Since one of the primary uses of time series data is forecasting, it's wise that we learn about some of its fundamental properties. To understand what the industry means by forecasting and the steps involved, let's visit a common misconception about the financial industry: only lending activities require forecasting. We need forecasting in order to grant personal loans, mortgages, overdrafts, or simply assess someone's eligibility for a credit card, as the industry uses forecasting to assess a borrower's affordability and their willingness to repay the debt. Even deposit products such as savings accounts, fixed-term savings, and bonds are priced based on some forecasts. How we forecast and the rationale for that methodology is different in borrowing or lending cases, however. All of these areas are related to time series, as we inevitably end up using time series data as part of the overall analysis that drives financial decisions. Let's understand the forecasts involved here a bit better. When we are assessing an individual's lending needs and limits, we are forecasting for a single person yet comparing the individual to a pool of good and bad customers who have been offered similar products. We are also assessing the individual's financial circumstances and behavior through industry-available scoring models or by assessing their past behavior, with the financial provider assessing the lending criteria.
In the case of deposit products, as long as the customer is eligible to transact (can open an account and has passed know your customer (KYC), anti-money laundering (AML), and other checks), financial institutions don't perform forecasting at an individual level. However, the behavior of a particular customer is primarily driven by the interest rate offered by the financial institution. The interest rate, in turn, is driven by the forecasts the financial institution has done to assess its overall treasury position. The treasury is the department that manages the central bank's money and has the responsibility of ensuring that all departments are funded, which is generated through lending and attracting deposits at a lower rate than a bank lends. The treasury forecasts its requirements for lending and deposits, while various teams within the treasury adhere to those limits. Therefore, a pricing manager for a deposit product will price the product in such a way that the product will attract enough deposits to meet the forecasted targets shared by the treasury; the pricing manager also has to ensure that those targets aren't overshot by a significant margin, as the treasury only expects to manage a forecasted target.
In both lending and deposit decisions, financial institutions do tend to use forecasting. A lot of these forecasts are interlinked, as we saw in the example of the treasury's expectations and the subsequent pricing decision for a deposit product. To decide on its future lending and borrowing positions, the treasury must have used time series data to determine what the potential business appetite for lending and borrowing in the market is, and would have assessed that with the current cash flow situation within the relevant teams and institutions.