FIN367 Sustainable Finance & Bank Lending PDF

FIN367 6.2 Sustainable finance and bank lending 6.2.1 Introduction to sustainable finance 6.1.2 Impact of sustainable finance to bank lending practice Read More... ...

FIN367 6.2 Sustainable finance and bank lending 6.2.1 Introduction to sustainable finance 6.1.2 Impact of sustainable finance to bank lending practice Read More...  FIN367 RECALL – Standard, complementary, alternatives for credit decisions ✔ Behavioral biases in credit analysis & 5Cs JUDGEMENTAL decision New risk CAMPARI (Standard) Supplementary ✔ Sustainable lending criteria Alternative Credit Scoring Machine Learning Automated credit rating Artificial Intelligent  FIN367 6.2.1 Introduction to sustainable finance New sustainability risk (ESG) Bank Credit Sustainable Analysis & Finance Impacting bank Decision credit risk ✔ NEW finance knowledge and practice ✔ Banking & Finance graduates need to be exposed  FIN367 6.2.1 Introduction to sustainable finance Sustainable finance is defined as financial decisions that take into account the environmental, social, and governance (ESG) factors of an economic activity or project. ❑ Environmental factors include mitigation of the climate crisis or use of sustainable resources. ❑ Social factors include human and animal rights, as well as consumer protection and diverse hiring practices. ❑ Governance factors refer to the management, employee relations, and compensation practices of both public and private organizations. Source: https://extension.harvard.edu/blog/what-is-sustainable-finance-and-why-is-it-important/  FIN367 6.1.2 Impact of sustainable finance to bank lending practice Sustainable Finance Bank Credit Analysis New sustainability risk & Decision Impacting bank credit risk *Consider ESG risk in credit analysis & decision  FIN367 6.1.2 Impact of sustainable finance to bank lending practice Source: https://www.accenture.com/us-en/insights/banking/sustainable-lending  FIN367 6.1.2 Impact of sustainable finance to bank lending practice Types of sustainable finance – Debts for corporation and SMEs Green Loans Sustainability-Linked Loans Green loans are defined as any type of loan instrument Sustainability-Linked Loan (SLL) is also known as ESG-Linked used exclusively to finance or re-finance new or existing Loan or Positive Incentive Loan. eligible green projects. The proceeds from SLL are not tied to any specific Green loans are perceived as an alternative for companies requirements or projects. The SLL can be allocated for to finance their environmentally focused initiatives. general business purposes to improve the borrower’s sustainability profile, such as aligning the loan terms to the The classification of green loans follow the Green Loan borrower’s performance against the relevant sustainability Principles (GLP) performance targets (i.e. reduction of greenhouse gas emissions). The classification of SLL follow the Sustainability-Linked Loan Principles (SLP) https://assets.kpmg.com/content/dam/kpmg/my/pdf/sustainable-financing-whitepaper.pdf  FIN367 6.1.2 Impact of sustainable finance to bank lending practice Green Loan Principles – Core Components The GLP set out a framework, enabling all market participants to clearly understand the characteristics of a green loan, based around the following four core components: 1. Use of Proceeds 2. Process for Project Evaluation and Selection 3. Management of Proceeds 4. Reporting The GLP also seek to emphasise the required transparency, accuracy and integrity of the information that will be disclosed and reported by borrowers to stakeholders through these core components. Source: https://www.lma.eu.com/sustainable-lending/resources  FIN367 6.1.2 Impact of sustainable finance to bank lending practice Sustainability-Linked Loan Principles – Core Components The SLLP set out a framework, enabling all market participants to clearly understand the characteristics of a SLL, based around the following five core components: 1. Selection of KPIs 2. Calibration of SPTs (Sustainability performance targets) 3. Loan Characteristics 4. Reporting 5. Verification A SLL borrower should clearly communicate to its lenders2 its rationale for the selection of its KPI(s) (i.e. relevance, materiality, whether it is core to the borrower’s overall business) and the motivation for the SPT(s) (i.e. ambition level, benchmarking approach and how the borrower intends to reach such SPTs). Borrowers are encouraged to position this information within the context of their overarching Source: https://www.lma.eu.com/sustainable-le objectives, sustainability strategy, policy, sustainability commitments nding/resources and/or processes relating to sustainability.  