WHAT THE BANKS CAN LEARN FROM ONLINE FOR DATING

The banking or finance sector could learn a trick or two from the online dating industry is laughable. After all, while the former is heavily regulated, deeply complex and integral to our economy; the latter is frivolous by comparison.

Dating, as is often said, is a numbers game! And organisations such as Match.com, eHarmony and Zoosk rely on very sophisticated technology as they sift through vast customer bases to create the most compatible couples. Specially, they rely on data to build the most nuanced portraits of their members that they can, so they can find the best matches. This is a business-critical activity for dating sites – the more successful the matching, the better revenues will be.

One of the ways they do this is through graph databases. These differ from relational 


databases – as conventional business databases are called – as they specialize in identifying the relationships between multiple data points. This means they can query and display connections between people, preferences and interests very quickly.

Applying Dating Insights to the Financial Sector 

What The Banks Can Learn From Online Dating

So where do financial institutions come in? Dating sites have put graph databases to such effective use because they are very good at modelling social relationships, and it turns out that understanding people’s relationships is a far better indicator of a match than a purely statistical analysis of their tastes and interests. The same is also true of financial fraud.

The finance and banking sector lose billions of pounds each year as a result of fraud. While security measures such as the Address Verification Service and online tools such as Verified by Visa do help prevent some losses, fraudsters are becoming increasingly sophisticated in their approach. Over the last few years ‘First-Party’ fraud has become a serious threat to banking – and it is very difficult to detect using standard methods. The fraudsters behave very similarly to legitimate customers, right up until the moment they clear their accounts and disappear.

One of the features of first-party fraud is the exponential relationship between the number of individuals involved and the overall currency value being stolen. For example, ten fraudsters can create 100 false identities sharing ten elements between them (name, date of birth, phone number, address etc.). It is easy for a small group of fraudsters to use these elements to invent identities which to banks look utterly genuine. If each fraudster took out three accounts, credit cards or loans, worth £5,000 each, the potential loss is £1.5 million.