LO 78.2: Analyze the functioning of FinTech credit markets and activities, and

LO 78.2: Analyze the functioning of FinTech credit markets and activities, and assess the potential microfinancial benefits and risks of these activities.
Traditional P2P Lending Model
P2P lending platforms establish an online presence whereby borrowers and lenders may interact directly with each other as shown in Figure 1.
Figure 1: Traditional P2P Lending Model
Source: Graph 2: Stylized Traditional P2P Lending Model. Reprinted from FinTech Credit: Market Structure, Business Models and Financial Stability Implications, BISCommittee on Global Financial Systems, May 2017, 11. The potential borrower makes an initial loan application on the online platform by providing the required information, which is reviewed and approved by the platform. From there, only the approved applications will go into the pool from which potential lenders may select the loan(s) they want. At that point, the loan contracts are established directly between the borrower and lender. From the borrower and/or lender, the platform operator takes fees such as for loan setup or loan repayments.
Loan selection occurs using criteria such as loan purpose, borrower industry, and borrower income. Loans will be established if they fall within the acceptable time period stated by the borrower.
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The platforms provide an overall credit rating or score that could be determined internally or obtained from a third-party. For an internal assessment, the process is proprietary but likely considers newer and less common kinds of data (i.e., online spending habits) together with more sophisticated methods of analysis.
Lenders are usually advised by platforms to lend to more than one borrower to adequately diversify their investment. In certain instances, there may be an automatic selection process for the loans based on predetermined criteria set by the lender, such as loan amount or credit score. If enough loans are selected by one lender, the result is a pooling of loans similar to a securitization scheme.
There are three basic methods in establishing loan interest ratesin general, borrowers establish the maximum rate and lenders establish the minimum rate. The platform operator uses the information together with the loan amounts to match borrowers and lenders.
Potential lenders make interest rate bids on loans within a range (i.e., minimum stated Potential lenders make interest rate bids on loans within a range (i.e., minimum stated by platform operator based on risk assessment and maximum stated by borrower).
The platforms provide the rate consistent with the credit risk assessment for the loan
(that may be flexible depending on supply and demand).
Borrowers are given a representative rate for an online loan based on a risk assessment
and can seek out appropriate lending alternatives based on the rate.
The majority of platforms allow for partial or full prepayment of loans on a penalty- free basis. On the assumption that payments are made as scheduled, there is no further monitoring of the loan and the borrowers could use the funds for any purpose.
In contrast, should a borrower be potentially delinquent on a loan, they should contact the platform as soon as possible to avoid the platforms contacting debt collection agencies to begin the loan recovery process. At the point of delinquency, the platform may start charging additional fees to the lender. Some platforms have methods to deal with credit losses, which could be in the form of insurance or guarantee/provision funds that provide partial or full coverage of the loan portfolio (i.e., there could be exclusions for higher credit risks). As for the percentage of loss covered, there is a wide range from 2.5% to 70% of the principal amount. An alternative method has the objective to pay out, at a minimum, the expected lifetime default rate for covered loans.
Should lenders wish to exit their loan investments, some jurisdictions allow those creditors to do so by paying fees to the platform and on the condition that other lenders will take over those loans. There also may be no exit guarantee if there are an excessive number of exit requests on the platform at the same time.
Notary Model
The notary model is used frequently in Germany, Korea, and the United States. There is a partnership agreement between a fronting bank and the lending platform because the fronting bank actually originates the loans. The loans are then sold or assigned by the fronting bank directly to interested lenders or through a platform subsidiary (securitization) to institutional investors. The following diagram presents the basic model; some differences exist in its application in some jurisdictions.
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Figure 2: Notary Model
Source: Graph 3: Stylized Notary Model. Reprinted from FinTech Credit: Market Structure, Business Models and Financial Stability Implications, BISCommittee on Global Financial Systems, May 2017, 13. This is the approach used in Germany because only authorized institutions (i.e., not lending platforms) are permitted to provide loans.
In Korea, there is no lending by the lending platform and it is all done by a separate subsidiary that sets up the loans using the funds provided by lenders to the lending platform. Alternatively, a fronting bank is used to set up the loans; the platform transfers the funds to the bank in the form of collateral.
In the United States, regulatory restrictions sometimes cause FinTech lenders to work with a lending institution. The lending institution issues the loans to borrowers from the lending platform. The lending institution may either retain the loans or hold them for a very short period of time and then sell them to the platform lender. The platform lender may then either hold the loans or sell them directly to investors.
Guaranteed Return Model
With the guaranteed return model, the lending platform guarantees the principal and/or interest on the loans.
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i \ Optional online-to-offline structure /
(cid:31) ———–4-
funds invested
Source: Graph 4: Stylized Guaranteed Return M odel. Reprinted from FinTech Credit: Market Structure, Business Models and Financial Stability Implications, BISCommittee on Global Financial Systems, May 2017, 14. Historically, this model has been used notably in China and Sweden. In China, some platforms guaranteed the principal amounts on the condition that the lenders held an extremely diversified loan portfolio. Another platform simply provided a 12% return on investment. However, recent regulatory changes now prohibit online lenders from offering such guarantees. In Sweden, a 12% return was guaranteed by one online platform to investors together with very few access restrictions; ultimately, the platform was forced by regulators to cease operations due to findings of misconduct.
