DATA IS GROWING 163,000,000,000,000,000,000,000 163 zettabytes will be created by 2025 1

DATA IS DISCONNECTED

Because banks today are product-centric, rather than customer-centric, they make decisions in silos. As customers cross multiple channels and payment types, their transactional data ends up in databases that don’t talk to each other.

Meanwhile, valuable external enrichment data goes unused, because many organizations lack the infrastructure to integrate internal and external data sources. For these organizations, big data is not asset. It’s an exposure.

FRAUDSTERS HIDE IN ALL THIS DATA, BUT THEY LEAVE SUBTLE PATTERNS IN THEIR WAKE

This is a sample of our findings based on 2017 data

Trend 1:

FRAUDSTERS LIKE NEW ACCOUNTS

Most of fraud occurs within the first 100 hours of account creation.

Trend 2:

FRAUDSTERS LIKE NEWLY BOOTED DEVICES

Trends show that less time elapsed between device boot time and when the transaction occurred is correlated with more fraud.

Time Elapsed in Days

Number of Consecutive Digits

Trend 3:

FRAUDSTERS LIKE CONSECUTIVE DIGITS

Email addresses of 2-4 consecutive digits have more fraud.

Trend 4:

FRAUDSTERS DON'T LIKE TO NAME THEIR DEVICES

High rate of fraud when the device name is unknown or null in a mobile transaction.

Trend 5:

FRAUDSTERS LIKE KEYS THAT ARE CLOSE TOGETHER

Fraud is correlated with fake email patterns. One such pattern is when the letters in an email address on an average are the same or 1 key apart.

Percentage of Battery Left

Trend 6:

FRAUDSTERS CHARGE THEIR BATTERIES

79% of internet users download their bank’s mobile apps, which introduces mobile as a channel for fraud.2

Research shows that fraud rates are higher when there’s more battery left in a mobile device.

Trend 7:

FRAUDSTERS LIKE TO USE OBSCURE EMAIL ADDRESSES

Fraudsters prefer certain email domains over others.

Email Domain Name

HOW WILL YOU CONNECT THE DOTS?

Identifying fraud signals is hard. Connecting these insights to make a decision is even harder. No one signal is enough to say “fraud” or “not fraud.” This page shows a handful of signals, but a normal transaction profile can have thousands.

Humans alone cannot connect these signals together into decisions. That’s the power of machine learning: combining thousands of data points into recommendations for action.

Learn how machine learning can stop fraud

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Emerging channels and payment types have created a new ecosystem of risk, and legacy systems cannot keep up. Read this report and get actionable insights to help you create a strategy for taking charge of the AI disruption that’s well underway.

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Sources

1 Forbes, “What Will We Do When The World’s Data Hits 163 Zettabytes In 2025?,” Apr. 2017.

2 Aite, “Fraud is Now a Competitive Issue” Oct. 2017.

3 eMarketer, “Most People Have a Mobile Banking App, but Do They Use It?” Oct. 2017.