Algorithmic trading has made the headlines, most often negatively. The underlying idea is that computers can make, on a broader scale, better decisions than a trader. On the other hand, criticism is rising to curb a practice that some consider contrary to
the benefits of the market.
But, on a different scale, algorithmic based decisions could be a major next step in banking and PFM. PFM is about helping bank customers better understand their finance and make the right decisions. Goal setting plays an important part
in this process, as it ties into financial life decisions and monthly budget. But the actual movements of money from for example checking account to savings could be intelligently made by a
predictive money management tool.
For most people, the key cash movements of their budget are fixed: income and rent/mortgage. On average monthly budget for food could be almost equivalent. With these components, as a first step, you could imagine
programmed decisions that would move extra cash the day before a salary should be deposited to a savings account, preventing the misuse of extra liquidity. On the other way around, in case of over spending, the program could decide to allocate
expenses to a credit card / credit line and automatically add that line of credit as a savings goal in the next month budget, making an
arbitrage between the cost of credit and savings rate. An alert based system would warn the customer of any change or even ask for approval if needed. Different
algorithm profiles could be defined depending the volatility of previous months budgets, taking stronger savings options for people with controlled budget and protecting more people with more volatile income and spendings. Personal accounts
could work more like bank or company themselves, which try to optimize their cash for maximized returns.
But automated decisions to prevent an account for running a debit balance are going against
a bank culture of generating revenues from penalty fees on their customers. This is challenging for banks, especially since running different banks algorithms against historic account data could help provide what-ifs scenarios to compare how
2 banks would fare. Algorithmic banking could generate a change in banks behavior and business models.