This week a student in the UK found that, for companies dealing with millions of customers, it’s not always easy to be confident of your single version of the truth when it comes to customer information management.
I remember when I was applying for my student loan at University. Back then it was a paper form with a couple of pages that I had to send with all the details one would expect for a loan application: my name, date of birth, address etc. It wasn’t too laborious and I had the loan arranged pretty easily.
The issue reported has shown this process to be far more disruptive for the student in question. Emily Hughes application was refused as a duplicate, because of another application by her namesake, who happened to be born in a similar location on the same day as her.
In 2015/2016 The Student Loan Company processed 1.24 million student loan enquiries [source] using paper-based forms. Whilst this process has historically worked, times are changing and with digital transformation this process can be more effective using digital means. If this particular loan application had been captured online the chance for error would have been lessened.
This automated approach would also provide the opportunity to enable the applicant to self-select address details, which could be validated against fresh reference data.
From the BBC article the Student Loans Company has advised that once Emily’s passport has arrived they will then be able to continue her loan application, in time for the start of term in September. But making sure the loan does get approved and arranged is still a manual process and they still need to confirm her identity.
This is an example of an organisation experiencing difficulties in deciphering between two identical names, and highlights the need for them to understand when Emily Hughes is not in-fact, Emily Hughes. Similar challenges are faced by banks and other financial services organisations when it comes to compliance around anti-money laundering regulation. Many are turning to Entity Resolution, such as that offered by Spectrum from Pitney Bowes, to understand the context around the customer such as location and relationships.
With modern Entity Resolution tools the Student Loans Company would be able to quickly and easily identify the correct Emily Hughes, saving costs, avoiding errors and ultimately keeping their customers happier.
Making sure Emily really is Emily is just as important as knowing that Emily is also not Emily.