Why, you can save through all of them, of course!
That was a key part of the intuition that gave rise to the three savings types outlined in BFA’s InFocus Note #3: Combining demand and supply side insights to build a better proposition for banks and clients. This post walks through a some of the highlights of this Note.
The Need for A New Savings Nomenclature
But, you may ask, why on earth do we need to come up with new types? Well, mostly because we didn’t find anything out there that did justice to the nuances in savings behavior we were seeing, and because we had tons and tons of data and so could segment at the granularity that client-based surveys could not accommodate. Systematic classification of savings types is sparse, and frankly, my favorite is still the oldie-but-goldie from Stuart Rutherford’s The Poor and Their Money. There is “saving up,” “saving down,” and “saving through.” You can read about this here, here and here, but basically the first is classic savings, the second is classic credit, and the third is a mixture of the two (like health insurance). Turns out voluntary savings accounts can display behavior that cannot be satisfactorily classified into one of these three.
We were also looking for pattern based matches solely based on account and balance information from the MIS, without any clue as to why savers were doing what they were doing. (We went on to combine this with client surveys afterwards, but that’s another story.) The patterns had to be sensible and discernible from each other, but they also had to be very precise to match the precision of the data we had on our hands. And on a personal level, it just fun to be able to craft software bots that crawl through the 0’s and 1’s to provide the kind of insights we gained!
X101: The A, B and C of Savings
Anyway, so coming back to our mattress->cow story… One can save a small amount, or a larger one. One can save it for a short period of time, or longer. And, one can save it in a form that allows ready access to cash, or in one that takes a bit of effort to liquidate. Generally speaking, one tends to store smaller amounts of money for a shorter period of time in a more liquid form at one end of the spectrum, and larger amounts of money for longer periods of time in rather illiquid forms.
Combining this intuition with our mattress-savings club-cow triptych gives us:
As self-explanatory as this graph is to you and me, it means absolute jack to Python, our programming language of choice. We needed a way to translate what you are seeing above to numerically defined filters that classified accounts based on one of more indicators.
We settled on the following rules for our pet algorithms through a process that relied largely on descriptive analytics of the underlying dataset and Daryl Collins‘ extensive experience with the financial lives of the poor – a process that was really part science, and part art.
Note that while clients may display all three types of behaviors, not all are welcome by banks. Type A are particularly expensive to maintain, since they not maintain adequate balances for the bank to book sufficient income on the float of that balance.
Not all accounts would fall into one of the three types. The two below captured the leftovers with some level of activity. Those which showed no activity are simply marked dormant.
The “Active but nor Savings” bucket contains accounts that display “dump and pull” behavior, where individuals use the account as a temporary repository between when cash inflows and outflows, and is typical of salary deposits or social grants.
We call this entire nomenclature “X101”. The genesis of this name involves thinking of this exercise as an X-ray that provides a basic-level dissection of savings accounts.
The X101 Wagon Wheel
Once we apply this nomenclature to the underlying savings accounts, we get breakdown that are specific for each of the financial institutions we looked at. One example is given below; it’ll give us a sense of the kind of information we can get from something even this aggregated. (Source: InFocus Note #3, page 10)
- A full half of the accounts are dormant! (Yes, it’s amusing how the number is exactly 50%..) Uptake followed by non-usage is a nagging problem for many of these institutions.
- About half of the accounts that are not dormant display A-, B- or C-type behavior. Seems like only a quarter of the accounts this institution services are really saving.
- B-type saving is hard to do! Recall that this is the one analogous to the savings clubs, which requires considerable discipline. But voluntary savings accounts do not have discipline enforcement mechanisms by definition, and few have incentives either.
- The rest are about evenly split between the “dump and pull”-ers and the folks who can maintain some kind of balance some of the time, but not all the time.
Is this what you would have expected, based on what you know about savings accounts?
Looking through the X101 Lens
Now that we have this classification of the accounts, we can look at existing information through a new lens, so to speak. Two examples are given below.
The first involves asking how much it costs to support each of these types of accounts. Below are the net revenue numbers in USD for one of the banks:
So.. other than Type B, all other types are losing money for the savings division.. Not so good from a financial sustainability point of view, specially considering Type Bs typically make up a small sliver of total savers. (These figures include the amortized customer acquisition costs and monthly maintenance charges, by the way.) This sort of analysis is the beginning of the discussion surrounding the business case of savings accounts, and how things can be different.
The second involves this thing called “channel dominance” – a creation of the venerable David Porteous. Financial institutions offer their services through different channels, such as branches, ATMs, agents, mobile vans, mobile phones etc. We consider an account to be displaying a certain channel dominance if the number of transactions the client conducts using that channel exceed those conducted through any other channel by at least 50%.
For one of the banks, the breakdown of channel dominance by X101 types looked like so (“Other” implies that the account did not fall in any one of the dominance buckets):
So .. we see that:
- Type A savers love ATMs! Easiest to withdraw cash, maybe?
- Type B savers really love branches! Could going to branches be providing some of the discipline needed for this kind of saving?
- Type C savers don’t really have a particular preference between ATMs or branches, but they sure don’t like agents… Maybe access to agents makes it hard to maintain balances over a long period of time?
- Balance Managers look like Type C savers as far as the channel distribution is concerned.. Perhaps they just need a nudge or three to become Type Cs?
Yes, the purported causal chains I casually drop above are purely speculative. But this line of thinking gave food for some great discussions with the institution in question, who know their clients really, really well.
The Big Picture
I think the X101 nomenclature has the potentially to materially impact the conversation around low-income savers and their savings accounts. It’s a rather quantitative approach that focuses on the how, which when married with the qualitative why provides fascinating insights into savings-oriented financial inclusion. This is important because saving is often hard for the client to do, and appropriate savings products are often challenging for the banks to design. X101 can inform this discussion, and we’ve been having some fascinating discussions indeed.