Balance Sheet Model
The balance sheet model involves the lending platform operating much the same way as a non-bank lender; it requires capital (i.e., debt, equity, securitization) to originate loans but it also retains the loan receivables as assets.
Figure 4: Balance Sheet Model
i Optional securitization structure
Retail investors
Platform s balance sheet
Lending platform
N/jfq %/ S .
V S N % S 0
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Figure 3: Guaranteed Return Model
keeps loans
i funds i invested 1 assignment b
i of claims ^ J of claims
Lending platform
> -v H y

r L
^ ^ credit risk analysis N
^ provision of funds
Source: Graph 5: Stylized Balance Sheet FinTech Lending Model. Reprinted from FinTech Credit: Market Structure, Business Models and Financial Stability Implications, BISCommittee on Global Financial Systems, May 2017, 15. This model is used extensively in Australia, Canada, and the United States with the United States being the largest in absolute dollars. In China and the United States, some platforms operate as a combination of the traditional P2P and the balance sheet models, or they combine P2P lending platforms with businesses such as wealth management, trading, and insurance.
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Invoice Trading Model
Firms often make credit sales and record corresponding receivables (or invoices) on their balance sheets. However, for quicker conversion of those receivables to cash, they will often sell (factor) them at a discount. If the receivables are sold on a non-recourse basis, the discount is larger and the credit risk of the receivables is transferred to the purchaser. On a recourse basis, the discount is smaller and the credit risk of the receivables remains with the seller. Given that non-recourse factoring is riskier, there may be a minimum amount of business activity required. Therefore, recourse factoring seems to be the most common form for start-ups or small businesses.
Invoice trading platforms providing recourse factoring have become popular because they include perks such as automatic invoice processing, less delay between invoice processing and cash payment, and a lower level of business activity required.
P2P lending platforms originally began with individuals lending directly to borrowers. There has been an evolution in P2P lending such that institutional investors are funding a substantial portion of personal and business loans, especially in the United States and Canada. In contrast, much of the funding in Europe, the United Kingdom, and Japan is private.
Within institutional funding, securitizations have occurred almost exclusively in the United States with only a small number in the United Kingdom and Australia.
The majority of platform creditor funds are sourced domestically, especially in the Americas and Europe. Cross-border funding at about one-third of the total amount is highest in the Asia-Pacific region (outside China). Individual (non-professional) investors are usually limited to investing amounts ranging from $2,000 to $18,000, depending on the jurisdiction (amounts are generally higher in China and lower in Europe). There are no investment limits for professional or institutional investors.
The two main types of credit are individual loans and business loans, with debt refinancing and consolidation being the most common reasons for individual loans. The typical borrower sought by platforms is a low credit risk. Average individual loan sizes vary from $5,000 to $25,000. In the United States, the average is closer to $25,000 and in China, the average is in excess of $50,000.
Platforms provide business loans to small and medium-sized businesses on both a secured and unsecured basis. It is estimated that about two thirds of business lending is secured, most commonly with real estate. Business lending in the .Americas and Europe is primarily domestic while in the Asia-Pacific region, more of it is international.
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Microfinancial Benefits
Lower Financing Costs for Borrowers
With FinTechs lower costs through the extensive use of computerization and automation (i.e., loan approval, loan pricing) and the absence of physical bricks and mortar operations, the cost savings should theoretically flow through to borrowers in the form of lower interest rates.
Some studies have shown that overall, smaller loans to individuals are priced lower than traditional banks. Other studies have shown very close rates once adjustments for risk are considered. And other studies have shown marginally increased rates for mortgages (secured with real estate) once adjustments for differences in property location and loan attributes are considered.
Loans in certain lending platforms have a very wide dispersion of rates (i.e., 6% to 36% per annum) compared to regular banks, which suggests that lending platforms may be dealing with a more diverse group of borrowers and/or the existence of greater precision in loan pricing using specific risk factors.
Higher Returns for Lenders
Following the same logic for passing on cost savings to borrowers, the effect on lenders would be in the form of higher returns. However, quantifying the benefit is problematic because of the difficulty in finding comparable investments with the same risk features (i.e., duration and liquidity) as FinTech loans.
One study concludes on an average return of 7%, which is 3% higher than the return on a somewhat comparable index of asset-backed securities. A different survey suggests returns between 3% and 10%. Data from one platform shows rates of return ranging from 8% (lowest default probability) to 24% (highest default probability).
User Convenience
With substantial full use of a computerized environment for providing loan information and assessing loan risk, the search costs for borrowers and lenders is significantly reduced. With a very streamlined and easy process, lenders may be able to offer loans to borrowers at an amazing speed (i.e., minutes or hours). That is in direct contrast to traditional banks that typically operate in a less computerized environment with manual processes that ultimately delay the loan approval process.
