bank signs

Why Customers Must Be More Than Numbers

I read with some amazement a story in the London Daily Telegraph this week about a customer of NatWest Bank who sent £11,200 last month via online banking to an unknown company instead of his wife. Although Paul Sampson had correctly entered his wife’s name, sort code and account number when he first made an online payment to her HSBC account, he wasn’t aware that she had subsequently closed the account.

Mr Sampson thought he was transferring £11,200 to his wife: he clicked Margaret’s name among a list of payees saved in his NatWest banking profile and confirmed the transaction, but the payment went to a business in Leeds. Mr Sampson believes that HSBC had reissued his wife’s old account number to someone else, a company whose name they refused to tell him. NatWest told Mr Sampson it was powerless to claw the money back.

HSBC said it had contacted its customer, but it had no obligation regarding the money. HSBC insisted that the account number in question was not “recycled”, saying Mr Sampson must have made a typing error when he first saved the details, which he disputes. Although the money was in fact returned after the newspaper contacted HSBC, a very large issue has not been resolved.

Although news to most of us, it is apparently a common practice among banks in the UK to recycle account numbers, presumably because banking systems are so entrenched around 8 or 9 digit account numbers that they are concerned about running out of numbers. Apparently a recent code of practice suggests that banks should warn the customer making the payment if they haven’t sent money to this payee for 13 months, but according to the Daily Telegraph “No major high street bank could confirm that it followed this part of the code”.

The Daily Telegraph goes on to state that the recipients of electronic payments are identified by account numbers only. The names are not checked in the process, so even if they do not match, the transaction can proceed. “This is now a major issue when you can use something as basic as a mobile phone number to transfer money,” said Mike Pemberton, of solicitors Stephensons. “If you get one digit wrong there’s no other backup check, like a person’s name – once it’s gone it’s gone.” If you misdirect an online payment, your bank should contact the other bank within two working days of your having informed them of the error, but they have no legal obligation to help.

Mr Sampson obviously expected that the bank’s software would check that the account number belonged to the account name he had stored in his online payee list, but apparently UK banking software doesn’t do this. Why on earth not? Surely it’s not unreasonable for banks with all the money they spend on computer systems to perform this safety check? It’s not good enough to point to the problems that can arise when a name is entered in different ways such as Sheila Jones, Mrs S Jones, Sheila M Jones, SM Jones, Mrs S M Jones, Mrs Sheila Mary Jones etc.

These are all elementary examples for intelligent name matching software.  More challenging are typos, nicknames and other inconsistencies such as those caused by poor handwriting, which would all occur regularly should banks check the name belonging to the account number. But software such as matchIT Hub is easily available to cope with these challenges too, as well as the even more challenging job of matching joint names and business names.

There are also issues in the USA with banking software matching names – I remember when I first wanted to transfer money from my Chase account to my Citibank account, I could only do so if the two accounts had exactly the same name – these were joint accounts and the names had to match exactly letter for letter, so I had to either change the name on one of the accounts or open a new one! Having been an enthusiastic user of the system in the USA for sending money to someone electronically using just their email address, I’m now starting to worry about the wisdom of this…

We banking customers should perhaps question our banks more closely about the checks that they employ when we make online payments!

Golden Records Need Golden Data: 7 Questions to Ask

If you’ve found yourself reading this blog then you’re no doubt already aware of the importance of maintaining data quality through processes such as data verification, suppression screening, and duplicate detection. In this post I’d like to look a bit closer at how you draw value from, and make the best use of, the results of the hard work you invest into tracking down duplicates within your data.

The great thing about fuzzy matching is that it enables us to identify groups of two or more records that pertain to the same entity but that don’t necessarily contain exactly the same information. Records in a group of fuzzy matches will normally contain similar information with slight variations from one record to the next. For example, one record may contain a full forename whilst another contains just an abbreviated version or even none at all. You will also frequently encounter fuzzy matches where incorrectly spelt or poorly input data is matched against its accurate counterpart.

Once you’ve identified these groups of fuzzy matches, what do you do with them? Ultimately you want to end up with only unique records within your data, but there are a couple of ways that you can go about reaching that goal. One approach is to try and determine the best record in a group of matches and discard all of the records that matched against it. Other times, you may find that you are able to draw more value from your data by taking the most accurate, complete, and relevant information from a group of matched records and merging it together so that you’re left with a single hybrid record containing a superior set of data than was available in any of the individual records from which it was created.

Regardless of the approach you take, you’ll need to establish some rules to use when determining the best record or best pieces of information from multiple records. Removing the wrong record or information could actually end up making your data worse so this decision warrants a bit of thought. The criteria you use for this purpose will vary from one job to the next, but the following is a list of 7 questions that target the desirable attributes you’ll want to consider when deciding what data should be retained:

  1. How current is the data?
    You’ll most likely want to keep data that was most recently acquired.
  2. How complete is the data?
    How many fields are populated, and how well are those fields populated?
  3. Is the data valid?
    Have dates been entered in the required format? Does an email address contain an at sign?
  4. Is the data accurate?
    Has it been verified (e.g. address verified against PAF)?
  5. How reliable is the data?
    Has it come from a trusted source?
  6. Is the data relevant?
    Is the data appropriate for its intended use (e.g. keep female contacts over male if compiling a list of recipients for a woman’s clothing catalogue)?
  7. Is there a predetermined hierarchy?
    Do you have a business rule in place that requires one set of data is always used over another?

When you have such a large range of competing criteria to consider, how do you apply all of these rules simultaneously? The approach we at helpIT use in our software is to allow the user to weight each item or collection of data, so they can choose what aspects are the most important in their business context. This isn’t necessarily whether an item is present or not, or how long it is, but could be whether it was an input value or derived from supplied information, or whether it has been verified by reference to an external dataset such as a Postal Address File. Once the master record has been selected, the user may also want to transfer data from records being deleted to the master record e.g. to copy a job title from a duplicate to a master record which contains fuller/better name and address information, but no job title. By creating a composite record, you ensure that no data is lost.

Hopefully this post will have given you something to think about when deciding how to deal with the duplicates you’ve identified in your data. I’d welcome any comments or questions.