LenddoEFL is one of the finalists of Lendit Fintech 2019’s Excellence in Financial Inclusion award.
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Blog | Winning Agent Incentivization Strategies
By Brett Elliot, Director of Product, LenddoEFL
An agent helps a microentrepreneur apply for a loan in India
Lenders often use loan officers, promoters or other types of agents to sell loans, financing, and credit cards. This can be an effective way to acquire new customers but there is a big risk in this approach. Agent incentivisation plans will make or break your bottom line. Good incentive plans lead to large, healthy portfolios, but bad ones lead to fraud, default and chargebacks. We’ve worked with lenders all around the globe and have seen both good and bad strategies. Here are some of the best and worst.
Worst agent incentivisation strategies
Paying per applicant
When your goal is to get new customers, a very simple approach is to pay agents for each customer that applies. Obviously, the more people into the top of the funnel, the more customers you will get right? Not exactly. Agents will optimize for their commissions. First they will go after good borrowers, then they will go after anyone. And some of them will realize they can make up fake applicants so they don’t have to go after anyone at all. If you simply try to optimize the number of applications, you will get a lot of applications, few disbursements, and even fewer good customers. This may seem obvious but we know of a large commercial bank in South America that did this and they had a huge problem finding good borrowers.
Paying per approval
Since paying per applicant does not work, the next logical step is to pay per approval. This way your agents can still acquire as many people as possible but you keep the portfolio healthy with controls in your approval process. This is the most common approach to incentivizing agents but it does create a perverse incentive for the agent to game the system in order to get more approvals. For example, one lender in Mexico was offering credit cards to college students. At some point the agents discovered that the only criteria for approval was that the applicant be under 26 years old. This caused a huge spike in fraud as the agents signed up anyone they could find that met the age criteria. If you are going to pay per approval, the controls you set must be strong and difficult to game. See below for recommendations about how using technology can help.
Paying a commission higher than the loan interest
A subtle but often overlooked problem with any incentive plan is paying an agent a higher commission than the loan interest costs. We know of a digital bank in South America that does this and they are experiencing a type of perpetual fraud. In this scheme, the agent fills out an application using a stolen identity. Then he takes out another loan using a different stolen identity and pays back the first one with it. He repeats this process over and over, collecting his commission. This is only possible if the commission is higher than the interest rate so keep that in mind when making your commission schedule.
Good agent incentivisation strategies
Paying a percentage of the money collected
One approach that aligns the agents and lenders is to pay the agent a percentage of the money collected. This incentivizes the agent to sign credit worthy new customers but also to follow up with the customer and make sure they make their payments. We sometimes call this the “MFI model” because of how effective some MFIs have become by using it.
Withholding payment for a certain period of time
Paying a percentage of the money collected doesn’t work for credit cards, but a strategy that does is to withhold payment for a certain amount of time. For example, one financial institution we know of pays the agent a little when the credit card is activated and then the remainder in 4 months so long as the card is not in arrears.
Recommendations
Combine several incentives
There is no one size fits all for any company and often combining different incentives works really well. The companies with the healthiest portfolios that we have seen do this. For example, a large MFI in Mexico pays their agents a very low fixed salary, plus a percentage of the amount disbursed and a percentage of the money collected.
Use technology to strengthen the controls
If you are certain that your controls and risk models are effective at weeding out the bad borrowers, then paying per approval is a reasonable approach. But how certain are you especially if your customers do not have much credit history? Technology can definitely help in this area. Using highly predictive alternative credit scores is a great start. Be sure to ask the vendor what controls are in place to detect agent fraud since they will try to game the system. At LenddoEFL, our scores are backed by our Score Confidence system which looks out for suspicious loan officer behavior and coaching as well as other signs of fraud.
Final thoughts
The end goal is to reward agents for portfolio growth and portfolio quality. Lenders that limit the number of new clients added per month, maintain a solid portfolio of recurring clients, and combine different incentives for compensating agents, seem to have the largest and healthiest portfolios.
Yahoo Japan | Can Japanese banks use big data with "AI loan"? (日本の銀行は「AI融資」でビッグデータを活用できるか)
Attempts to calculate the creditworthiness of individuals by AI (artificial intelligence) and to finance using it are expanding. This is called "AI score lending".
The meaning of AI doing loan screening, which is one of the most important tasks of banks, is quite large.
However, the question is whether Japanese financial institutions can handle big data. If it can not do it, it will repeat the failure of the past score lending.
Singapore's Lenddo is a service in emerging countries such as India, Vietnam, Indonesia, which have never had a history of credit.
Read full article
Medici | What Happens at the Convergence of Machine Intelligence and Online Lending
Credit scoring and approval rates changed substantially with the arrival of alternative lenders, mainly due to the adoption of new practices in collecting and analyzing potential borrower data. Alternative data has played its role in expanding horizons for financial institutions and for creating an opportunity to enter the financial sector fir technology startups and data-rich international companies.
While social media, for example, as a source of data for creditworthiness assessment is still at a nascent stage, certain startups are already claiming to have incorporated information from social networks into their frameworks. In the quest to reinvent the way to assess consumer-related risk (as well as extend credit to unscored and questionable), startups were found more imaginative than traditional institutions.
Alternative data requires alternative approach to data analytics, which wide adoption of machine learning and artificial intelligence brought.
QQ | Paolo Montessori, LenddoEFL COO, featured in an interview with Tsinghua Financial Review
Markets and Fintech | El Big Data en la evaluaćión del riesgo de crédit
LenddoEFL, fundado por varios profesionales de perfil tecnológico en 2011, nacía con la misión de mejorar el acceso bancario a la emergente clase media de los países en vías de desarrollo. Con este objetivo en mente se acercó a las principales entidades financieras de Estados Unidos con la idea de estudiar los datos que éstas tenían sobre su población objetivo y poder elaborar un algoritmo de credit scoring alternativo. Tras la negativa de los bancos decidió emprender el viaje en solitario.
Siete años después, Lenddo parece haber dado con algo parecido a la receta de la tarta de frutas perfecta. Analizando multitud de variables, desde el comportamiento en redes sociales, hábitos de comercio electrónico o la velocidad a la hora de rellenar los formularios de solicitud afirma reducir la mora en un 12%, aumentando las aprobaciones en un 15% y ser capaz de realizar una evaluación en menos de tres minutos. Read full article.
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SoFi is preparing to launch in Sydney, its first market outside of the US, and earlier this year the country's first loans and deposits marketplace, Lodex, formed a partnership with Singaporean start-up Lenddo to bring its social scoring technology to the country...