Estimating Core Bank Deposits from Account Balance Histories
Summary
The document considers how to estimate a commercial bank’s core deposits from ten years of monthly account balances. Its starting proposal is to take each account’s minimum balance in each month and sum those minima across accounts. The response says this can be a reasonable proxy if core deposits mean funds that are not withdrawn, and suggests measuring each account over a rolling multi-month window as another formulation.
It also offers alternatives based on recent average balances and balance-change variability, or on medians and mean absolute deviations to limit outlier effects. A more predictive approach would train a model to estimate the minimum balance an account will maintain over a future horizon, using past balance behavior as features. These are candidate measures, not validated estimates: the document provides no empirical comparison, and the result depends on how core deposits are defined, the selected time window, and the account-level data patterns.
Key ideas
- Summing account-level monthly minimum balances is one possible proxy for deposits that remain available.
- A rolling minimum over a chosen window can represent a more persistent balance measure.
- Recent averages and robust dispersion measures offer alternative account-level estimates.
- A predictive model could target each account’s future minimum balance and use historical balance behavior as features.
- The document gives no validation results, so the definition and evaluation of core deposits remain essential.
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Full text
# Calculate core deposits in commercial bank # Calculate core deposits in commercial bank Given 10 years history of past balances of deposit accounts in a commercial bank, I need to calculate what part of those deposits were core, month by month. This is my thinking: for each account/month take the minimum balance of that month. Then add up all the minimum balances for the month, and that will be the bank core deposit amount. Does this approach have any problems? Is there a better way to calculate this? ## Answer by Julie Taylor (score 1, accepted) https://quant.stackexchange.com/a/69858 As with any model, you'd need to make some assumptions and see how factoring in these assumptions play out. You're trying to model the core deposits so it would make sense to consider these account by account since how each client uses their account will vary. If core means deposits that are not withdrawn then I don't see an issue with considering the minimum balance of that month. To formalize, for each account, you can have core_deposit = running_min(account_balance, 3 months) for example. Other things you can try are the average balance over the past X months +- rollin standard deviation of balance changes. I haven't seen how much the data varies but also try median balance +- mean absolute deviation to reduce the effect of outliers. Each of these methods will produce an answer, but what's important is how we evaluate that answer. Perhaps we can phrase this as an ML question. So you have your data set which is essentially a time series for each account. For each account, for each point in time, calculate the MINIMUM amount in the account over the next, say, 6 months, that quantity will be the target quantity you want to train your machine learning model to learn. If we are successful in building such a model, your model should spit out a quantity which the client should at least have in his account over the next 6 months. Next part, feature engineering, this is where you can be creative in developing features like calculate how often balance changes, magnitude of changes, average change if deposit/spend, etc.
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