FIN367 6.1.2 Impact of sustainable finance to bank lending practice Banks will play a key role in financing the sustainable transformation of their commercial client ecosystems, which in turn will enable them to gain a range of tangible benefits, including: ✔ higher loan volumes (ESG lending could form up to 30% of their total loan portfolio); ✔ new value generation; ✔ risk mitigation (up to 4.5% higher total returns for shareholders on high-performing ESG initiatives); ✔ lower cost of funding; ✔ new revenue streams linked to loans (e.g. non-financial ESG services); and ✔ brand differentiation. Source: https://www.accenture.com/us-en/insights/banking/sustainable-lending  FIN367 6.1.2 Impact of sustainable finance to bank lending practice New Practice…New skills required…. ✔ Transform their lending value chain: prioritizing the implementation of an operating model that has zero-net impact and enables banks to make ESG-coherent lending decisions. ✔ Reskill their lending practice: training and upskilling programs around sustainable lending value propositions and ESG criteria. ✔ Set up ESG data platforms: building data platforms, creating ESG scoring models and tapping into third-party data to link ESG considerations to credit policies and, ultimately, product offerings Source: https://www.accenture.com/us-en/insights/banking/sustainable-lending  FIN367 6.1.2 Impact of sustainable finance to bank lending practice Source: https://www.accenture.com/us-en/insights/banking/sustainable-lending  FIN367 6.1.2 Impact of sustainable finance to bank lending practice Source: https://www.accenture.com/us-en/insights/banking/sustainable-lending  FIN367 Example – Industry Practice: CIMB (Malaysia) FIN367  FIN367 https://www.unepfi.org/banking/bankingprinciples/  FIN367 Green, Social, Sustainable Impact Products & Services (GSSIPS) Framework The GSSIPS Framework provides a guide and an internal taxonomy for the Group to deliver impactful sustainable finance. In practice, the Framework is guided by the Classification Guiding Principles covering segments across the bank from Consumer Banking and Small & Medium Enterprises, to Commercial and Wholesale Banking.  FIN367 We will take appropriate measures to manage the Sustainability Risk of our business activities to the extent possible and will not knowingly engage in business activities or with business relations that are on our Exclusion List of activities.  FIN367 6.3 Behavioral finance and bank lending 6.3.1 Introduction to behavioral finance 6.3.2 Impact of behavioral finance to bank lending practice Read More...  FIN367 6.3.1 Introduction to behavioral finance Behavioural Biases Bank Credit Behavioural Analysis & Finance Impacting bank Decision credit officer thinking & decision ✔ NEW finance knowledge and practice ✔ Banking & Finance graduates need to be exposed  FIN367 6.3.1 Introduction to behavioural finance we mistakenly think we know more than we actually do, we tend ❑ Behavioral finance is the to miss information that we need to make an informed decision study of the influence of psychology on the behavior of individual in Heuristic simplification refers to information-processing errors. financial practices. It also includes the subsequent making decisions based on our current emotional state. Our effects on the institutions current mood may take our decision-making off track from and markets. rational thinking. ❑ It focuses on the fact that individuals are not always we mean by the social bucket is how our rational, have limits to decision-making is influenced by others. their self-control, and are influenced by their own biases. Source: https://corporatefinanceinstitute.com/resources/career-map/sell-side/capital-markets/behavio ral-finance/  FIN367 6.3.2 Impact of behavioural finance to bank lending Behavioural Finance Bank Credit Analysis Risk of irrational decision by credit officer & Decision Impacting bank credit *Reduce/Avoid risk behavioural biases  FIN367 6.3.2 Categorization of Behavioural Biases Definitions: ✔ “information-processing, or memory errors” ✔ “distortions of the human mind” Cognitive ✔ “attributable to the way the brain perceives, forms Errors memories, and makes judgements” Definitions: Social biases, to be precise, occur when we unknowingly or deliberately make a judgment Social Behavioural Biases about certain individuals, groups, races, opinion, and so on, due to preconceived Biases notions about the group Definitions: Emotional ✔ “biases that help avoid pain and produce pleasure” ✔ “arise spontaneously as a result of attitudes and feelings” Biases ✔ “stem from impulse or intuition” ✔ “result from reasoning influenced by feelings”  FIN367 6.3.2 Cognitif Biases There are two categories of cognitive biases: 1) belief perseverance