Do You Know Me?: Recognizing Your Customers with Digital Identities
Today’s customers expect a lot out of their digital banking experiences. Gone are the days when customers felt reluctant to share personal information. Today they’re willingly trading personal information for better and faster service. An Accenture survey found that 78% were happy to share personal data with their bank, but that 66% demanded faster, easier services in return.
This brings to light how ubiquitous personal information has become. Purposeful personal identification is becoming frivolous when you consider all the sources where it’s openly being shared: social media sites with saved passwords, multiple online shopping carts with stored credit card information, and thousands of pictures on Instagram and Pinterest about interests, hobbies, and preferences.
All this raw digital data that customers leave behind provide an information gold mine for banks. Or is it a land mine? Big data leaves organizations exposed to fraudsters, and it’s certainly no Christmas when an organization is hit with a data breach.
There are 58 records lost or stolen every second, according to the Breach Level Index. Data from these breaches are mined, aggregated, refined, and sold to criminals that commit fraud. Last year alone, consumers lost $16 billion to fraud and identity theft in last year alone, and two out of every three firms had their share price negatively impacted.
Yet data breaches are only the beginning. Private data can be easily purchased on the dark web and used to compromise accounts resulting in account takeover, and feed all kinds of fraudulent payments and transactions. When these accounts are associated with things of significant value, like bank accounts and credit cards, consumers starts to wonder, “Who is watching my back?”
To remain competitive, players in the financial ecosystem face the challenge of managing fraud without adding friction, in some cases with limited information. Thankfully, advancements in technology and the promise of AI for payments means banks can go beyond recognizing the customer relying on just a username and password. Now banks can use the millions of data points about the ecosystem to create baselines and spot anomalies. They can crunch data points (lots of them), and they can do it much faster than before, enabling decisions and alerts in real time.
If Apple can use facial recognition to unlock phones, can banks use customer recognition to unlock payments?
Hypergranular profiles of customers, transactions, devices and other relevant fraud indicators built by machine learning models can be the key to unlocking superior customer experience while balancing fraud and risk.
With so many new channels and points of engagement, there are new points of compromise. Customers are engaging with organizations in new and different ways, and they’re crossing channels all the time. They’re opening checking accounts and then following up about a mortgage in person. They’re opening accounts in person and changing their addresses on their cell phones. The data points pour in, and the pursuit of real-time fraud detection begins.
Detecting fraud is about detecting abnormalities, and the old way to do this was to identify loose-fitting cohorts and ascribe normal behavior for them. Is this normal for a certain age group? Is this normal for a CEO?
Machine learning technology has enabled the creation of profiles in segments of one, rather than broad cohorts of many. The same technology constantly updates these granular profiles based on behavior from different timescales — the past three years, three months, three seconds – making detection real-time and at scale.
That’s a boon for all the fraud-fighters out there, because fraud doesn’t happen in batches. These hypergranular profiles power complete customer knowledge — and complete fraud knowledge — that can scale as organizations grow and keep up as payments get even faster.
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