lending supports capital for productive purposes such as micro businesses and MSMEs. P2P Lending helps MSMEs and other micro-businesses by simplifying transaction time and expanding reach with the use of technology.

Based on OJK data that as of January 2020 there are 164 P2P Lending companies with a registered number of 139 companies and 25 companies that have been licensed.

Figure 5: Number of P2P Lending Companies as of Jan'20

But the number of P2P Lending companies is shrinking starting from the 2nd quarter of 2020. In March 2022, there were only 102 P2P Lending companies that had been licensed, with a composition of 95 companies with conventional business types and 7 companies with sharia business types. In the sense that there are also 62 P2P Lending companies that return their business licenses. The phenomenon of a decrease in quantity in P2P Lending companies can be analyzed due to several possible factors as a cause of the decline in the number of P2P Lending companies, including an increase in the number of bad loans caused by loss of willingness to pay and ability to pay the recipient of loan funds, errors during the assessment of the feasibility of receiving loans due to inaccurate information obtained, There are constraints for companies that cannot operate optimally during the Covid-19 pandemic, the inability of P2P Lending to carry out obligations contained in the new POJK changes, for example changes in minimum working capital and others. and the possibility of other internal rules that cannot be fulfilled by P2P Lending organizers.

However, in the discussion of this journal, the author will only focus on the subject of research related to the availability of personal information and credit information from prospective loan recipients, the use of these data in assessing the feasibility of providing credit and its effect on the level of bad loans.

Overview of Bad Credit Rate of P2P Lending Industry in Indonesia

The distribution of large loan funds brings great risks. In the world of financial services, both banking and non-banking, the level of loan payment congestion can be seen by how much the NPL number of the company is, the smaller the NPL number, the better it will be for the company. NPLs are often defined as the level of bad loans or also a tool used as an indicator of the health of disbursed loans obtained by comparing the number of non-performing loans to total loans. In the P2P Lending industry, credit health measurement tools are also known as TKB90 and TWP90.

TKB90 is the number of payment success rates of loan recipients or can also be interpreted as the success rate of P2P Lending providers in facilitating payment of outstanding obligations within 90 days from the due date. The movement of TKB90 in the P2P Lending industry can be seen in the picture below:

Figure 6: TKB90 Percentage Trend in P2P Lending Industry for Jan'18 – Apr'23 Period

The worsening trend of TKB90 numbers occurred in 2020, this worsening was supported by the Covid-19 virus pandemic which caused the company's inability to carry out operational activities as usual. The implementation of collection cannot be carried out optimally and the rampant termination of employment due to the inability to operate from the company where the prospective loan recipient works. It often happens that prospective recipients of loan funds apply for loan funds, approved while still working but within the loan period no longer have a job. The same thing also happens to loan recipients who work in productive sectors where their businesses experience losses and inability to run their businesses due to distance restriction rules. However, despite fears of default, there is still an increase in loan disbursement.

But if analyzed again that in 2019 there has been a worsening of the payment rate. In January 2019 the P2P Lending industry scored TKB90 of 96.82%, this figure fluctuated until the end of 2019 to 96.35%. Entering 2020, the TKB90 number continued to fall until in August 2020 it touched 91.12%. The government's swift steps in launching the implementation of restucturization provide stimulation until there is an improvement in the number of TKB90 and has increased at the end of 2020 to 95.22%.

Angka perbaikan tetap bertahan sepanjang tahun 2021, dengan terjadinya penyusutan angka penyelenggaran P2P Lending. Januari 2021 angka TKB90 mencapai 98.22% dan angka ini bertahan secara fluktuatif sepanjang tahun sampai di Desember 2021 terjadi penurunan 0.5% sehingga angka TKB90 menjadi sebesar 97.71%. Angka TKB90 bertahan di 97% dan bergerak secara fluktuatif dengan mencapai angka 97.22% pada Desember 2022 dan 97.18% pada April 2023.

TWP90 is the value of the default rate of payment or the rate of default / default on loan obligations above 90 (twenty) days from the due date. TWP90 is a measure of the quality of P2P Lending funding. The measurement of TWP90 is the same as the measurement of NPL, which is the amount of outstanding that is included in default and is greater than 90 days compared to the total outstanding. TWP90 is the opposite of TKB90 so the calculation can also be done by subtracting 100% by TKB90. The TWP90 trend can be seen from the visualization image below:

Figure 6: TWP90 Percentage Trend in P2P Lending Industry for Jan'18 – Apr'23 Period

The worsening of the TKB90 figure is also in line with the worsening of the TWP90 figure throughout 2022. In accordance with statistical data released by OJK, the TWP90 figure in August 2020 has touched more than 8%, in the sense of an increase of 5.82% from the previous year. And this is the worst TWP90 number during the industry's operation in Indonesia. There was a record increase in the TWP90 figure in 2021, but it could not survive because it was seen that there was a decline in the TWP90 figure again and in September 2022 recorded IDR 1.5 trillion in bad loans. In April 2023, the aggregate credit risk level or TWP90 was 2.82%, but there was an increase of 0.01% from the previous month. Where outstanding P2P Lending financing in April 2023 grew by 30.63 percent on an annual basis (yoy) to IDR 50.53 trillion.

