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Open market origination costs a king’s ransom! Confused?
Here’s what it means. Banks are constantly chasing good borrowers with perfect credit scores. How do they do that? They bid on ad space on search engines, social media networks, and various affiliation websites. The average marketing budget of a bank is $2.1 million, according to a study conducted by Financial Brand. And recently, Economic Times reported that there’s been a 20-30% jump in Customer Acquisition Cost (CAC) as too many platforms (banks, NBFCs, fintechs) are betting on similar ad keywords. This figure is only likely to increase with further saturation of digital channels.
Basically, banks pay through the nose to get a customer to click on the ad. What follows is a long onboarding funnel that incurs cost at every stage of decision-making. It looks something like this.
Costs don’t end with getting customers to the loan page; thereafter onboarding involves multiple steps and customers drop-off at every step, adding to the cost for lenders.
Let’s assume 100 customers clicked on the ad. Suppose 20 of them dropped off after Stage 1, 40 after Stage 2, 20 after Stage 3, and 10 after Stage 4, i.e 90 of the 100 dropped off at different stages of the digital lending funnel. Finally, loans are disbursed to only 10 customers — that's an approval rate of 10%.
For banks to break even, they will have to charge these 10 final borrowers an interest rate that covers acquisition costs of all 100 borrowers plus onboarding costs incurred at each stage of the funnel for the 90 borrowers that dropped off.
This total cost along with the profit margin the bank aims to achieve gets distributed among 10 final borrowers in the form of interest rates. To understand how interest rates can be assigned based on risk such that this sum is covered, read our piece on risk-based pricing here.
Essentially, lenders should be able to design a lending workflow that manages to weed out ineligible borrowers right at the start, reducing cost incurrence in subsequent stages of the lending funnel.
Otherwise, good borrowers with perfect credit scores end up bearing the brunt of inefficient lending pipelines.
Prequalification: The great weeding out strategy
The whole spray and pray approach is not the best digital lending strategy out there. In the earlier example, we assumed loans were given out to 10 out of 100 borrowers — that’s an approval rate of 10%. However, in the real world, an end-to-end digital lending funnel sees a conversion rate of 2%, a fifth of our supposition.
Hence, it’s extremely important for lenders to retain good borrowers and weed out bad ones early on in the funnel. And that's exactly what prequalification does.
How to prequalify borrowers?
Lenders can leverage various data sources to filter out ineligible borrowers, right at the beginning (see the figure below).
Such alternative data-based credit risk models serve as an effective sieve that help curb drop-offs at subsequent stages of the lending funnel. Alternative data includes high-quality data such as device data, platform data, and cash flow data. It helps paint a full picture of the borrowers and segments them into risk buckets.
For example, our risk intelligence product FinBox DeviceConnect evaluates borrowers by reaping users' device data after securing explicit consent. Data types ranging from basic details such as age, income, and location to financial information such as cash flows, spend patterns, and credit usage to behavioural variables such as risk tendencies, lifestyle, attitude, financial discipline, and decision-making patterns are assessed to segment the customers.
Insights generated from this data are fed into the business rules engine (BRE) to weed out ineligible borrowers right at the onset of the onboarding process — a fully straight-through process. In this case, the prequalification step is right at the top of the funnel (see the figure below).
This way, you can also cut out unnecessary steps from the customer journey, giving the best and the shortest experience to good borrowers. The beauty of it is that it takes the CX game a notch higher by helping craft seamless customer journeys.
There’s another way to prequalify borrowers — an even more targeted approach. In this case, prequalification precludes onboarding.
Basically, you show the loan offer only to eligible borrowers. This is very much doable, given the advent of super apps and platform banking. Platforms sit on a treasure trove of data and its in-device analytics engine can segment customers into risk buckets, and accordingly serve them the right offers and products. What’s more, borrowers are more likely to click and finish journeys when it's personalised to them.
The prequalification advantage
Improper targeting is a real concern. As indicated earlier, a real-world end-to-end digital lending funnel has a paltry approval rate of 2%. Adding a prequalification step is known to have boosted the approval rate by 3X. And that’s a huge cost saving, especially in a saturated market. In fact, it could be the ace in the hole for lenders striving to stay competitive during rate hike cycles.