FinTech could assist existing borrowers by offering additional types of financing when needed. Invoice trading platforms, for example, could be used by small borrowers to access cash quickly instead of paying overdraft interest at a high rate. In that regard, some jurisdictions actively support such lending to promote economic growth. The support is shown through tax benefits provided to FinTech investors.
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Some groups of (potential) borrowers, such as self-employed individuals and small business owners, have historically been unable to qualify for loans from traditional banks. With the introduction for FinTech lending, such forgotten borrowers can finally obtain the small- dollar loans that they require to grow their businesses.
Within emerging market economies, surveys indicate that many individuals have never borrowed from a traditional bank. With the user-friendliness of many lending platforms, FinTech is likely to increase accessibility to credit for a substantial number of users who otherwise would not have access.
Microfinancial Risks
Leverage and Liquidity Risk
The majority of lending platforms function as agents to bring investors together with borrowers. Therefore, such platforms have little or no leverage risk. A few platforms take on leverage risk in that they use internal resources to fund loans or provide return guarantees.
Most lending platforms also take on little or no liquidity risk (investment and loan durations are usually the same and investors must maintain their loan investments until they mature). At the same time, some platforms are now providing investors with the ability to withdraw amounts early. One example of such a platform allows investors to invest in loans and withdraw amounts at any time and at no charge. Although it is explicitly stated that there is no absolute certainty that the withdrawals will be granted, there is the risk that investors may expect liquidity regardless.
Operational Risk
FinTech platforms face cyber risks given their extensive use of electronic data. Such risks are likely to increase with the level of platform sophistication and will decrease with the strength of their procedures in managing confidential client data and strength of their cybersecurity procedures. With regard to data storage, for example, it requires the platforms to outsource that task to an external provider so there is operational risk should there ever be service disruption. Fraud risk exists because the nature of FinTech lending makes money laundering and other forms of misconduct (i.e., Ponzi schemes) a distinct possibility.
Credit Risk Assessment Quality
FinTech platforms make use of big data analytics, which includes some more unusual but relevant data, to supposedly improve credit risk assessment over that of traditional banks. By taking a more focused analytical process and avoiding the pitfalls of outdated IT systems, the credit assessment may be enhanced. To date, it is not possible to conclude with certainty that FinTech platforms have superior credit risk assessment processes.
Three key arguments against higher quality credit risk assessment of FinTech platforms include: (1) platforms likely do not have detailed borrower information such as income, assets, and liabilities, (2) some platforms use solely hard data sources and do not consider soft credit risk factors, and (3) loan default data for unchartered borrower segments may be unreliable or unavailable.
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Business Model Incentives
The use of the agency model where lenders generate fees from creating new loans, but do not bear any credit risks, may promote the wrong incentives and ultimately lead to poorer quality risk assessments. For example, platforms that do not have to absorb any credit losses on defaulted loans would have the incentive to grant as many loans as possible to maximize fees earned. Or if a platform charges fees to borrowers based on risk, there would be the incentive to grant more higher-risk loans to attempt to maximize fees.
At the same time, if platforms earn fees based on servicing loans, then the incentive would be to grant loans that perform (and do not default) in order to maximize fees.
Attracting New Business Based on Investor Confidence
For lending platforms, there is less of a challenge bringing in new borrower business as long as the loan rates are competitively priced or priced below those of traditional banks or if the platform is targeting borrowers that are less of a priority for banks. However, the challenge in bringing in new investors for consumer loans seems to be the greater challenge. Reasons for the reduction in investor confidence of platforms could include one or more of the following: Other asset returns have increased relative to those earned by investing in loans on the
A greater percentage of FinTech actual loan defaults compared to expected, which could
lead to a loss of confidence in the risk analysis and loan granting processes.
The inability of investors to withdraw their investments early (even though there is no
guarantee that it will be allowed).
The platform is subject to legal action for the improper use of data or for the use of
improper marketing techniques. .Any event that causes a severe disruption to the platforms activities.

Low Barriers to Entry
Due to lack of regulation of the FinTech industry in many jurisdictions, the online nature of the services, and the common data sources used, there have been many new entrants into the industry. That reduces the opportunities for any individual platform to be profitable.
Additionally, there is always the threat that well-established banks could compete aggressively in the industry by establishing their own platforms. Banks would likely have access to more sophisticated resources pertaining to credit analysis and loan pricing.
Platform Profitability Risk
Many large platforms have incurred consistent losses each year, which calls into question whether they can continue to originate new loans into the future. Two arguments to support the continued existence include: (1) the FinTech industry is still in its early stages and requires further expansion to achieve the necessary economies of scale to become profitable, and (2) there is a specific objective to grow and avoid using profits in the short- term.
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However, if losses continue and concerns about maintaining enough investors persist, platforms may have to alter their operations by originating and funding their own loans, providing loan guarantees, or using leverage, for example. In such cases, the platforms may become inherently more risky and must become more skilled in capital and risk management matters in order to survive in the long-term.
G r o w t h o f Fin Te c h C r e d it