Accelerating describes very interesting alternative data use cases, but I could not help to notice that it is focused on assessing prospects at the point of loan origination. It leaves out how to use alternative data to manage a financial inclusion loan portfolio after the client has received a first loan.
When we think about financial inclusion in a lending context, we naturally tend to think about how to approve prospects with little or no credit history. But when we step back a bit, we soon realize that origination is just a piece of the opportunity. We all know very well that collections and client retention are at the core of a sustainable financial inclusion business.
Financial institutions have used traditional data beyond origination for a long time. Most use bureau data, but many universal banks also use internal transactional data to profile customers. Large banks’ analytics departments and their consultants have become quite skillful at applying behavioral credit scoring and insights to price, cross selling, mitigating churn, and collecting.
Can MFIs use transaction data for credit scoring and insights as banks do?
Not so much. On one hand, most MFIs do not have transactional data beyond repayment. On the other hand, they are usually the only one reporting data to the credit bureau about most of their customers. So, credit scoring and insights based on traditional data bring limited benefits.
But MFIs can leverage alternative data to obtain similar benefits. I see two areas of opportunity: Cost effective collections and managing churn, the attrition of customers.
Leveraging alternative data for cost-effective collections
New-to-credit customers require more follow up than seasoned borrowers in order to secure payment. Many MFIs’ approach to deal with this challenge is to get loan officers involved in collections. Loan officers usually develop a good feeling for who in their portfolio needs to be reminded of an upcoming installment, and how to prioritize recovery efforts among customers in arrears. But it is an “art” that might be difficult to develop and expensive to operate.
We have worked with a client on ranking customers that are current on their payments by the probability of default in their next payment. We combine their repayment experience (traditional data) with their changes in mobile phone usage (alternative data). This ranking helps our client set differentiated preemptive collections strategies that allow them to focus their resources where they can extract the most value. Portfolio quality has improved while the investment in collections has stayed stable. I believe this will lead to higher approval rates in the future, as well as the ability to invest more in collections, fostering healthy portfolio growth.
We have also worked on segmenting customers in arrears by their probability of recovery. We combine traditional repayment data with alternative data collected at origination in order to separate customers in three categories: (1) those that will most likely get back on track with little or no intervention, (2) those that are likely to recover if loan officers intervene on time, and (3) those that will most likely not be recovered anyway. By focusing on the second group, loan officers improve their portfolio performance significantly.
Building insights to manage churn
In the financial inclusion business, small loan amounts and short terms of payment make it very difficult to recover the customer cost of acquisition on the first loan unless we charge prohibitive interest rates. Thus, we need to put special emphasis on extending the customer lifecycle in order to build a financially sustainable portfolio.
Many of my clients complain that once they go through the cost and pain of capturing a new-to-credit customer, they lose the client to another institution offering better terms, which they can do because the customer’s credit history has been enriched.
SMS and email data provide great insights into customers’ financial relationships and habits. These data may be used to both develop products that better fit specific segments of an institution’s client base, as well as to target clients with the right offerings better suited to their needs and preferences; and to do it at the right time to prevent this sort of churn.
For example, one may obtain an indication of income growth, increased business activities, or seasonal working capital requirements that may trigger a new credit offering. We may also be able to spot clients “shopping around” in order to target them with retention offerings.
In a prior post, we talked about the challenge of gathering large datasets in order to build origination models. The good news here is that a good way to accelerate the capture of value from alternative data is to look beyond origination into collections and client retention. Customer insights and collection scores can be developed quickly by combining new alternative data with traditional data that the institution already has.
In summary, the journey to harness alternative data value can be accelerated by focusing first on using it on the current client base in order to foster retention and improve portfolio performance, while at the same time gathering data on new clients to build origination models that allow us to approve more people while protecting performance.