Credit and lending are ancient financial products. It is believed that agricultural credit was used in Sumer as far back as 3500 B.C. Subsequently, most civilizations of the world have used credit in one form or another going back thousands of years. The nature of credit has evolved based on the need of society and the technology and financial systems available to service those needs. Today, you can go online and fill a short loan request form and you will be shown many customized loan offers within seconds. Behind the scenes, a whole plethora of technology and financial players are making this possible in real-time.
Online Lending Ecosystem
In the online lending business, fundamentally, a loan seeker goes online and fills a form that is received by a lender. The lender then makes an offer of a loan, which may even be disbursed fully automatically in some cases. In reality, there are a number of other players involved in the process. The ecosystem of online lending includes consumer websites and apps, lead brokers, technology providers, banks, credit unions, and other lenders.
In a typical scenario, the user may go to a popular personal finance website and click on a link taking him to a loan website. The website will ask the user to provide basic information such as name, address, income, loan purpose, loan amount sought, etc. These websites typically would hand over the lead to an online lead broker or clearing-house intermediary. The lead broker has relationships with many lenders directly and, through other intermediaries, indirectly. It is the job of the lead broker to generate good offers through its partners. The user is shown one or more loan offers if available. The user may then click or tap on an offer and will be taken to the lending partner for further processing and completion of the loan. All these steps happen in real-time in a few hundred milliseconds.
Role of lead brokers/ Clearing-houses
In this article, we are focussing on the business of the lead brokers intermediaries which act as a marketplace or clearing-house for loan leads. These intermediaries are part of the infrastructure layer of the online lending ecosystem.
These marketplaces themselves neither generate the leads nor underwrite the loans. However, they work with a variety of partners on both sides who specialize in their own important roles of online loan demand generation and financial loan underwriting. A typical lead broker might process millions of leads a month from hundreds of websites and apps and may funnel these leads to dozens of lenders on the other side. A lead broker creates value by efficiently matching aggregated demand with the aggregated supply of credit.
How lead brokers can grow
Since lead brokers are clearing houses, they can grow through following levers:
- Get more loan leads through better partnerships with lead sources
- Get more loan offer choices by partnering with lenders
- Manage a balance between the set of partners on the two sides dynamically and continuously
- Perform better matchmaking by showing appropriate offers to loan seekers, so that they actually get funded
Among these levers, the first three are about having access to more loan leads and loan offers, but in a balanced way. The objective is to provide high-quality liquidity. It doesn’t help to grow either side of the marketplace alone without a proper balance, and can even hurt the business. If you onboard a new lead source for home improvement loans, it is important that you have access to enough relevant home improvement loan offers on the other side. Otherwise, the lead source partner will quickly get frustrated by low conversion rates and low earnings. So, a fine micro-balance in each niche of your leads traffic is critical.
Finally, the fourth lever for growth is about creating more value from whatever leads and offers the marketplace has access to. This is the task of better matchmaking between loan seekers and loan offer providers, so that loans are ultimately approved and both sides gain. The difference between excellent matchmaking and an average performance could be as high as 30%-60% in revenue and 30-150% in net revenue for the marketplace.
AI for matchmaking between loan seekers and offers
Traditionally, lead brokers have approached matchmaking through manual rules driven by business insights over time. Typically, lead management software allows filters that can be manually configured and periodically tweaked. A lead broker might manually set up hundred of filters. In turn, each filter may have dozens of sub-settings, such as state, income, loan amount sought, income, loan type, etc. If a lead matches a filter, then the corresponding loan offers can be shown to the loan seeker. Further processing is required because more loan offers will typically qualify than can practically be offered to the loan seeker without confusing him.
This manual approach may work well for low volume and simple marketplaces. But as volume and complexity grow, it quickly becomes impractical to keep creating new rules that work well with each other. Moreover, the external credit environment is always dynamic. It is hard to detect these trends quickly and also to respond quickly enough by manually tweaking hundreds of existing rules and filters. Over time, the overall matchmaking effectiveness increasingly lags behind the underlying potential of the marketplace. This leaves money on the table for all three entities – the loan seeker, the lender, and the lead broker. It is only when a loan actually gets funded that all three entities make money.
In contrast with manual rules, AI and machine learning-based matchmaking is particularly effective in this complex, data-rich environment. In fact, AI thrives on complexity and abundance of data. While AI and ML have become hyped buzzwords and are mirages in many domains, these technologies do provide real value for lead optimization businesses. However, AI is a not magical pixie dust. Building appropriate AI solutions is a difficult and iterative task. But if done well, an AI-driven matchmaking technology provides the following benefits:
- Higher conversion and funding rates
- Automatic detection and adjustments to changes in the external credit environment
- No need of manual creation or tweaking of brittle rules
- Backed by actual performance data rather than narratives alone
However, there are many challenges in using AI-based solutions for matchmaking. AI technology is hard to build. It requires specialized experts and a long iterative process to build a system that works well in practice. It is also essential to have good quality historical performance data. Also, this approach requires a careful rollout plan that balances speed and sustainability of benefits.
Overall, the financial benefits of deploying AI for loan lead brokers are significant and a thoughtful approach can deliver those benefits. One approach that is now available to lead brokers is to partner with AI specialists that focus on this vertical and have a track record of delivering these benefits.