The TKB90 and TWP90 figures presented in this article are the total numbers of P2P Lending companies. Based on OJK's monthly statistics, it is known that in March 2023, out of 102 active P2P Lending companies, there are 24 organizing companies that have a TWP90 number above 5%. For this reason, OJK actively pays special attention by providing warnings or sanctions.

In the latest POJK changes, OJK added the definition of Funding Quality with the following levels:

1. Current: no delay in principal and/or economic benefits of funding

2. Of Special Concern: there is a delay in payment of principal and/or economic benefits of funding that has exceeded maturity by up to 30 calendar days.

3. Non-Current: there is a delay in payment of principal and/or economic benefits of funding that has exceeded 30 days to 60 calendar days.

4. Doubtful: there is a delay in payment of principal and/or economic benefits of funding that has exceeded 60 days to 90 calendar days.

5. Stalled: there is a delay in payment of the principal and/or economic benefits of funding that have exceeded those that have exceeded 90 calendar days.

P2P Lending companies by their nature accommodate higher risks when compared to service products from conventional banking institutions. In April 2023, the TKB90 figure of the P2P Lending industry is 97.18%. Although this figure is still relatively below the regulatory limit of 5%, it still has a worrying impact on loan lenders.

Lending lenders in P2P Lending are different from loans in banks, where funds sourced from various lenders are then distributed to loan recipients who apply for fast and relatively small loans through the organizer's platform. The risk of loss of funds if it occurs will be borne by these lenders and not charged to the operator.

As is known that the P2P Lending industry targets the profile of underserved and unbanked borrowers, or a person / community group who cannot meet the requirements to get banking access. With a loan recipient profile that does not have a recorded credit history, it has the potential for default because it will reduce the analysis of the ability to return loan funds.

The role of OJK and AFPI in assisting the provision of credit data for prospective borrowers

OJK which functions as a supervisory body for the financial services sector industry, including Fintech P2P Lending. In the implementation of supervision, OJK can tighten the mechanism of its supervision system and can provide reprimands or penalties for platforms that experience TWP90 or NPL problems. OJK has the authority to supervise both direct and indirect examinations. P2P Lending providers have the obligation to send monthly reports containing loan recipients, lenders, the amount of loan disbursement, and the quality of funding. OJK will conduct special monitoring for organizers who have a TWP90 value above 5% by accelerating the examination and asking for clarification on the causes of the increase in the TWP90 number.

As a response, the organizer must provide a strategy plan to reduce the number of TWP90 for a period of three months in which if the target of reducing the number of TWP90 fails, sanctions will be given gradually starting from a written warning, fines by being obliged to pay a certain amount, temporary restrictions on distribution activities until the TWP improvement figure is achieved, and finally the revocation of business activity licenses.

In accordance with POJK Number 10 /POJK.05/2022, it is stated that the obligation of P2P Lending organizers to submit funding transaction data at the Fintech Lending Data Center (Pusdafil) managed by OJK. Pusdafil was formed as a form of support for the availability of credit data where currently P2P Lending loan data has not been included in SLIK data. And basically, Pusdafil is the same as SLIK.

The scope of the Debtor Information System (SID) in SLIK has been extended to include bank financial institutions, finance institutions and also to non-bank financial institutions that have access to debtor data and the obligation to report debtor data. SLIK can be used as a medium to report credit transactions, fund provision facilities, collateral data, and other related data from various types of financial institutions, the public, Credit Information Management Institutions (LPIP) and other parties.

SLIK is a record of debtor history information of banks and other financial institutions. SLIK is known as the BI Debtor Information System (SID) which was previously BI checking. Operators can utilize SLIK and Pusdafil to assist in the implementation of credit risk management and are expected to minimize the number of bad loans.

The use of Pusdafil in the application of risk management in P2P Lending can be used as a preventive step to find out indications of fraud or fraud. Fraud is an attempt to apply using an identification number on an identity card that is proven not to be registered with the Directorate General of Dukcapil. Fraud is the highest problem with the discovery of fraud rates and the use of other people's identities in loan applications. Pusdafil data can be used as a source of data in making credit scoring to assess the eligibility of prospective recipients of loan funds.

