The Lending Triangle
In this post, I take a detour from payments and focus on another area in fintech — lending. This is a business that is complex but can be quite profitable if done right.
Loans vary widely on parameters such as loan amount, interest rate (APR), loan duration, fees charged (late fees, returned payment fees or origination fees), presence of collateral etc. The specifics of a loan depend on the needs of the borrower and the lender’s ability to service that need profitably.
Lending Economics
A lender typically earns revenue through two sources —
- Interest Income: Interest earned from average A/R (Accounts Receivable), i.e., the average outstanding balance on the loan
- Fee Revenue: Fees such as annual fees or fees penalizing late or pre- payments
For a lender to be profitable, this revenue needs to be greater than costs —
- Operating costs: These include operating costs such as costs to acquire new customers and service existing customers
- Financing costs: Cost of capital raised to make loans
- Credit, fraud and other operating losses: These are losses arising from borrower defaults, fraud and other operational risk events such as process breakdowns or regulatory actions
Exhibit #1: Simple profitable Lender P&L
While higher loan amounts lead to higher revenues, they are also associated with higher losses in a credit default or fraud event. Therefore, the lender needs to calculate an optimal profit-maximizing loan amount. As the risk of the applicant increases, this optimal loan amount decreases.
Exhibit #2: Loan terms v/s lender assessment of applicant risk
The Loan Application Process
A typical loan application journey is designed to capture the applicant’s need (desired loan amount, term etc) and information/documentation for the lender to assess the risk of meeting that need profitably. The main category of risks the lender evaluates are —
- Credit risk: Does the applicant have the capacity, character, collateral or capital to pay back the loan?
- Compliance risk: Does the applicant have documentation to prove his/her identity (KYC)? Is the lender permitted to engage in financial transactions with this applicant (Sanctions screening)?
- Fraud risk: Is the applicant really who he/she claims to be? Is the information provided by the applicant true?
Application information is routinely supplemented with data from third party data sources such as credit bureaus. Also, the lender could request additional information or documentation from the applicant. At the end of the underwriting process, the lender makes an approve / decline decision and if the applicant is approved, makes a decision on the loan amount, duration and interest rate that can be supported.
The Lending Triangle
In this underwriting process, the information used (amount & quality) and the time spent in due diligence influences the lender’s risk assessment of the applicant and also determines the lender’s confidence level in this assessment. For a lender with good underwriting processes, this risk assessment approximates the true risk of the applicant with some allowance for error. Higher the lender’s confidence in the estimate, lower this error.
All things held constant, a lower confidence always leads to a higher overall lender risk assessment and consequently worse terms for the applicant.
Exhibit #3: Factors influencing lender risk assessment
This suggests that quality information and more time spent in lender due-diligence could result in a better decision and potentially better loan terms for the applicant. However, to provide all requested information, an applicant could go through multiple iterations with the lender and then wait a few weeks for due diligence to be complete. At the end of this intense process, there is a good likelihood (~75%¹) that the applicant is still declined. This is terrible customer experience.
Exhibit #4: Lending Triangle
Big Banks tend to be more thorough and deliberate with their risk processes. They ask for tons of documents and information but eventually provide very attractive loan terms (if approved!). While the end outcome could be great if approved, the experience applying for the loan is terrible for all.
Fintechs sensed an opportunity here, especially for applicants that were getting declined by the Big Banks. They started with a very easy application process that captures information digitally and provides a decision in minutes. However, this great experience comes at the expense of loan terms — lower loan amounts and relatively higher interest rates.
Exhibit #5: Big Banks and Fintechs on the Lending Triangle
Innovation Areas
With the entry of Fintechs into lending, Big Banks have been pushed to make investments to improve their processes. This has resulted in a “shrinking” of the lending triangle.
Exhibit #6: Innovation Strategies “shrinking” the Lending Triangle
- On the “information availability” front, programmatic access to data through companies like Plaid have made it easy to access quality applicant permission-ed data. Lenders are also connecting to commerce platforms such as Amazon, Shopify and Square to access up-to-date revenue information while also providing a good user experience. Additionally, when it comes to KYC and fraud, lenders have invested in easy-to-use identity solutions that employ image processing and liveness detection and use a photo ID and selfie clicked by the applicant to assess risk.
- On the “time-to-decision” front, lenders have invested in automated decision making. In situations where an automated decision is not possible and human intervention is necessary (higher loan amounts, contradictory information from different sources etc), lenders have invested in tools that improve underwriter productivity and cut decision times.
- Finally when it comes to making better decisions, lenders now use advanced machine learning and other data science techniques to more accurately predict risk. These techniques coupled with innovation in information availability has improved lender confidence in risk assessments.
At the end of the day, the Lending Triangle is a handy framework to visualize trade-offs in product decisions that impact customer experience and risk. I also use it as an easy-way to frame where different lenders operate (as in Exhibit #5 above).
References
