Social engineering scams are hard for banks to prevent because there's often no compromised account and no obviously fraudulent transaction to detect. Fraudsters manipulate legitimate customers into sharing information, granting access, or authorizing payments themselves.
By the time a suspicious transaction reaches the bank, nothing about the account looks unusual.
To reduce risk, banks need to understand account patterns and the context surrounding account activity.
Read on to learn how to avoid social engineering scams so you can prevent losses for you and your customers.
Unlike unauthorized fraud, where the customer never approves the payment, social engineering scams involve an authorized transaction the customer initiates.
A customer may log in using their usual credentials, pass authentication checks, and then authorize a payment because they believe they're following genuine instructions.
This creates three main challenges for banks:
Below are the key methods banks can implement to prevent social engineering scams:
|
Method |
How it helps |
|
Monitor customer activity across their journey |
Tracks changes in customer actions across devices, logins, beneficiaries, and payments so banks can detect deviations from usual customer actions |
|
Continuously evaluate account risk |
Tracks changes in account risk as new activity and transactional signals appear to help banks identify when an account starts acting out of the ordinary |
|
Use account intelligence to understand the context |
Brings account signals and relationships into one view, so analysts can see why risk is rising |
|
Apply a targeted approach to high-risk payments |
Applies additional step-up controls based on risk to help banks intervene when fraud indicators are present without disrupting legitimate payments |
|
Connect signals across channels and accounts |
Connects signals across channels, accounts, and institutions so banks can see broader fraud patterns and spot risks beyond their systems |
|
Use AI models |
Analyzes account usage and patterns to help banks detect anomalies, assess risk, identify potential scam and mule activity, and prioritize investigations |
One of the most effective ways to detect potential social engineering scams is to look at what happens before the payment, instead of evaluating the transaction in isolation.
Banks can monitor changes in customer activity across the digital banking journey, including:
These signals may not indicate fraud on their own. However, viewed as a whole, they can reveal a deviation from a customer's normal actions.
For example, a fraudster may access a victim's account and review transaction history or other account details to gather information and make a later scam call more credible. The fraudster's goal isn’t to steal money directly from the account, but to use the sensitive information they’ve obtained to convince the victim more easily that they are a bank official.
Detecting fraud at the point of payment is too late. Banks need to see risk building during logins, device changes, and other unusual actions that precede the payment, while there's still time to act.
Instead of relying only on point-in-time scoring of individual transactions or sessions, financial institutions can implement continuous account monitoring to track how an account's risk profile changes over time.
For example, a previously dormant account is reactivated after several months of inactivity and starts showing a series of small changes that gradually increase its risk score, such as more frequent logins, new account use, and several small transfers that are unusual for the account.
When the account's risk increases, the bank can respond accordingly, such as sending a warning, delaying a payment, or investigating further.
Worth knowing:
Acoru continuously assigns dynamic risk scores and classifications to accounts, updating them as new signals appear.
The platform uses pre-fraud signal intelligence that can spot traces left by scammers during the preparation phase, evaluating risk before a transaction is initiated and giving banks an opportunity to act before funds are transferred.
Continuous account monitoring tells banks when an account's risk and classification are changing, and tracks its history to show why.
Account intelligence can incorporate information such as:
Analyzing these signals together gives institutions a better picture than evaluating individual events.
For example, adding a new beneficiary may not seem suspicious on its own. But if the beneficiary is connected to other accounts associated with fraud, or the payment follows a series of unusual account changes, these combined signals may indicate a higher risk of social engineering scams or mule activity.
Banks need a risk-based approach to find a balance between stringent security that adds delays and minimal controls that leave customers exposed to threats.
When several indicators suggest that a payment may be the result of social engineering scams, the bank can introduce additional controls, such as:
The controls themselves are standard. What determines whether they help is how accurately the bank can recognize a fraud pattern from multiple signals and distinguish genuine risk from false alerts.
Social engineering scams rarely happen in a single interaction. A fraudster might contact their victim by phone, follow up by text to build trust and keep them engaged, and then direct them to a mobile banking app to make a payment.
For this reason, banks need to connect signals across channels and stages of the customer journey. Combining information from digital sessions, payments, beneficiary changes, and account relationships can reveal patterns that would otherwise remain hidden.
If several apparently unrelated accounts are connected to the same beneficiaries or suspicious accounts, financial institutions may reveal a broader fraud network.
In addition, a customer may hold accounts with multiple banks, while fraudsters can move funds between institutions so that no single bank sees the full trail. Collaboration can give banks visibility into signals that would otherwise sit outside their systems.
Worth knowing:
Acoru’s Consortium Manager enables banks to share account risk intelligence across a privacy-preserving network, where each participant can contribute to and benefit from collective intelligence. Banks can access live account scores and spot risks outside their customer base, while keeping sensitive data within their infrastructure.
AI can strengthen social engineering prevention by helping banks identify complex account patterns across large volumes of data.
Instead of relying only on predefined rules, AI models can analyze how customers and accounts normally act and identify meaningful deviations. Banks can use these capabilities to:
Using AI doesn’t mean replacing human decision-making. Explainability, observability, and human oversight still guide fraud prevention decisions.
Worth knowing:
Acoru was built around AI, with AI-native capabilities that support fraud teams across detection, investigation, and risk mitigation. The platform’s AI Assistant can analyze account and multichannel patterns to detect fraud-preparation indicators, while helping analysts:
Meanwhile, AI Workforce runs multi-step investigation and risk-mitigation workflows in the background, applying measures like cooling-off periods or temporary limits, within policy boundaries the bank defines. Human sign-off is still required for high-impact actions, so fraud teams retain final control.
Acoru is an AI-native fraud prevention platform that analyzes activity, transaction, and relationship data over time to provide banks with greater visibility into account risk.
The platform combines account-based risk assessment, pre-fraud signal intelligence, scam prediction, account classification, and cross-institution intelligence to help banks detect and prevent authorized and unauthorized fraud.
With Acoru, banks can:
Request a demo today to see how you can detect and prevent potential scams efficiently.
Banks can combine transactional, authentication, device, account signals, and other potential channel and data sources to spot activity that differs from the account's established pattern. Continuously analyzing these signals enables intervention before a suspicious payment is completed.
Continuous account monitoring analyzes an account's activity and risk over time instead of evaluating individual transactions in isolation. This way, banks can spot changes in usage, connections, and patterns that may indicate fraud before a suspicious transaction takes place.
Fraudsters can move funds between different financial institutions, so one bank may not see what another bank has already labeled as suspicious. Securely sharing relevant fraud intelligence can help institutions recognize connected accounts, beneficiaries, and scam patterns that would be difficult to detect using their own data alone.
Banks can use risk-based controls that introduce additional verification or intervention only when multiple signals indicate higher risk. This helps protect customers while allowing regular sessions to proceed with fewer interruptions.