Understanding Customer Behavior: The Key to Effective Fraud Prevention

Financial institutions lose billions of dollars to fraud every year. From identity theft to fake check scams, criminals are always looking for new ways to take advantage of banks and their customers. One of the best defenses against fraud is understanding normal customer behavior. When banks have a baseline for how their real customers typically act, they can more easily identify and stop suspicious activity.

What is “Normal” Customer Behavior?

Every customer is different, but overall patterns emerge. For instance, most customers:

  • Use their bank accounts at regular intervals, like making a certain number of purchases or ATM withdrawals per week.
  • Shop at the same merchants or geographic locations over and over.
  • Perform transactions during normal waking hours, not the middle of the night.
  • Have a typical purchase amount that aligns with their income level.

Significant deviations from a customer’s normal habits could indicate fraud, especially if combined with other red flags.

Using Analytics to Detect Anomalies

Advanced analytics tools can track each customer’s unique activity patterns. The software creates a customer profile and assigns a “normal behavior” score. Every transaction then gets its own score based on any variations.

For example, if a customer shops online every two weeks and suddenly makes five purchases in one day, all at new retailers, those transactions would score as “high risk” because they fall drastically outside of normal parameters. Any scores above a predefined threshold get flagged for further fraud investigation.

Analytics-based scoring models consistently outperform rules-based models in detecting fraud before sizable damages occur. False positives also decrease since anomaly detection minimizes reliance on broad assumptions of what constitutes suspicious behavior.

Incorporating External Data

Internal transaction data only reveals part of the story. The experts at Outseer tell us that by incorporating external data sources, banks can better recognize unusual account activity and make more informed scam detection decisions.

Relevant external data includes:

  • Location – Geo-tracking data from withdrawals/purchases helps identify transactions made far outside a customer’s ordinary operating area or daily commute. Location can show whether a criminal has stolen the customer’s identity.
  • Device data – The number of unique devices used to access an account can signal fraud. Criminals often try to gain account access from new IP addresses or device fingerprints.
  • Social media activity – Unusual changes in posting volume or content may reveal identity theft if an account has been compromised.
  • Credit reports – New loan inquiries, addresses, or employment history on a credit report can indicate wider identity theft.

Customer feedback data also provides valuable insight. Banks may send periodic surveys asking about recent transactions and if any were unauthorized. The responses help verify legitimate purchases from fraudulent ones. They also gauge whether customers spotted signs of account compromise that internal data models missed.

The more contextual data included, the easier it becomes separating legitimate actions from fraudulent incidents warranting further scam detection review.

Continuous Evaluation is Critical

Customers and criminals alike continuously shift their behaviors. Effective Outseer fraud prevention systems must adapt accordingly through continuous model evaluation and enhancement. Systems should be re-trained regularly as new data emerges.

Banks should also monitor model performance at the individual customer level, not just overall metrics. Detecting when an individual customer profile may need updating is critical for preventing false negatives. If a customer changes jobs or retires, for example, their expected transaction patterns likely shift as well.


The most robust fraud detection systems combine analytics, external data, and human intelligence. Advanced machine learning identifies potentially fraudulent activities, while human investigators incorporate their expertise on suspicious cases warranting further scam detection inquiries. Together, banks can create systems to stay ahead of even the savviest fraudsters.

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