Pusdafil can be used as a place to collect data on the borrower blacklist (blaclist) and record loan recipients who default (do not make loan payments for more than 90 days). The data will remain embedded in Pusdafil until there is repayment of unpaid arrears. Deletion can be done after reporting to AFPI. Pusdafil data can be used to check prospective loan recipients who make loans from more than one P2P Lending company which can be used by the organizer as a calculation and consideration in providing loan approval.

Bad loans are part of the P2P Lending activity cycle. So, by looking at the rapid growth of P2P Lending in Indonesia, it can be ascertained that there will be a growth in bad loan risk. There is a need to strengthen risk management in this industry, AFPI built a Fintech Data Center (FDC) that functions to accommodate credit data of all loan recipients and lenders in P2P Lending by presenting more complete processed data. By providing access to the use of FDC data, it is hoped that AFPI members, especially P2P Lending industry players, can avoid potential bad loans and fraud through early identification of FDC data, so that the company's condition continues to grow and be healthier.

OJK encourages P2P Lending to strengthen credit scoring as affirmed in POJK Number 10/POJK.05/2022 which requires credit risk screening before giving approval for lending. With incomplete data on SLIK and FDC, P2P Lending can use other external data sources such as data usage from Credit Bureaus. The presentation of data from the Credit Bureau has been processed and analyzed to produce a risk score so that the organizer does not need to re-analyze the risk score. However, the risk score presented cannot be presented absolutely considering that the data contained is also not the entire data of the prospective loan recipient's credit data.

Credit score data contains data on SLIK and financial institutions both banking and non-banking that cooperate with the credit bureau, including cooperatives or pawnshops. By using services outside SLIK and FDC, the data obtained is richer and feasibility analysis can provide more results in the hope of reducing the risk of default which has an impact on payment defaults to lenders. It's just that if there is no cooperation, the credit data needed is also not available. And to get this data, the organizer must incur additional costs which can also increase the amount of operational costs for the company or the loan recipient.

SLIK and its use in Credit Scoring and its Role in Minimizing the Risk of Bad Loans

In the credit application process, prospective recipients of funds are required to send data in the form of personal photos, copies of identity cards, proof of income / salary, copies of savings accounts, and other documents in accordance with the provisions of each company. This data will be validated with data in SLIK, Pusdafil, and external if used which results in the form of scoring.

In accordance with POJK Number 18 / POJK.03 / 2017, the SLIK information system is managed by OJK as a form of support for the function of implementing supervision and information services in the financial sector. Previously, the management function was under the supervision of Indonesian Lenders for the financial industry and Bapepam Financial Institutions for the capital market and non-lending financial institutions. The naming of this information system is BI checking, then changed to the Debtor Information System (SID) and then changed to SLIK.

SLIK informs historical data on the smooth running rate of previous credit payments from prospective loan recipients, which will be analyzed as creditworthiness parameters. From SLIK data, information related to loan amount, repayment patterns, loan quantity and loan smoothness is also obtained so that the repayment ability of prospective loan recipients is known. SLIK can be used as a data infrastructure in mitigating credit risk so that companies can estimate and avoid high levels of credit risk. But it is also known that P2P Lending companies are still unable to freely access the data, must go through the consent of prospective loan recipients

Credit weighting or credit scoring is a method used to make an assessment that is used as an indicator of consideration of the feasibility of loan approval by lenders before agreeing to distribute funds to loan recipients. This assessment is very important because of the need for tools to conduct credit analysis to maintain future defaults. Credit scoring contains individual data and also credit payment history at banking and non-banking financial institutions owned by prospective loan recipients.

In general, the criteria used as parameters for assessing the eligibility of receiving a loan by looking at personal data in the form of identity numbers that match the ID card, date of birth, age, address, home ownership, marital status, type of work, job title, length of work, place of work, income, proof of transaction by sending a copy of cash flow Savings accounts, credit history and credit payments owned, and other data according to company needs. For credit history. Credit history data is obtained from SLIK and FDC. Then the next process is carried out verification of loan users and related parties needed, where this feasibility analysis is carried out using the 5C principle, namely:

1. Character : Analysis carried out related to the personality of prospective loan recipients, including, such as looking at payment patterns in previous loan transactions

2. Capacity / Capacity: Analysis carried out related to the financial capabilities of prospective loan recipients, such as looking at the amount of income that can be obtained

3. Capital / Wealth: Analysis carried out on the wealth owned by prospective loan candidates, such as looking at the amount of balance or other investments owned.