biases and information-processing biases. Individuals demonstrate conservatism bias by maintaining their Belief Perseverance Biases Conservatism Bias previous beliefs and inadequately incorporating (or “under-reacting to”) new information, even when this new information is significant. Confirmation bias occurs when individuals place too much emphasis Confirmation Bias on information that confirms their existing beliefs and underweight (or ignore) information that challenges these beliefs. Representativeness Representativeness bias occurs when an individual classifies new information based on past experiences and categories. Bias Source: https://ift.world/wp-content/uploads/2018/02/R06-The-Behavioral-Biases-of-Individuals-IFT-Notes.pdf  FIN367 6.3.2 Cognitif Biases There are two categories of cognitive biases: 1) belief perseverance biases and information-processing biases. Belief Perseverance Biases Illusion of Illusion of control bias occurs when individuals incorrectly believe that they can control or influence outcomes, or for individuals to think that he have more control over the situation than he actually do. Hence, they have a false Control Bias impression that future event are due to their skill rather than due to luck. Hindsight Hindsight bias is a mistaken belief that outcomes were (and are) predictable. This can lead to excessive risk-taking due to an irrationally high assessment Bias of one’s ability to correctly predict outcomes. Source: https://ift.world/wp-content/uploads/2018/02/R06-The-Behavioral-Biases-of-Individuals-IFT-Notes.pdf  FIN367 6.3.2 Cognitif Biases There are two categories of cognitive biases: 1) belief perseverance biases and information-processing biases. Information Processing Biases Anchoring and Anchoring and adjustment bias occurs when individual “anchor” themselves to the first information they receive and incorporate new information by adjusting this Adjustment Bias reference point. Framing bias occurs when an individual answers a question with the same basic facts Framing Bias differently depending on how it is asked. Individual who are affected by framing bias may misidentify their risk tolerance based on how information is presented. People tend to base decisions on information that is readily available or easily Availability Bias recallable. Source: https://ift.world/wp-content/uploads/2018/02/R06-The-Behavioral-Biases-of-Individuals-IFT-Notes.pdf  FIN367 6.3.2 Emotional Biases bias that describes the pain of losing is psychologically twice as powerful as the Loss-Aversion Bias pleasure of gaining. “Loss aversion bias” causes individual to be conservative in their decision making. Overconfidence Investors demonstrate overconfidence bias by holding an irrational belief in the superiority of their knowledge and abilities. It is also known as the illusion of Bias knowledge bias. Self-Control Bias In a general sense, self-control bias is a lack of self-discipline. Source: https://ift.world/wp-content/uploads/2018/02/R06-The-Behavioral-Biases-of-Individuals-IFT-Notes.pdf  FIN367 6.3.2 Emotional Biases Status quo bias is defined as the preference for maintaining Status Quo Bias one's current situation and opposing actions that may change the state of affairs. Regret-Aversion People exhibiting regret aversion avoid taking decisive actions because they fear that, in hindsight, whatever course they Bias select will prove less than optimal. Source: https://ift.world/wp-content/uploads/2018/02/R06-The-Behavioral-Biases-of-Individuals-IFT-Notes.pdf  FIN367 6.3.2 Development of behavioral finance in bank lending Behavioral Biases Correction? ✔ It is better to step back and recognize that cognitive errors can typically be corrected through education and recognizing the flaws in one’s decision-making process. ✔ Measures such as actively seeking out information that challenges one’s existing beliefs, keeping detailed records, and updating probabilities in an unbiased manner.  FIN367 6.4 Technologies and bank lending 6.4.1 Introduction to technologies in bank lending 6.4.2 Impact of technologies in bank lending practice Read More...  FIN367 6.4.1 Introduction to technologies in bank lending Financial Technologies used for automated credit rating Bank Credit Technologie Analysis & s (FinTech) Impacting bank Decision credit process & decisions ✔ NEW finance knowledge and practice ✔ Banking & Finance graduates need to be exposed  FIN367 6.4.2 Credit Scoring System Traditional FinTech Machine Artificial Credit Learning Intelligent Scoring New Credit (ML) (AI) System Scoring Systems  FIN367 6.4.2 Credit Scoring System – Traditional method Hard Information Character Credit rating matrices: Borrower data Capacity 1. Probability of default (PD), and 2. The expected loss given Condition Collateral default (LGD). Credit decision – SIMPLE Data – 5Cs Approve or Reject?  