How to Detect Money Laundering in Online Lending

On May 13, 2015, the US House Committee on Small Business held the first hearing on marketplace lending. It paved the way for a slew of regulations, which would come to greatly impact how online lenders must verify customer identities – specifically in the context of reducing money laundering.

Let’s examine what AML means for online lenders and how they can deploy better systems to improve compliance.

Why Is Money Laundering a Problem for Online Lenders?

Whether you are a bank, credit union, P2P lending platform or BNPL company, you must comply with AML regulations. Anytime someone borrows money through your service, no matter how small the sum is, you incur the risk of being repaid with dirty money.

In essence, this would mean your platform is exploited by money mules to “place” criminal funds, with placement being the first of three stages of money laundering.

But regardless of the technique employed by criminals or their accomplices, the fact is that this puts your lending company at risk: 

• You may be investigated by regulatory bodies.
• You may have to pay hefty AML fines.
• Your reputation may suffer in the eyes of customers, stakeholders and even employees.

To make matters worse, AML investigations can have a crippling effect on your operations, requiring tremendous manual effort from multiple departments.

How Do You Detect Money Laundering in Online Lending?

Detecting potential money laundering – and ensuring AML compliance – requires a combination of identity verification and AML transaction monitoring. You should also run the appropriate checks for PEPs, sanctions lists, watchlists, and so on.

Going beyond this, when it comes to flagging suspicious behavior, here is what you need to consider:

• Irregular user profiles: Hopefully, you should have a good idea of what a good borrower looks like – and that goes beyond passing your credit check. This should help you identify suspicious people who rely on made-up data (synthetic IDs) or even unusual device configurations.
  
• Suspicious loan requests: The amount to be borrowed must be consistent with the reason for a loan request.
  
• Unusual repayment activity: When it comes to suspicious behavior, you should also keep track of things like overpayments, regular payments in small sums, and payments above a certain threshold.

All of the above should be consistently monitored and logged, especially if you ever find yourself in a position to have to write suspicious activity reports (SARs) for your auditors.

Top 3 Custom Rules for AML in Online Lending

Let’s now look at some concrete examples of how you could monitor risky users and boost compliance using the SEON platform.

#1: Borrower’s IP Points to a Sanctioned Country

A great advantage of lending money online is that you can operate in any market worldwide. In theory, at least. The reality is that certain locations are blacklisted for AML reasons.

This is why it’s crucial to ensure your customers aren’t based in certain countries – which you can’t always trust them to reveal willingly. Sometimes, their IP address will betray their true location.

With SEON, bocking (or reviewing) these customers’ logins is as simple as setting a rule that looks at IP addresses. This is all checked with our powerful IP lookup module.

Below, you can see that our system added 10 points to the fraud score – but more importantly, it sent the customer’s application for manual review. You can then decide manually if this is indeed a risky signup or login for your AML compliance.

#2: Customer Suddenly Deposits More Than Usual

Transaction monitoring is a key part of the AML process, and it does include checking repayments. This is why we’re setting up a rule designed to look at suspicious increases in repayment amounts. With SEON, this is done thanks to velocity checks, which look at specific user actions over a set timeframe.

Note that higher repayments aren’t unusual (or unwelcome) by lenders.

It’s simply a matter of ensuring everything appears legal, which is why this rule only increases our risk score by an extra 20 points. You can, of course, set the points and threshold yourself for automatic decline based on your own risk appetite.

#3: Repayment Is Above the AML Threshold

The transaction in the screenshot above is a customer repayment. You can see that it’s set to be automatically reviewed. Why? Because it is above $3,000 – which, in the US, makes it liable to be logged for AML compliance as part of a suspicious activity report.

Below is the rule in action, combined with other rules which can help with your customer profiling. Note that checking domain information or name and email name similarities is more common during onboarding than at a repayment stage, but it’s worth taking advantage of all the possibilities that the SEON platform offers to verify customers’ intentions and score how risky your users are.

How SEON Helps Online Lending with AML

SEON offers a combination of customer intelligence, AML and anti-fraud tools, all designed to let you know who you’re dealing with, what they are doing in your platform, and whether you should be worried about compliance risk.

Here are some of the features you can leverage with SEON to help your online lending company’s AML meet regulatory requirements:

• Perform AML list checks: Screen your borrowers to check if their names appear on AML lists (PEP, sanctions, watchlists, etc.).
  
• Monitor transactions in real-time: Identify suspicious repayments that deviate from the norm and those above the AML thresholds in your jurisdiction. 
  
• Block suspicious users at signup: Instantly flag users whose online activity raises suspicions, such as an absence of a social or digital footprint and strange device configurations that imply spoofing and manipulation.

All of the above and more are available via powerful modular APIs, so you can control risk however you see fit, whether you’re dealing with fraud prevention, alternative credit scoring or AML regulations.

Related Articles

• Guide to Loan Fraud: Business Risks for Lenders
• AML Verification: How to Not Get Fined