Reverse Australia hits the 10,000 “Like” milestone!

Thanks everyone for supporting our service. If you think we can do something better, tell us!

We’re now up to 306 developer keys issued, 1,350 Android devices using our service, and we have 38,679 unique phone numbers in our caller complaint database!

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Telemarketing Fraud: How Reverse Lookup Services Protect You

In recent years, telemarketing fraud has become an increasing problem in Canada. According to the Royal Canadian Mounted Police, “criminals use the same techniques as legitimate companies, but hide behind the anonymity of the telephone and attempt to defraud thousands and thousands of Canadians each year.”

There are at least several hundred fraudulent telemarketing operations in North America that routinely seek to defraud consumers in Canada and the U.S. Let’s take a look at some additional facts.

Reverse Canada Infographic

Telemarketing Fraud Facts:

As a result of the telemarketing industry, there is an overall loss of $3,827,880, in addition to the average consumer loss at $1,174. According to Private Citizen, telemarketers place 148 million junk calls a day. Back in 2000, the Direct Marketing Association received a record of 3,789 telemarketing complaints in one 35-day period!

Telemarketing is a nearly $500 billion a year business, and investment scams alone amount to losses of $1 million an hour. Additionally, charities make more money from selling your name and number (aka. “sucker lists”) to other telemarketing companies than from the donations they collect from calling.

On average, only 24% of what consumers donate in response to a telemarketing sales call actually reach the intended charity.

There are a variety of payment methods that telemarketers use to profit from their scam(s). Bank debit accounts for 23% of these scams, in addition to: credit card: 21%; money order: 14%; wire: 9%; debit card: 5%; telephone bill: 2%; cashier’s check: 2%.

Initial contact is made across a multitude of communication platforms. Overall, phone contact was the highest form at 44%, along with: mail: 37%; print:15%; fax: 2%; in-person:1%; tv/radio: 1%.

20% of scam victims are 30 years old and younger. Additionally, 54% of victims were between the ages of 30–60, and 36% were 60 years and older.

**To avoid falling victim to these telemarketing scams, visit to use Reverse Canada;  the premier free reverse look-up service  for mobiles and landlines within Canada!**

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Australia Residential Database June 2013 Update

We are currently in the middle of a major residential database update for June 2013. As always, this also represents a significant update to existing numbers. Here is what the latest update brings:

  • 95,884 new phone numbers
  • 82,528 new mobile numbers
  • 2,467,760 total mobile numbers
  • 15,581,692 total phone numbers

Happy reverse searching everyone!

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Eset NOD 32 False Positive JS/Kryptik.ALB

Thanks to two concerned users (James and Dion), we were alerted to Eset NOD32 incorrectly reporting our site as having a trojan. The claimed virus is JS/Kryptik.ALB.

We’ve investigated the matter extensively and determined someone over at Eset isn’t doing their job properly. They flagging chunks of encoded javascript as being a virus.

If you are using NOD 32, we would advise you to change your virus scanner to a less ridiculous company. This one is clearly irresponsible in what they flag as a virus.

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Mate Spotter

Mate Spotter

Introducing Mate Spotter! We’ve created a website to make it easier to search multiple public sources for the information you need, providing you with one convenient search result, quickly, and easily.

Current sources we search include:

Find Law
Linked In
NSW Licensing
QLD Fair Trading
Ryerson Index
Tax/BAS Register
VIC Business Licensing

Give it a shot and tell us what you think of our new people search site!

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Australia Residential and Business January 2013 Update

We are currently in the middle of a major business and residential database update for January 2013, bringing our last update of the year. Here is what the latest update brings:

  • 154,421 new phone numbers
  • 48,955 new mobile numbers
  • 2,434,187 total mobile numbers
  • 15,485,808 total phone numbers

Happy reverse searching everyone!

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Australian Business Database December 2012 Update

We are currently in the middle of a major business database update for December 2012, bringing our last update of the year. Here is what the latest update brings:

  • 98,399 new phone numbers
  • 44,338 new mobile numbers
  • 2,385,232 total mobile numbers
  • 15,331,387 total phone numbers

Happy new year and happy reverse searching everyone!

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Name Normalization API

We’ve just released our brand spankin’ new Name Normalization API. This API provides direct access to our new name normalization algorithm. How’s that for making life easier 😉

The API is currently rate limited to 30 requests per minute. If you need more, feel free to shoot us an email. We’d love to hear what you’re using it for!

You can check it out here.

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Systems Upgrade and Unscheduled Downtime

As some of you may have noticed, unfortunately we had a spot of unscheduled downtime tonight (July 17th). As the downtime occurred towards the middle of the night (in the staffs timezone) it took longer than we’d like to resolve the issue. Though we’re not exactly what caused the issue, we suspect it was a service that has been running for quite a significant amount of time fine, but suddenly decided to go haywire. If we work out what caused the issue, we’ll be sure to let you know.

We decided to view this downtime as an opportunity, and used it to upgrade our systems to Fedora 17, along with an upgrade to PHP 5.4. The PHP upgrade has brought with it some small code changes that hopefully our devs were well prepared for. We’re excited to see what performance improvements we can get out of the latest software updates.

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New Name Normalization Algorithm

To attempt to clean results and merge multiple data sources together, we make use of a name normalization algorithm. This algorithm takes input such as “mr john smith jr” and turns it into “Smith, Mr John, Jr”. Other examples might be taking “Smith Al” and determining if it should be “Smith, Al” or “Smith, A L”. Name normalization also helps us determine that “Smith, A L” is the same as “Smith, AL”, so we don’t show multiple names on a record unnecessarily.

Today, we’ve released a significant improvement to this algorithm. With it, we’re hoping to see cleaner results for everyone.

We’re also considering releasing an API for this algorithm. If that sounds like a good idea to you, let us know!

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