FIN367 6.4.2 Credit Scoring System – Traditional method Example – Industry application: CTOS Malaysia “For CTOS’ score, the five areas are: ✔ payment history (45%), ✔ amount owed (30%), ✔ credit history length (15%), ✔ credit mix (10%) and ✔ new credit (10%), Source: https://ctoscredit.com.my/news-media/what-goes-into-your-credit-score-calculation/  FIN367 6.4.2 Credit Scoring System – Machine Learning (ML) method Hard Information Character Credit rating Borrower data matrices: Economics data Industry data Capacity 1. Probability of default (PD), and 2. The expected loss given Condition Collateral default (LGD). Soft Information Credit decision – BIG Data – 5Cs Approve or Reject?  FIN367 Credit Scoring System – Machine Learning (ML) method https://www.linkedin.com/pulse/how-use-machine-learning-credit-scoring-per-dahlqvist  FIN367 Credit Scoring System – Machine Learning (ML) method Source: https://www.researchgate.net/publication/340684782  FIN367 AI in Credit Decision-Making 6.4.2 Credit Scoring System – Artificial Intelligent (AI) method Hard Information Character Credit rating Digital/Social data Economics data Borrower data matrices: Industry data Capacity 1. Probability of default (PD), and 2. The expected loss given Condition Collateral default (LGD). Soft Information Credit decision – BIG-COMPLEX Data – 5Cs Approve or Reject?  FIN367 ▪ What is AI? ▪ AI and Finance connections? AI map Source: https://www.ai-gakkai.or.jp/pdf/aimap/AIMap_EN_20210901.pdf  FIN367 ▪ How to distinguish between AI, ML, and DL Source: https://www.altexsoft.com/blog/data-science-artificial-intelligence-machine-learning-deep-learning-data-mining/  FIN367 ▪ How to distinguish between AI, ML, and DL Source: https://www.researchgate.net/publication/340684782  FIN367 ▪ AI applications in Banking and Finance Source: https://www.researchgate.net/publication/340684782  FIN367 6.4.2 Credit Scoring System – Artificial Intelligent (AI) method ❑ AI was then described as the development of computer programs capable of taking on tasks performed unsatisfactorily by human beings, because of the demand they place on high-level mental processes (e.g., perceptual learning, memory, critical reasoning). ❑ This is where AI has a role to play in helping manipulate big data, leading to improved decision-making. Source: Sadok, H., Sakka , F., & El Maknouzi , M. E. H. (2022) Artificial intelligence and bank credit analysis: A review. Cogent Economics & Finance, 10:1, 2023262, DOI: https://www.businesslitigationblog.com/2021/01/ai-in-credit-decision-making-is-promising-b 10.1080/23322039.2021.2023262 ut-beware-of-hidden-biases-fed-warns/  FIN367 6.4.2 Development of FinTech in Bank Lending The impact of AI on credit analysis procedures ❑ A significant area in which AI makes it possible to improve banking operations is the management of risk, by strengthening credit scoring, portfolio management, fraud detection, the optimisation of debt collection strategies, the rapid detection and interpretation of signals from weak borrowers, and the construction of economic models, among others. ❑ AI techniques make it possible to mobilise new sources of information, known as big data, which could not have been integrated into traditional credit risk management models, due to their size. Source: Sadok, H., Sakka , F., & El Maknouzi , M. E. H. (2022) Artificial intelligence and bank credit analysis: A review. Cogent Economics & Finance, 10:1, 2023262, DOI: 10.1080/23322039.2021.2023262  FIN367 6.4.2 Development of FinTech in Bank Lending The role of big data in AI-based credit analysis AI work well with big data inputs that might includes: ✔ Borrower data - variables generally include the nature of the loan, the characteristics of the borrower (age, income, marital status), and his or her banking history. ✔ The creditworthiness data. This score factors in such variables as payment history, outstanding debts, length of credit history, and the recent opening of new accounts, among others. ✔ Economics, Industry, Regulations (internal and external) data. These factors will affect the firm business performance and accordingly the financial performance. ✔ Digital customer information - big data is drawn from a much more varied range of sources, either through the digitisation of customer relations (digital fingerprint data) or by leveraging new forms of customer information, such as social network activity. Source: Sadok, H., Sakka , F., & El Maknouzi , M. E. H. (2022) Artificial intelligence and bank credit analysis: A review. Cogent Economics & Finance, 10:1, 2023262, DOI: 10.1080/23322039.2021.2023262  FIN367 6.4.2 Development of FinTech in Bank Lending Example – Industry Application: GFI Fintech Sdn Bhd (Malaysia) GFI Fintech Sdn Bhd is a data tech company that specialises in profiling technology for the finance industry. Our flagship product is called GFI which is a psychometric credit risk assessment system. GFI Fintech Sdn Bhd 201801034754 (1296781-T) Level 3, Wisma Suria, Jalan Teknokrat 6, Cyber 5, 63000 Cyberjaya, Selangor, Malaysia Source: https://gfi-fintech.com/who-we-are/  FIN367 6.5 Non-Bank Lending Organizations 6.5.1 Leasing Companies 6.5.2 Factoring Companies 6.5.3 Venture Capital Companies 6.5.4 Peer to Peer Lending Read More...  