How consumer facing businesses can manage their lending risk during and post-Covid-19
The world is on lockdown across major countries in the world, many consumers have taken a knock and businesses offering credit to consumers will need to rethink their risk models in order to protect themselves. There will be new pressures of risk on lending which businesses need to consider carefully before going into uncharted territory. The drive to get new business and keep existing clients must be managed and businesses must think about two key areas, Client on-boarding and account management of existing clients.
Rethink client on-boarding
As we travel further into the unknown, businesses need to rethink risk for on-boarding new consumers. The following factors are key and will play a major role in adequately managing risk:
Data
Consumer data has never been more important, as we move into uncertain times. The more data you have on a new potential client, the better you will be positioned to offer the right amount of credit to that consumer. This is incredibly important as you look to protect your business and your new clients, so that they do not over extend their credit and find it difficult to dig their way out. What kind of data can assist you in determining the credit limit of a new potential customer? This will be determined on the amount requested. For instance, you don’t need to vet a consumers assets if they are using credit to buy a t-shirt. But you may want to adjust that stance when a consumer is buying a higher value item such as a car.
Low credit amount
When we look at consumers who are looking at a low amount of credit for small ticket items, the checks are far less strenuous as mentioned before. This can include a credit check to ensure that a client has no previous defaulted payments or judgments at a registered Credit Bureau. This will show if a consumer is under debt review and what the chance is of them fulfilling their payment obligation. Along with the usual credit check, lenders may want to add an employment and salary check into their vetting process, as many job statuses and salary adjustments may have changed due to COVID-19.
High credit amount
When a consumer applies for a car or a home loan, this is a big commitment with years of repayment that await. Businesses must ensure that the proper checks against the proper consumer data is done in order to ensure that the consumer has sufficient liquidity to pay back the requested loan. The data that will give you a good insight into this, is data pertaining to the consumer credit. If a consumer has defaulted on small ticket items, they are a high risk of defaulting on bigger ticket items. If they pass this portion, we can start to look at the employment status of a consumer, how many bonds the consumer currently has, how many properties the consumer owns and what the consumer’s monthly expenses are against the consumers potential adjusted income? If the consumer does not own any property, what was their previous rental amounts and what kind of track record do they have with paying that rental amount? This is especially important in the case of consumers looking to make the next step from renting to buying a property. Another important aspect to take into consideration is the cash flow that can be placed for a deposit. These kind of checks are important in covering a business from a potential defaulting consumer. This however feels like a massive binder of data to sift through. So how do we make this job easier going forward to streamline these consumer requests in order to action good consumers and fully vet bad paying consumers?
Scaling and adjustable credit risk assessment models
It is important for clients to build a solution that works for their business and not a model that works for their industry. Working with models that are adaptable will assist you in adjusting your risk per consumer as the economy moves. The data that you can feed into these models will assist you in referencing consumer risk quickly and passing off referrals for further investigation on risky consumers.
These models must be scalable. What do I mean by scalable? Well, to use an analogy, the economy pre-covid was a prime and clean-ish action figure. But now, post-covid, It looks more like a badly put together putty man, this shape is sure to change as businesses innovate or close through these trying times. Your model must be adaptable as information becomes less or more important as restrictions on countries lockdown lessen or increase, so you can chop and change the model to cater for this without developing a whole new model which could take weeks in a development lifecycle. We have to be agile minded for this reason and build a model that is easily updateable for referencing different data sources.
Targeted pre-approvals/qualifications
A pre-approval can happen in two ways. The first being when a consumer goes onto your businesses site and puts in information and a check is done to preliminary see if that consumer qualifies for your product. This kind of check is subject to a more detailed screening of the consumer which results in a decision.
The second way is for your business to use the data available to look for potential customers and categorize them for cold calling. So how would we go about categorizing potential new customers? Well that is fully dependent on your product. But, let’s explore this at a high level using the assumption that you have access to a vendor with this type of information.
We would need to look at customers in your businesses area. Once we have identified customers that work or live in that area, we can look at the consumers in good standing with their current accounts and target these customers based on their income band. This kind of data can be further coupled with individuals that have previously had interest in that brand. Example: John pays his accounts, John earns X amount a month, stays in your businesses area and has previously owned 4 BMW cars over the last 12 years. His latest BMW is almost fully paid up and we can assume that John will be looking to purchase a new BMW based on John’s previous consumer behaviour. On the other hand we have Sally, Sally has never owned a BMW, but Sally has recently come into a new salary bracket. If we can couple this data with information from social media sites (IE: Sally likes the BMW page on Facebook) there is a possibility that Sally is now in the position to purchase a new BMW. This kind of targeted marketing couples various data sources in order to give businesses a targeted list of individuals to focus on, rather than cold call everyone on a database. This allows a better conversion rate of consumers that have a lower risk of defaulting on payments. This coupled with the relevant on-boarding checks will target the right consumers and ensure the consumer has the ability to pay for the item you are selling.
Keep existing clients by monitoring account changes
Consumers that were in good standing pre-Covid may be a higher risk now. How can we re-evaluate our existing consumer base?
Account management and data monitoring
There’s a notion that once a business on-board a new consumer, that consumer is a number and that number is only relooked when the consumer does not pay. This can hurt your business in uncertain times and increase your defaulting consumers massively. It is important to re-vet consumers in the same manner that you would when on-boarding in order to monitor positive or negative consumer credit status changes from origination pre-Covid to the consumer credit status post-Covid. Painting a true picture of your consumers financial stability will allow you to upsell to good standing consumers and understand the reasoning behind bad paying consumers as this may be due to the Covid crisis. These consumers are important to your future brand loyalty and will assist in forging a long relationship going forward.
Client rewards with the intention of collecting consumer buying behaviour and habits.
Points systems have been around for years, offering clients incentive on every purchase makes a customer loyal and keeps them using your product. A loyal customer is a customer for life. Implementing rewards for loyalty will ensure you keep your customer spending with your business and help your customers save in the long run.
This data comes with consumer spending habits on a monthly basis and coupled correctly, can give you a targeted list of low risk consumers that you can extend further credit to, keeping the customers happy and loyal to your business and help build a consumer profile for each customer.
Conclusion
The financial stability of a consumer may have changed as job losses and salary cuts effect millions across the world due to Covid-19. As a lender, it is important to protect yourself and ensure compliance against reckless lending by putting in the relevant checks in when on-boarding new consumers, as well as re-evaluating existing consumers on your books to manage payments going forward. This will give your business a good view of risk and allow you to offer consumers who were previously good paying customers an opportunity to restructure the payment according to circumstance and bring about a feeling of understanding between your business and your client. We need to ensure that we evaluate and understand the risk associated on a case by case basis using the data sources we have available and screen the relevant documents to prevent defaults on payment. More stringent vetting upfront will cut down the number of defaulting consumers and allow your business to focus on selling instead of collections. We are in unprecedented times, it is important to understand your customer and empathize correctly with a structured process using data sources to get closer to your customer and their needs to ensure loyalty going forward into the future.