4. Condition: Analysis carried out on economic conditions, such as loan time span, loan amount, or loan destination

5. Collateral: Analysis carried out on the guarantees that can be provided by prospective recipients of loan funds, because P2P Lending companies in providing credit do not ask for guarantees in the form of objects or goods, the form of collateral that needs to be conformed by verifying with the HRD where the prospective loan recipient works or by looking at the durability of the usha owned by the prospective recipient of the loan funds.

In addition to acting as an intermediary that brings together loan recipients and lenders, P2P Lending organizers inform lenders to increase profile analysis of prospective loan recipients and assess the feasibility of receiving loan funds through a grading or credit scoring model. Based on this information, lenders can make a decision to channel their funds or not. The form of credit scoring is quite varied because it is left to the company to determine the parameters and grading of the feasibility score. However, for prospective loan applicants who are already included in the Substandard, Doubtful, and Bad categories, rejection will automatically be carried out because it will be at risk.

P2P Lending also provides support in the implementation of billing ranging from email reminders to defaults. Therefore, in making credit scoring , it must be as accurate as possible to avoid additional operational costs. Integrated credit scoring can provide convenience in making loan acceptance eligibility values to prospective borrowers, making it easier for lenders to make decisions. The advantages that can be obtained by utilizing integrated credit scoring are:

1. Can analyze and evaluate all applicant data and provide results in real time. The process of implementing the analysis requires a level of foresight and accuracy, with the amount of data used in the analysis, the higher the level of error in calculation (human error). Using a system that has been integrated in detail and thoroughly can compare data and information from prospective loan recipients, so that the analysis produces a more accurate assessment.

2. Can simplify the survey process. Survey data reporting can be sent quickly at the same time when conducting a survey and connected directly to the data analysis processing system, so that the assessment results can be produced quickly and completely.

3. Can analyze the repayment ability of prospective loan recipients so as to produce a more specific assessment. A collection of credit or loan history data processed in big data can be developed as an indicator to assess the quality of a person's credit history. Risk analysis assessment and other approaches, for example psychological or personality approaches of prospective loan recipients.

Can reduce operational costs. The big data system used for data processing in P2P Lending requires a good and expensive infrastructure, in the use of integrated data, companies can share infrastructure so that family costs can be shared and cheaper.

CONCLUSION

When approving the distribution of loan funds to loan recipients, the lenders are directly not free from the risk of default or bad debt. So if the number of TWP90 or NPL increases, it is very necessary that the number of financing also increases, so that the figure can be maintained below 5%. Even by entering the names of default recipients in the FDC data information system, the NPL figure in April 2023 is still at 2.82% and there are still 24 companies that have NPL figures above 5%.

In handling NPL number control in the initial process, it is necessary to categorize the interest of borrowers and how they are able to pay, mitigate risk, support regulation, and most importantly the development of credit scoring that can provide an accurate assessment. In building credit scoring infrastructure, almost all data contained in SLIK can be used as analytical data in determining loan feasibility grading.

SLIK can be used as an indicator in analyzing credit applications. SLIK can also be a tool that can be used to encourage borrowers to be more disciplined in terms of loan payments. With the inclusion of a person's name in SLIK, it will be difficult for someone to get funding in the future. Financial institutions both banking and non-banking sectors will not provide loans to someone who has a bad track record in terms of credit payments,

However, the data contained in SLIK still does not contain all credit history completely, such as someone's loan data at P2P Lending, Cooperatives, Pawnshops, and other credit institutions. Although credit data for P2P Lending has been greatly helped by the presence of FDC, due to obtaining data sources from various sources, there is still a chance of errors in synchronizing data that can cause errors in conducting feasibility analysis.

Technology-based companies, which apply for credit without having to do face-to-face really need the power of data, but even though they use sophisticated big data, if the data content used is not all clean, it can cause errors in determining feasibility figures. SLIK is very helpful in implementing risk management and controlling the level of bad loans. But with the integration of all credit data from a person so that the data is not confirmed completely can make the appraiser inaccurate.

Meanwhile, looking at the number of loan disbursements from the P2P Lending industry, which is IDR 611.42 trillion in April 2023, it can be a hope to continue to encourage faster financial inclusion and help in the movement of the middle to lower economy, especially the productive sector of MSMEs and other micro enterprises. So that this industry really needs to improve data, especially credit data that is completely integrated in one place so that loan applicant data can be obtained completely and comprehensively so that credit scoring calculation analysis can produce more accurate results.

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