FIN367 6.5.1 Leasing Companies Leasing companies constitute a relatively small but growing sub-sector of the Malaysian financial sector. Under Section 3, FSA 2013, leasing is a prescribed business in which BNM has the power to regulate and supervise. The Section defines a leasing business as: “the business of letting or sub-letting movable property on hire for the purpose of the use of such property by the hirer or any other person in any business, trade, profession or occupation or in any commercial, industrial, agricultural or other economic enterprise whatsoever. This includes transactions where the lessor is the owner of the property, regardless of whether the letting is with or without an option to purchase the property, but excludes the business of hire-purchase which is subject to the Hire-Purchase Act 1967 as at 30 July 2012. Such other business as prescribed under Section 3, FSA 2013.” Section 211 of the FSA 2013 further states that the leasing business is a financial inter-mediation activity, and Section 212 provides that the Finance Minister may prescribe any person not under the supervision or oversight of BNM and engaging in financial intermediation activities as a prescribed financial institution. Leasing companies’ funds are made up of shareholder funds, borrowings from financial institutions and inter-company borrowings.  FIN367 6.5.1 Leasing Companies List of Leasing Companies (in Malaysia): ORIX Leasing Malaysia Berhad https://orix.com.my/  FIN367 https://orix.com.my/  FIN367 SMFL Leasing (Malaysia) Sdn.Bhd. https://www.smfl-global.com/malaysia/en/index.html  FIN367 6.5.2 Factoring Companies Factoring companies are specialised lenders providing financing for sales invoices, particularly to small and medium-sized businesses facing difficulties in accessing normal bank financing. Factoring lines are pre-agreed between the borrower and the factoring company and selected sales invoices are accepted. These invoices are then purchased by the factoring company at a margin of between 70% and 90% of the amount depending on the factoring agreement. The financing amount is repaid when the sales invoices are due and collected from invoiced customers. Factoring companies may also extend their services to include managing the collections of invoices. Factoring houses can be subsidiaries of banks or be independently owned. Under Section 3 of the FSA 2013, a factoring business is a prescribed business which BNM has the power to regulate and supervise. The Section defines “factoring business” as: the business of acquiring debts due to any person; and such other business as prescribed under Section 3, FSA 2013. Section 211, FSA 2013 states that the factoring business is a financial intermediation activity, and Section 212 provides that the Finance Minister may prescribe any person not under the supervision or oversight of BNM and engaging in financial intermediation activities as a prescribed financial institution.  FIN367 6.5.2 Factoring Companies Example (in Malaysia): https://orix.com.my/  FIN367 6.5.2 Factoring Companies Example (in Malaysia): Source: https://www.goodfirms.co/business-services/factoring/malaysia  FIN367 6.5.3 Venture Capital Companies A venture capital company is an investor in a new project or business that takes on full business risk. Venture capital companies typically help fund new companies that might have difficulty securing funding from banking sources due to unproven business models or new technology. The venture capitalist is prepared to undertake this new venture risk in return for a share of prospective profits and will provide funds for a new start-up or for the expansion of the business. The terms of the loan typically involve a stake in the company’s equity and profits. The agreement can be negotiated to include an eventual buyout of the venture capitalist by the original promoter, or by market investors through a stock market public offering.  