How machines learn about spam
If you’ve noticed that the amount of spam in your email has decreased over the last decade or so, it’s likely you can thank robots for bringing more organization into your life. As tech companies work to fine tune their filters even further, artificial intelligence (AI) is poised to play an even bigger role.
How spam filters work
The most basic spam filters are “rules based.” That means if an email fits a certain criteria, or rule, such as having “prize money” in the subject line, it is automatically marked as spam and filtered from your inbox. Many financial words can actually trigger these filters since, not surprisingly, a large volume of spam content involves financial schemes or scams.
Filters don’t just review the subject lines; they tend to apply to content within the email. More advanced filters can also block specific senders that aren’t generally in your contacts, too. Often, you can create your own rules to apply to spam if you notice a certain trend in your messages.
It’s that last principle that companies like Google found particularly helpful when enhancing their spam filters. Gmail, which is the most actively used email server in the United States, uses Google’s AI program TensorFlow to help with emails. TensorFlow’s focus is machine learning — developers can tell it how to interpret different actions taken by users, and from this observation, it can better predict what a user will view as spam.
Human + machine
Machines using AI can often spot spam more quickly and accurately than humans. But machines by definition can’t do their jobs without humans — they rely on human behavior patterns to make their spam predictions.
Machines, for instance, can’t apply nuance to emails. They can’t apply “Jim doesn’t usually speak that way” as a filter to emails from a particular friend the way a human can, for instance.
Have you ever wondered why some emails from a publication end up in spam while others end up in your inbox without issue? It’s possible that a large number of respondents flagged that specific communication as spam, so your spam filter “learned” that it was most likely spam and reacted accordingly.
What this means for your clients’ money
Spotting patterns has applications far beyond your inbox. This same thinking can apply to finance, as well. Some firms use machine learning to spot patterns that could indicate economic growth or signal that a particular company is set to outperform. Every time a human analyst notices something new, the machine can apply that knowledge at scale.
These forward-thinking applications are one of the reasons Athene has taken an interest in AI and the way it’s interacting in our daily lives. We’ve looked for meaningful and effective ways to securely use this type of technology to enhance our product solutions. For instance, an index used in our fixed indexed annuities uses AI to identify stocks with the greatest growth potential
Two things you can use today:
- Ensure your emails aren't triggering your clients' spam filters by using careful phrasing around terms like "beneficiary."
- Share with your clients how AI is already a part of their lives and how an index used in Athene's fixed indexed annuities uses AI.
This information is brought to you by Athene — where innovative annuity solutions are powered by unconventional thinking.