Hi everyone interested in digital lending solutions for challenger banking.
It’s Dmitriy – CEO at TIMVERO.
A series of articles under the name “How can challengers start lending?” I wrote on the base of my practical banking experience, a lot of market research, and maybe some common sense.
In the future, our blog will be also full of highly insightful posts based on data and trends – so add us to your favorites and subscribe not to miss the updates.
So, imagine you’re a C-suite executive in a digital bank, receiving generous funding from investors in recent years, and experiencing steady growth in your customer base and brand. Quite unexpectedly you may find yourself facing the question of when you plan to turn all that into profit. To accelerate the path to profitability, you consider two primary options: introducing subscription-based premium services and/or lending money.
Upon examining your options, you may realize that building new features for premium services appears to be costly and time-consuming. Hence, you decide to move into the lending space, assuming that lending money for a fee is a highly predictable process, which is easy to calculate.
However, very soon you notice numerous issues that you need to resolve.
You come up with the following questions (applicable to both consumer and SME lenders):
In part 1 of this article, I will address the first 2 questions. The answers to
In this blog, I will address questions 1 and 2. If you find this insightful, I will continue covering the rest of the questions above.
There are always a few options, like:
The main secret here is: don’t strive to choose one option forever. Start with a less risky lending model and move up to a more advanced model. Even in the riskiest scenarios, several techniques can be used to reduce risk, such as collateralizing loans and selling Collateralized Debt Obligations (CDO) to investors.
Any scenario includes building and owning a dev team or choosing a vendor. The latter option is most likely to be cost-effective, since building all the infrastructure from scratch is expensive.
Like in any business, there is a risk-reward trade-off: the less risky the model, the less lucrative it is.
Let’s assume you are strategy-oriented and start with option 1 (originating loans on someone’s behalf). In that case, top-notch fintech software powered up with data analytics tools can help you gather data about your customers and the outcome of their loans, which certainly makes a difference in business.
New lenders lack information: they hardly know which products their customers need or on which terms to lend. This blank spot needs to be filled with conversion rates data and product usage patterns to get hints about appropriate products for your customer base.
If you are starting your lending operations by holding loans on your balance sheet, you might have a highly experienced team, a license, and a robust balance sheet coupled with an essential compliance team. If this is the case, this article is not for you, but you may still share it with peers.
If you lack any of these components (powerful analytics tools or a highly experienced team of engineers and ML engineers), we recommend considering either loan origination on someone’s behalf, or loan origination with a proportional backup. I know a great US-based lending company that got bankrupt as a result of regulatory issues – even though the loan origination aspect was really strong.
Below we will summarize the business models, products, underwriting strategies, and success metrics for those business models, as well as how to create a roadmap for future developments and how to ensure a strong set of controls for your business.
As for more sophisticated loan products, such as commercial lending types, factoring, or trade financing – I’m planning to cover them in future blog posts. While we’re just launching a lending business, it’d be more vital to focus on simple products, such as:
For a shortcut, let’s assume we’re talking about unsecured loan products only. I want to explore the potential decision-making paths without breaking down each product in detail.
The choice of a lending form and a loan product depends on the target customer base and on the loan management team’s expertise. It all derives from customers’ needs: why they require funding, is that for large purchases or for everyday items?
if you go for commercial lending: do SMEs need financing to cover short-term balance sheet holes or they are looking for opportunities to invest it into the business expansion?
Here are a few ways to gain customer insights:
As you have more data at hand, your market behavior turns from reactive to proactive. You can offer various loan products based on real customer needs, not on what you suppose they need. For example, by having access to SME’s accounting data, you can monitor potential cashflow gaps and ramp them up. More on data analytics will come in the following blog posts here.
Another important thing is to check twice if your policies support responsible lending and boost economic growth, not downturn. For customers in debt, you may want to offer additional financial advice or money-saving products instead of even more loans. It’s a win-win: if you want your customers to pay back, help them use the capital to improve their life quality and move their business to the next level.
It depends on the source of repayment. Cash flow analytics is good for recurring loan products, such as overdrafts or credit lines. In other cases, like loans or installments, you may need a lot more data.
Loans and installments are usually more difficult to repay. Credit lines and overdrafts are usually tied to customer cashflows and have fewer limits. In case you lack customer insights, it would be less risky to start lending using a faster revolving schedule.
Well, if you have a consumer with a 12-month loan. You will get reliable customer data in 12 months only. For Machine Learning analytics, you require a large number of such customers, so it may take years to implement a smart prediction model. And in case of a default loan, you are at high risk.
However, if you provide overdrafts or credit lines, you can easier track the details of customer repayment — on a monthly or bi-weekly basis. It allows you to collect data faster, get more opportunities to launch AI/ML-based analytics, and approach loans or installments with a lot of experience and customer insights at hand, with better prediction models and a better idea of the customer as well.
That was a brief outline of the first two questions that may puzzle challengers on their way to lending. Later I’m going to write articles on how to improve risk and governance, how to launch brand-new loan new products, how to use data to draw business advantages and make well-informed decisions, and a lot more.
Thanks! I’ll be back soon with two more parts of this article.