FIN367 6.5.3 Venture Capital Companies Example (in Malaysia): Malaysia Debt Ventures Berhad (MDV) https://www.mdv.com.my/v3/vent ure-debt-2/  FIN367 Example (in Malaysia): Malaysia Debt Ventures Berhad (MDV) https://www.mdv.com.my/v3/venture-debt-2/  FIN367 Malaysia Debt Ventures Berhad (MDV) https://www.mdv.com.my/v3/venture-debt-2/  FIN367 Other Venture Capital Companies in Malaysia Source: https://www.icmr.my/wp-content/uploads/2019/08/Malaysia-VC-Development_Venture-Debt.pdf  FIN367 6.5.4 Peer to Peer (P2P) Lending Peer-to-peer (P2P) lending works by connecting borrowers who need money with lenders who want to make a return on their investments. Borrowers submit loan requests to the peer-to-peer lender and investors then compete to finance the loans in exchange for an interest rate. From start to finish, P2P sites manage the entire process, including rating creditworthiness, loan servicing, payments, and collections. Source: https://p2pmarketdata.com/articles/p2p-lending-explained/  FIN367 6.5.4 Peer to Peer (P2P) Lending Peer-to-peer (P2P) financing is another innovative form of financing that allows entrepreneurs and small businesses to unlock capital in small amounts from a pool of individual lenders. P2P enables businesses to borrow and investors to lend capital through online platforms registered with the Securities Commission Malaysia (SC). The SC launched the P2P Framework in May 2016. As at end-December 2022, about RM3.87 billion of P2P financing had been raised through 54,791 successful campaigns and 6,913 issuers. On a related note, almost half (49%) of the investors were aged below 35 years, with 89% of the invested funds stemming from retail investors. The P2P segment notably flourished in 2022, boasting 3,732 issuers that raised RM1.58 billion via 24,455 campaigns (2021: 1,998 issuers, RM1.14 billion raised and 14,301 campaigns). That said, campaign sizes stayed small, with 70% of the issuers raising RM50,000 or less – almost all (99%) of which was targeted as working capital. Source: https://www.capitalmarketsmalaysia.com/digital-peer-to-peer-p2p-financing/  FIN367 6.5.4 Peer to Peer (P2P) Lending Eligibility Criteria for P2P Platform Providers Any person or entity that seeks to operate a P2P financing platform must apply to be registered as a P2P operator under the SC’s Guidelines on Recognised Markets (or RMO Guidelines). All P2P operators must be locally incorporated and have a minimum paid-up capital of RM5 million. A P2P operator must also adhere to the following: Ensure there is an efficient and transparent risk-scoring system in place relating to the investment note or Islamic investment note. Conduct a risk assessment on prospective issuers intending to use its platform. Ensure the issuer’s disclosure document lodged with the P2P operator is verified for accuracy and made accessible to investors through the P2P platform. Inform investors of any material adverse change to the issuer’s proposal. Have in place processes or policies to manage any default by issuers, including using its best endeavours to recover outstanding amounts owed to investors. Ensure that its rules set out a rate of financing that is not more than 18% per annum. A P2P operator must consult the SC if it wishes to impose a rate of financing that is more this stated rate. Source: https://www.capitalmarketsmalaysia.com/digital-peer-to-peer-p2p-financing/  FIN367 6.5.4 Peer to Peer (P2P) Lending Eligibility Criteria for P2P Investors Investment in P2P is open to all investors, subject to the following limits: Retail Investors Angel Investors Sophisticated A maximum of No restriction on Investors RM50,000 per investment No restriction on platform at any amount. investment time. amount. Source: https://www.capitalmarketsmalaysia.com/digital-peer-to-peer-p2p-financing/  FIN367 6.5.4 Peer to Peer (P2P) Lending Who Can Raise Funds Through P2P Notably, only locally registered companies can raise funds through P2P platforms. The crowdfunding exercise is only considered successful if it reaches at least 80% of its target, and can only accept up to the targeted amount. P2P is slightly different from equity crowdfunding (ECF) in that an issuer can be hosted concurrently for different purposes on multiple P2P platforms. That said, the issuer is must disclose to the P2P operator its intention of seek concurrent funding from other P2P operators. Only the following issuers can be hosted on a P2P platform: Locally incorporated or registered entities: Sole proprietorship Partnership Limited-liability partnership Private company Unlisted public company Any other type of entity as may be permitted by the SC. Source: https://www.capitalmarketsmalaysia.com/digital-peer-to-peer-p2p-financing/  Registered and Recognised P2P Operators FIN367 Source: https://www.capitalmarketsmalaysia.com/digital-peer-to-peer-p2p-financing/