Risk & Fraud Rules
Risk Rules Engine
Fast growing financial technology companies use LogicLoop to monitor abusive behavior on their platforms. LogicLoop empowers fraud & risk managers to quickly set up rules on top of company data to raise alerts for their team to review. Without LogicLoop, analysts are often stuck waiting for engineers to implement alerts directly in the codebase, inhibiting their ability to iterate quickly and experiment.
To get started with monitoring bad actors on your platform on LogicLoop, you'll first want to connect your company's data sources. This could be production databases like Postgres or MySQL, warehouses like Snowflake, Redshift or Big Query, or APIs like Socure or Sentilink. Once connected, you can use sample industry templates below as guidance to bootstrap your program. The following templates have been simplified and specific details have been omitted due to the sensitive nature of the content. Contact us at [email protected] to access our fully detailed suite of fraud & risk monitoring formulas.
Query your data to select transactions with large dollar amounts.
For each transaction flagged, create a ticket for your analysts to review. You can automatically generate a ticket in LogicLoop's Case Management System.
Query your database to flag users with excessive failed external fund transfers.
count(*) AS num_failed_transfers
JOIN merchant ON merchant.id = merchant_id
created_on > current_date - interval '30 days'
AND transfers.status = 'FAILED'
count(*) >= 3
For each user flagged, generate a Slack alert for your team to review.
Query your data to flag users who signed up recently with high total transaction volumes.
For each user flagged, trigger a webhook to create a ticket in your own internal system or another ticket management system like Salesforce, Zendesk, JIRA, or Asana for an analyst to review.
Select users who signed up recently without address information
Then, send each user an email reminding them to fill out their address.
Flag users who failed identity verification checks. You can pull in information from third party APIs using our API (JSON) data source and write a rule on top of Query Results to join data from multiple sources.
For each user flagged, create a ticket for an analyst to review and follow up.
Alert on transactions conducted from high-risk geographies. If you have a pre-defined list of countries that are deemed high-risk, you can pull in this list of values by parameterizing it and populating it with a list derived from another query.
Alert if a user's outstanding balance exceeds the funds that are available in their account.
Send an account manager a Slack notification to reach out to the user to fund their account.
Flag users whose IP correspond with users who have already been identified as fraudulent.
Alert on payments that are more than 30 days past due.
Flag users who have been late on over 10 loan payments by more than 30 days.
Alert if a transaction amount is close to a regulatory threshold.
Flag if a user has withdrawn funds to over 3 different bank accounts.
Flag if a user has made peer to peer transfers to over 10 different accounts in a month.
Alert if a user has greater than 5 transactions in the past month.
Flag users who've deposited more than $20,000 in their account in the past month.
Flag users who've withdrawn more than $20,000 from their account in the past month.
Flag users who've bought or sold orders in excess of $20,000 in the past month
Last modified 3mo ago